Design and Development of a Contactless Planar Capacitive ... · Design and Development of a Contactless Planar Capacitive Sensor Thuvatahan Sivayogan ... who lives in the hearts

Design and Development of a Contactless Planar CapacitiveSensor

by

Thuvatahan Sivayogan

A thesis submitted in conformity with the requirementsfor the degree of Master of Health Science in Clinical Engineering

Institute of Biomaterials and Biomedical EngineeringUniversity of Toronto

c© Copyright 2013 by Thuvatahan Sivayogan

Abstract

Design and Development of a Contactless Planar Capacitive Sensor

Thuvatahan Sivayogan

Master of Health Science in Clinical Engineering

Institute of Biomaterials and Biomedical Engineering

University of Toronto

2013

The measurement of vital signs is a risk-free, inexpensive, and reproducible clinical practice

that enables identification of physiological deterioration of patients before an adverse event

occurs. However, studies show that manual clinical measurements of respiratory rate are

intermittent, biased, and inaccurate. Therefore, a contactless planar capacitive sensor was

developed and evaluated against a clinical reference method. Results show that the sensor

is accurate (i.e. strong agreement with an average ICC value of 0.99 and an average BSI

coefficient of 2.76 < 4 breaths/min clinical threshold) and unbiased (i.e. average mean

difference of -0.02 breaths/min). The sensor has promise for respiratory rate monitoring

of bedridden patients even during shallow breathing. Future work includes addressing

technology limitations, conducting a clinical pilot with a diverse patient population, and

exploring potential in sleep quality assessment.

ii

Dedication

I dedicate this work to Lord Krishna, the Supreme Person and the Supreme Absolute

Truth, who lives in the hearts of all living entities as their greatest friend, well-wisher,

and companion. I offer my respectful obeisances to Him: the personification of pure

consciousness, the reservoir of eternal truth and bliss, the controller of unlimited and

inconceivable potencies, and the origin of everything material and spiritual including the

living entities themselves. All works are to be performed in devotion for His pleasure.

Therefore, I humbly offer this work to Lord Krishna as an act of devotional service.

iii

Acknowledgements

I am grateful to my supervisor, Dr. Joseph Cafazzo, for his patience, support, and

guidance during the entire course of the project. A special thanks goes to Kevin Tallevi;

this project would not have been possible without his technical expertise. I especially

want to mention the following people: Adam Hart, Dr. Elaine Biddiss, Aarti Mathur,

Peter Picton, Jack Lam, Lily Alexander, and David Chartash. Finally, I am very grateful

to my parents for their encouragement and support.

iv

Contents

1 Introduction 1

1.1 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Literature Review 3

2.1 Clinical value of monitoring . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Prevalence and impact of inadequate monitoring . . . . . . . . . . . . . . 4

2.3 Clinical technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.4 Novel sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Computational Model 13

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4 Technology Redesign 21

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 Design choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.3 Final prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5 Safety Assessment 28

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2 Electromagnetic exposure safety . . . . . . . . . . . . . . . . . . . . . . . 28

5.3 Electrical safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.4 Medical device classification . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

v

6 Feasibility Study 31

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

7 Conclusion 42

7.1 Original contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7.2 Limitations of work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.3 Limitations of technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.4 Directions for future work . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Bibliography 46

vi

List of Tables

2.1 Summary of novel respiration sensors. . . . . . . . . . . . . . . . . . . . . 8

3.1 Material properties of human tissue. . . . . . . . . . . . . . . . . . . . . . 16

3.2 Verification of the computational model setup. . . . . . . . . . . . . . . . 16

5.1 Electromagnetic exposure limits. . . . . . . . . . . . . . . . . . . . . . . . 29

6.1 Results sliced by RR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6.2 Results sliced by TV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

vii

List of Figures

2.1 Bias in clinical measurement. . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Invasive respiratory monitors. . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Non-invasive respiratory monitors. . . . . . . . . . . . . . . . . . . . . . . 7

2.4 Original Respiration Pad. . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.5 Capacitive differential sensor. . . . . . . . . . . . . . . . . . . . . . . . . 10

2.6 Qualitative testing of Angelcare SensorPad. . . . . . . . . . . . . . . . . 12

3.1 Computational models for capacitive sensing. . . . . . . . . . . . . . . . . 14

3.2 Fringing electric field. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3 Simulated breathing waveforms. . . . . . . . . . . . . . . . . . . . . . . . 17

3.4 Electric field distributions for capacitive volume sensing. . . . . . . . . . 18

3.5 Electric field distributions for capacitive displacement sensing. . . . . . . 18

3.6 Electric field distributions for capacitive deformation sensing. . . . . . . . 19

4.1 Interdigital capacitance sensor. . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Comparison of sensor heads designed and constructed. . . . . . . . . . . 23

4.3 Sensor head. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.4 Sensor electronics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.5 Sensor electrical components. . . . . . . . . . . . . . . . . . . . . . . . . 26

4.6 Sensor software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6.1 Experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6.2 Boxplots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.3 Plot of misreadings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6.4 Scatter plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6.5 Bland-Alman plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

viii

List of Abbreviations

ANOVA - Analysis of Variance

ICC - Intraclass Correlation Coefficient

IDCS - Interdigital Capacitance Sensor

MV - Minute Volume

PAD - Respiration Pad

PCC - Pearson’s Correlation Coefficient

Piezo - Piezoelectric Transducer

RIP - Respiratory Inductance Plethysmography

RR - Respiratory Rate

TSI - TSI 4040 Airflow Meter

TTI - Transthoracic Impedance Plethysmography

TV - Tidal Volume

ix

Chapter 1

Introduction

The measurement of vital signs is a risk-free, inexpensive, and reproducible clinical

practice that enables identification of physiological deterioration of patients well before

an adverse event occurs [1, 2] and, consequently, deliver optimal care rapidly [3]. Despite

this, numerous studies show that respiratory rate is still not adequately monitored in

hospital wards [4, 5] and that manual clinical measurement is infrequently reported and

unreliable due to inaccuracy and measurement bias [6, 7].

Frequent, accurate, and unbiased monitoring of respiratory rate matters for several reasons.

First, respiratory rate has an important role in the early recognition of diverse illnesses

[8]. Second, respiratory rate is incorporated into decision support systems that identify

patients at risk of clinical deterioration [9]. Finally, respiratory rate is used to alert rapid

response teams and enable intervention before an adverse event occurs [10].

Current technologies for respiratory monitoring are not widely implemented in hospital

wards because of the cost of staffing and automated monitoring equipment [11, 12], as well

as absence of continuous and non-invasive monitoring systems [13]. Continuous monitoring

enables health care providers to observe trends in respiratory status over time and provide

information to assist with decision support and patient management [14]. Therefore, there

is an urgent need for a low-cost electronic respiratory monitor for continuous, non-invasive

monitoring of respiratory rate to improve vital signs documentation within hospitals and

enable early detection of deterioration. To address this need, a contactless capacitance

sensor, henceforth called the Respiration Pad, was developed.

1

Chapter 1. Introduction 2

1.1 Research objectives

This work aims to make a contribution through the design and development of an improved

Respiration Pad (PAD) and its evaluation using a feasibilty study. In order to fully realize

the potential of the PAD for respiratory monitoring, several limitations with the original

PAD by Hart et al. [15] were identified. These limitations include (1) an incomplete

understanding of the sensing principle; (2) technological issues with durability, portability,

safety, and real-time operation; (3) safety issues regarding electromagnetic fields and

electrical safety; and (4) low sensitivity to shallow breathing. The following research

objectives address these limitations.

1. Computational model: Verify the hypothesis that the PAD works by capacitive

deformation sensing by developing computational models to simulate respiration

under the assumptions of capacitive volume, displacement, and deformation sensing.

2. Technology redesign: Improve durability, portability, safety, and enable real-

time operation by redesigning all aspects of the technology including sensor head,

electronics, and software from scratch.

3. Safety assessment: Ensure patient safety by assessing the technology for (1)

electromagnetic exposure safety using Health Canada’s Safety Code 6, (2) electrical

safety through certification to CSA standard, and (3) medical device regulatory

class through a medical device assessment.

4. Feasibility study: Evaluate the performance of the technology using a human

subject to determine whether measurements agree with those from an established

method sufficiently well for clinical purposes.

By addressing the limitations with the original Respiration Pad, this work attempts to

(1) design and develop an improved contactless planar capacitive sensor and (2) evaluate

its feasibility as a clinically accurate method for continuous respiratory rate monitoring.

Chapter 2

Literature Review

2.1 Clinical value of monitoring

Measurement of patient vital signs is a valuable clinical practice because it allows clinicians

to effectively monitor patient status and manage care [2, 8]. Respiratory rate is one of

the four classical vital signs because it enables baseline assessment of ventilation and is

an important indicator of severe disturbance in many organ systems [14]. Furthermore,

numerous studies have shown that respiratory rate can provide early indications of patient

deterioration preceding a failure-to-rescue, which is an established measure of patient

safety and hospital quality [16]. For these reasons, respiratory rate is incorporated into

guidelines and decision rules for diagnosing and managing patient care.

However, ventilation status is a better indicator of physiological condition than simply

respiratory rate alone [14]. Ventilation refers to the process of gaseous exchange between

the atmosphere and the alveoli, and is quantified by parameters such as minute ventilation,

alveolar ventilation, and dead space ventilation. These parameters are dependent on both

respiratory rate and tidal volume. Therefore, respiratory rate alone is not sufficient to

completely characterize a patient’s respiratory status before deterioration [14]. Despite

this, there is still value in measuring respiratory rate even without tidal volume.

Accurate and frequent respiratory rate documentation may better identify those at most

risk for deterioration in a variety of clinical settings. The normal respiratory rate in

adults is between 12 and 20 breaths per minute and should be counted for one full minute

[17]. Abnormal respiratory rates are identified as either bradypnea (< 12 breaths/min) or

tachypnea (> 20 breaths/min) [13]. A pilot study in a tertiary-care hospital found that

3

Chapter 2. Literature Review 4

abnormal respiratory rates (≤ 6 and > 30 breaths/min) increased the risk of mortality by

a factor of 13.7 [3]. These studies relied on chart data and intermittent vital sign data.

Findings may have been even more significant, had the studies investigated the predictive

value of continuous monitoring of respiratory rate using a reliable and automatic method

over manual clinical measurement.

2.2 Prevalence and impact of inadequate monitoring

Despite the accepted importance of proper vital signs documentation, several large studies

have cast doubt on the frequency and accuracy of their measurements in hospitalized

patients. A pilot study conducted in a tertiary-care hospital with 564 patients, found

that changes in respiratory status were associated with high risk of mortality while

accounting for less than 5% of all observations [3]. A study conducted at the Virginia

University Health System (UHS) compared recordings made by medical students and

ward nurses within an hour apart for all 3 shifts during a 5 day period [18]. Results,

shown in figure 2.1, showed that measurements made by nurses in general medicine wards

were systematically biased towards values of 20± 2 breaths/min. Similar findings were

published by a multi-institutional study, which investigated the accuracy of respiratory

rate measurement in 368 patients across 6 tertiary-care centers in the United States [6].

These studies explain that inadequate respiratory monitoring is due to reliance on manual

measurement, long intervals between measurements (8 hours or longer), and lack of

continuous monitoring technologies in general wards. Furthermore, even though the

clinical standard calls for counting respiratory rate for a full 60 seconds, nurses often

count for only 15 or 30 seconds resulting in an over-representation of even values. These

studies provide evidence that poor documentation of respiratory rate is not isolated to a

single medical center or region but is prevalent and systematic.


Figure 2.1: Bias in clinical measurement of respiratory rate at Virginia UHS.

Inadequate respiratory rate monitoring will certainly reduce the quality of patient data and,

therefore, severely undermine the effectiveness of early warning systems and rapid response

teams. Data quality is important because it allows for timely and appropriate medical

intervention leading to improved patient survival and reduced re-hospitalization rates [19].

A study analyzing 25 distinct scoring systems such as the early warning score and modified

early warning score, which are indicators of patient deterioration risk, showed that all

the scoring systems were limited by the quality of vital signs data measured manually

by nursing staff [7]. Furthermore, findings by Tee et al. [4] suggest that activation of

rapid response teams, which have been introduced in hospitals to allow for more timely

response by bypassing the traditional medical consultation system [10], depend heavily

on the quality of data captured by monitoring equipment and the recognition of patient

status by clinicians. Therefore, monitoring technologies are necessary to improve the

quality of vital signs data, which is essential for clinical decision support systems, rapid

response team activation, and health care providers who rely upon that data and those

systems.


2.3 Clinical technologies

Clinical technologies for respiratory monitoring can be categorized by their detection

approaches as airflow sensing, blood-gas measurement, and movement/volume detection.

The most commonly used technologies in these categories are spirometry, capnography,

transthoracic impedance plethysmography (TTI), and respiratory inductance plethysmog-

raphy (RIP).

Spirometry, shown in figure 2.2(a), is most commonly used to assess lung health and func-

tion by accurately measuring airflow using a differential pressure transducer. Spirometry

is limited to short-term use because it is uncomfortable to wear (requiring the use of a

face-mask, nose-clip, and/or mouthpiece) and its use does affect respiratory activity of

the patient [20].

a: Spirometry b: Capnography

Figure 2.2: Invasive respiratory monitors.

Capnography, shown in figure 2.2(b), is a rapid and accurate method for monitoring

concentration of end-tidal carbon dioxide in the respiratory gases by measuring changes

in infra-red radiation absorption. Similar to spirometry, the need for a face mask or nasal

cannula to collect the expired air can cause patient discomfort and inaccuracy of the

measurement [20]. Furthermore, the need for a pumping system for gas sampling is a

limiting factor in practical use [20].

Transthoracic impedance plethysmography, shown in figure 2.3(a), is a non-invasive

respiratory monitoring technology that measures impedance changes between electrodes

placed on the chest wall. TTI suffers from numerous drawbacks including sensitivity

to motion artefacts, cardiac pulsation, and artefacts originating from the skin-electrode


interface. Furthermore, measurement of tidal volume is difficult and not precise enough

for individual breaths [21].

a: TTI b: RIP

Figure 2.3: Non-invasive respiratory monitors.

Respiratory inductance plethysmography, shown in figure 2.3(b), is a non-invasive method

that quantifies respiratory effort by measuring the voltage induced in two coils encircling

the chest and abdomen walls caused by cross-sectional change of the chest and abdomin.

RIP has been established as a superior method for monitoring respiratory rate and

tidal volume and is widely used in sleep clinics as part of a polysomnography test to

diagnose many types of sleep disorders including sleep apnea [22]. Of the four clinical

technologies presented here, RIP is the most useful for non-invasive, continuous respiratory

measurement. However, RIP is still relatively expensive due to the complex and proprietary

data acquisition system; it is difficult to handle; and covers a large body area.

Although pulse oximetry is not a respiratory monitoring technology, it is frequently

used as a non-invasive monitoring tool for estimating arterial blood oxygenation in non-

intubated patients. However, pulse oximetry is not an indicator of adequate ventilation

[13]. Furthermore, the response of pulse oximetry to respiratory changes such as apnea is

very slow [23]. These limitations indicate that pulse oximetry is not a replacement to a

respiratory monitor and should not be used as such.

Therefore, despite the availability of clinical technologies for respiratory measurement, a

low-cost, non-invasive and continuous respiratory rate monitor has not been fully realized.

Therefore, there is a real incentive to study and develop a novel sensor that allows for

non-invasive, continuous respiratory rate monitoring.


2.4 Novel sensors

The evidence presented thus far provide a strong case for continuous monitoring tech-

nologies that provide frequent, unbiased, and accurate monitoring of respiratory rate to

improve detection of patient deterioration and allow for timely response to a recognized

patient crisis. An overview of various novel respiration sensors is presented and three

novel sensor technologies (Respiration Pad, capacitive differential sensor, and Angelcare

baby monitor) were further investigated and qualitatively evaluated to determine their

potential for further technological development.

Advantages Disadvantages

Acoustic • Simple• Useful for apnea detection

• Sensitive to speech and motion artefacts• Sensitive to positioning and tightness of fit• Potential for discomfort

Piezoelectric • Simple• Inexpensive• Unconstrained measurement

• Sensitive to motion artefacts• Dependent on bed characteristics• Dependent on patient orientation, positioning, and size

Capacitive • Simple• Easy fabric integration

• Sensitive to motion artefacts• Signal corruption due to metal• Electromagnetic interference from environment• Potential signal drift• Dependent on bed, clothing, and patient characteristics

Video • Fully non-contact method • Privacy concerns• Limited by visibility of patient• Heavy signal processing and computation• Dependent on lighting conditions and image contrast

Doppler • Fully non-contact method • Not inexpensive• Limited by visibility of patient• Dependent on reflection characteristics of patient surface• Signal degrades with patient orientation, posture, and

position

Table 2.1: Summary of novel respiration sensors.

Table 2.1 presents a summary of the advantages and disadvantages of several novel sensor

technologies found in literature, particuarly acoustic [24], piezoelectric [25], capacitive [26],

video [27], and Doppler sensors [28]. Acoustic sensors work by measuring tracheal sounds

using a microphone placed around the neck or attached to a mouthpiece. Piezoelectric

sensors are the most common implementation which involve sensors placed underneath

the bed. Capacitive sensors embedded into clothing measure respiration through stretch.

Video sensors use cameras placed overhead and sometimes require a marker placed on


the patient. Finally, Doppler sensors use an antenna placed overhead to measure the

doppler-shift between the transmitted and received signal reflected from the body surface.

Overall, these sensors suffer from a variety of shortcomings including low sensitivity, poor

reliability, and impracticality, which have limited their use in the clinic.

2.4.1 Original Respiration Pad

The first functional prototype of the Respiration Pad (PAD) was developed by Hart et al.

[15]. The original PAD, shown in figure 2.4, has 3 major components: the pad sensor,

development board, and iPhone application. The pad sensor consists of dry electrodes

silk-screened on a yoga mat placed underneath the patient. The development board

contains a capacitance chip (Analog Devices AD7746), which sends an excitation signal

to the electrodes to make capacitance measurements. The iPhone application implements

a user interface that captures, filters, and displays the respiration waveform.

Figure 2.4: The original Respiration Pad consisting of the pad sensor, development board,and iPhone application. Figure adapted from Hart et al. [15].

Hart et al. hypothesized that the electric field generated by the electrodes was able to

penetrate into the body to measure the dielectric properties of the human torso, which

change with respiration. However, this hypothesis has been shown to be false because the

generated electric field does not penetrate into the human body (see chapter 3).

The study investigated the accuracy of the PAD measurement against respiratory induc-

tance plethysmography (RIP) for respiratory rates (RR) and tidal volumes (TV) of 5 to


55 breaths/min and 150 to 400 cubic-centimeters (cc), respectively. Results showed high

correlations of 0.882 (95% CI, 0.861 to 0.900) and 0.953 (95% CI, 0.945 to 0.960) for the

SimMan and healthy subject, respectively. However, correlation does not imply agreement

[29] and, therefore, the inappropriate use of correlation may lead to very misleading

conclusions. Furthermore, the study showed that the PAD had reduced sensitivity to

shallow breathing for tidal volumes below 250 cc.

The Hart work showed that it was possible to measure respiratory rate using an active

capacitive sensor. However, two issues remain unclear: (1) the actual sensing mechanism

and (2) the true performance of the sensor using correct statistical analysis. These issues,

as well as the clinical potential of the Respiration Pad, prompted further investigation

and development of the technology.

2.4.2 Capacitive differential sensor

A novel capacitive sensor was developed by Wartzek et al. [30] to measure respiration of

subjects lying supine on a mattress. The sensor, shown in figure 2.5, consists of an array

of 3 pairs of planar electrodes oriented orthogonal to the sagittal plane and positioned

underneath the mattress at either edge of the torso. The sensor works by using a 20 MHz

electric field Us with gain ±G to drive the differential capacitive electrode. The received

signal is amplified by a gain Gr, filtered, and measured as a phase change ∆φ. Phase

changes are caused by Maxwell-Wagner relaxation that occurs due to conductivity and

permittivity changes within biological tissue as a result of respiration [31].

Figure 2.5: Cross-sectional view of the torso and the capacitive differential sensor.

A small self-study was done by the authors to validate the technology against a mass flow

meter (TSI 4040), which was used as the gold standard. In the experimental setup, the


sensor array was placed under the mattress with the subject lying supine. Results indicate

that the size of the phase changes are extremely small with an order of magnitude of 4

milli-degrees. Therefore, the authors admit that the signal quality is very poor resulting

in the wrong estimation of respiratory rate in some cases.

The authors claim that overall, the accuracy of the sensor was within ±0.8 breaths/min

of the the gold standard. However, the experiment conducted by the authors does not

evaluate the sensor performance at multiple tidal volumes or respiratory frequencies.

The authors also fail to use a diverse population of subjects and instead take repeated

measurements on the same subjects at a fixed tidal volume and respiratory rate. Therefore,

the results presented by the authors are very limited and may be misleading.

The capacitive differential sensor has several limitations that make it inadequate for use

as a non-invasive, continuous respiratory monitor. The sensor is sensitive to body size

(i.e. height and weight), patient positioning, and motion artefacts. Also, since the sensor

injects a high-frequency electrical field into the body, there is potential for interference

with other medical devices in the patient vicinity or implanted within the patient’s body

(e.g. pacemakers). These issues are a cause for concern and need to be examined further.

2.4.3 Angelcare baby monitor

The Angelcare AC401 baby monitor is a consumer device designed to provide parents with

wireless sound and movement monitoring of their infant in another room. The monitor is

composed of 3 components: the SensorPad, the nursery unit, and the parent unit. The

purpose of the device is to monitor sound and motion of the infant in the crib and to alert

the parent of the infant’s status. The device does not measure nor display respiratory

rate information on the unit although, as shown from the experiment below, the device

does have the capability to do so.

The SensorPad is actually a spring-loaded force-plate with a piezoelectric pressure trans-

ducer (piezo) placed inside the device. The piezo is made of a thin piezoelectric ceramic

round glued to a thin metal disc. When pressure is applied to the SensorPad, it compresses

the SensorPad resulting in contact between the piezo and a small rubber cone directly

underneath it. The piezo uses the piezoelectric effect to measure pressure by converting

applied pressure to an electrical charge, which is then measured by a circuit.


a: SensorPad on top of mattress. b: SensorPad under mattress.

Figure 2.6: Qualitative testing of SensorPad using an adult subject.

To demonstrate the operation of the piezo transducer, two wires were soldered to the

transducer and connected to an oscilloscope. A qualitative experiment was performed

using 2 healthy subjects of different sizes breathing normally at a fixed respiratory rate

on a foam mattress. The signal for the thin subject was noisy but usable (see figure 2.6);

but the signal for the large-bodied subject was not visually distinguishable from noise.

Although the sensor is sensitive to patient orientation, position, posture, and body size,

these qualitative results indicate that the Angelcare baby monitor or any piezoelectric

pressure transducer has the potential for respiratory monitoring.

2.5 Summary

Respiratory rate is a clinically valuable vital sign; yet, clinical measurement of respiratory

rate remains intermittent, biased, and inaccurate. Research findings suggests that a

point-of-care device that allows for continuous, unbiased, and accurate measurement

of respiratory rate may improve respiratory rate documentation in hospitals. Several

respiration sensors found in literature were discussed; among these sensors, the original

Respiration Pad is novel and shows potential for clinical measurement of respiratory rate.

Therefore, this work aims to make a contribution through the design and development of

an improved Respiration Pad and its evaluation using a feasibility study.

Chapter 3

Computational Model

3.1 Introduction

The Hart work [15] hypothesized that the Respiration Pad works by capacitive volume

sensing as shown in figure 3.1(a). However, doubts about his reasoning and conclusion

arose from literature review [32] that suggested that planar capacitive sensors primarily

work by capacitive displacement sensing as shown in figure 3.1(b). But, intuitively, there

will not be any significant vertical displacement of the torso away from the mattress surface

during respiration since the mattress will contour itself to the shape of the torso. This

suggests that, perhaps, the Respiration Pad is actually measuring capacitance changes due

to changes in electrode geometry on the mattress surface caused by respiration as shown

in figure 3.1(c). Therefore, the new hypothesis is that the Respiration Pad measures

respiration through capacitive deformation sensing.

Although mathematical models describing planar capacitive sensors exist, they do not

provide insight into the interaction of the electric field with the human body nor do

they explain the actual sensing mechanism that is occurring in reality. Therefore, a

computational model is required. The goal of this theoretical study is to validate the

capacitive deformation sensing hypothesis using a computational model.

13

Chapter 3. Computational Model 14

a: Volume sensing.b: Displacement sensing. c: Deformation sensing.

Figure 3.1: Computational models for capacitive (a) volume, (b) displacement, and (c)deformation sensing where the yellow square represents the human torso; the blue striprepresents clothing and linen; and the red and black strips represent the transmitting andreceiving electrodes, respectively.

3.2 Methods

The computational model was developed using a finite-element methods software, which

enables modelling, and simulating coupled physics phenomena to solve various physics

and engineering problems. By using this software to develop a computational model, it is

possible to theoretically demonstrate how capacitance measurement takes place under three

different underlying assumptions of capacitive volume, displacement, and deformation

sensing. This would otherwise be very difficult to demonstrate using experiment since

ensuring consistency throughout all tests will be challenging due to numerous confounding

factors arising from the environment and through random effects and disturbances that are

always present with capacitance measurement [33]. Therefore, by using a computational

model, the hypothesis can painlessly be verified in a repeatable and controlled way.

Development of the computational model involved several steps in the modelling process

including defining the geometry, specifying the physics phenomenon, meshing, solving

and post-processing of the results. To ensure that the computational model was built

correctly, the model setup was verified by comparing the capacitance values calculated

using known analytical formulas against those predicted by the model for both parallel

plate and planar capacitive sensors.

The parallel plate capacitor, shown in figure 3.2, has a simple analytical formula:

C = εrεoA

d(3.1)

where C is the measured capacitance; d is the separation distance between the two


Figure 3.2: Parallel plate capacitive sensor opens up to become a planar capacitive sensor.

capacitor plates; A is the surface area; εr is the permittivity of the dielectric material

sandwiched by the two capacitor plates; and εo = 8.8541× 10−12 F/m is the permittivity

of free space. The dielectric permittivity, εr, is a measure of how easily a material is

polarized in response to an external electric field.

The two-electrode planar capacitive sensor, also shown in figure 3.2, has both electrodes

lying on the same plane and produces a fringing electric field that allows for one-sided

measurement of the dielectric material. The analytical formula for a two-electrode planar

capacitive sensor with electrodes of unit length and dielectric layer of unit height was

developed by Nassr et al. [34] using a conformal mapping technique [35]:

C = εoK(k

′o)

K(ko)(3.2)

where K() is a function of the complete elliptic integrals of the first kind with the elliptic

integral moduli ko = g/(s + g) and k′o =

√1− k2o . The parameters s and 2g represent

electrode width and electrode separation, respectively.

To test the hypothesis, three computational models were developed; one model for each

capacitive sensing mechanism shown in figure 3.1. A simulation was performed using each

model to generate a simulated respiratory waveform. By comparing the magnitude of the

capacitance change, ∆C, the hypothesis that the Respiration Pad works by capacitive

deformation sensing can be validated.

To standardize the simulations across all three models, the models were built using two

planar electrodes of unit width and length (1 cm x 1 cm) and negligible height (1 mm).

The electrodes are placed such that one electrode is underneath the sternum and the

other is spaced 30 cm apart towards the lumbar spine. A spacing of 30 cm between the

electrodes was used because this should be sufficient to cover the entire length of an

adult human lung. The models have two stationary electrodes (one transmitting and

one receiving) placed on top of a mattress and underneath cloth (bedsheets and patient


Tissue Conductivity (s/m) Permittivity (εr)

Skin 0.29 209.2Fat 0.03 9.7

Muscle 0.64 110.6Bone 0.14 48.3

Lung (Inspired) 0.25 70.8Lung (Expired) 0.47 119.8

Table 3.1: Material properties of human tissue.

clothing). On top of the cloth is a simple human phantom consisting of layers of skin,

fat, muscle, bone, heart, and lung. Table 3.1 shows the material properties used in the

model to ensure that the interaction between the electric field and the human tissue is as

accurate as possible.

3.3 Results

Table 3.2 compares the capacitance values derived from the analytical formulas against

values determined by the computational models for both parallel plate and planar capac-

itive sensors. Capacitance values were calculated for a parallel plate capacitive sensor

with electrodes of size 1 cm x 1 cm separated by 1 cm air gap, and a planar capacitive

sensor with electrodes of size 1 cm x 1 cm separated by 2 cm air gap. These dimensions

were chosen because they represent a standard unit sized capacitor. Results show that

the computational model differed in the calculated capacitance by 1.6% and 3.5% for the

parallel plate and planar capacitive sensors, respectively. Since the difference is small, this

verifies that the model was built and simulated correctly with the appropriate physics,

meshing, and solver configurations.

Parallel Plate Capacitive Sensor Planar Capacitive Sensor

Formula 0.0885 pF 0.117 pF

Model 0.0871 pF 0.113 pF

Difference 1.6% 3.5%

Table 3.2: Capacitance values derived from formulas and the models for parallel plateand planar capacitive sensors to verify correct computational model setup.


Figure 3.3 shows the simulated respiratory waveforms generated by the computational

models for each of the sensing mechanisms; and figures 3.4, 3.5, and 3.6 show the

corresponding electric field distributions. The capacitance deformation sensing model

produced the largest capacitance change with ∆C = 0.085 pF, followed by the capacitive

displacement sensing model with ∆C = 0.015 pF. The capacitive volume sensing model

had ∆C = 0 pF indicating that changes in lung volume do not affect the measured

capacitance.

0 1 2 30.8

0.85

0.9

0.95

1

Breathing Cycle

Cap

acita

nce

(pF

)

a: Volume sensing.

0 1 2 30.8

0.85

0.9

0.95

1

Breathing Cycle

Cap

acita

nce

(pF

)

b: Displacement sensing.

0 1 2 30.8

0.85

0.9

0.95

1

Breathing Cycle

Cap

acita

nce

(pF

)

c: Deformation sensing.

Figure 3.3: Simulated breathing waveforms for capacitive (a) volume, (b) displacement,and (c) deformation sensing.

3.4 Discussion

The goal of developing a computational model was to better understand the physical

principle behind capacitive sensing utilized by the Respiration Pad. Literature review cast

doubt on the original explanation of the sensing mechanism, thus, motivating this work.

Figure 3.4 shows that no change in the electric field occurs when the chest expands due to

inhalation. This is consistent with the simulated waveform for capacitive volume sensing

in figure 3.3(a), which shows a flat line indicating no change in capacitance. Furthermore,

figure 3.4 show that the electric field remains constant within the human torso but changes

outside the torso, which suggests that the electric field does not penetrate into the body.

Therefore, the results confirm that the electric field does not penetrate into the body and

that the Respiration Pad does not measure lung volume changes due to respiration.


a: Resting. b: Chest expands.

Figure 3.4: Electric field distributions for capacitive volume sensing.

Figure 3.5 shows noticable changes in the electric field distribution when the chest lifts off

the mattress surface. This lift-off is consistent with the simulated respiratory waveform

in figure 3.3(b), which shows an exponential change in capacitance with a change of

∆C = 0.015 pF between the two extremes of resting and a unit displacement of the torso

above the electrode surface due to inhalation. However, the assumption of lift-off does not

apply when the patient is lying on a mattress because the mattress contours to the body

during respiration; therefore, no displacement can occur and no capacitance change is

possible on a mattress. Alternatively, if the Respiration Pad is placed on the floor, then,

in this case, there will be lift-off of the chest from the electrode surface due to the rigidity

of the floor.

a: Resting. b: Chest lifts.

Figure 3.5: Electric field distributions for capacitive displacement sensing.


Figure 3.6 shows a significant change in the electric field distribution due to deformation

of the mattress surface, which corresponds to the simulated respiratory waveform in figure

3.3(c) showing a linear change in capacitance. The capacitance changes by ∆C = 0.085

pF between resting and a unit change in deformation of the mattress surface. This result

is consistent with formulas for parallel plate and planar capacitive sensors, which indicate

that as more deformation in the mattress surface occurs the more the electrodes rotate to

face each other to increase the effective surface area resulting in an increase in capacitance.

a: Resting. b: Mattress deformed.

Figure 3.6: Electric field distributions for capacitive deformation sensing.

There are some limitations with the computational model. First, three sensing mechanisms

were explored; however, it may possible that other physical phenomenon were unaccounted

for. Second, the model is simple and does not incorporate material properties of the

electrode and dielectrics aside from the human phantom, which may offset the capacitance

values but not the conclusions drawn from the results. The human phantom was also

simple, not of high resolution, and did not model all organs and tissues of the body.

Finally, stray capacitive effects and noise from the environment could not be modelled

and therefore was left out. Despite all these limitations, the computational model was

adequate enough to draw some clear conclusions about the sensing mechanism.

In summary, the results show that (1) capacitive volume sensing does not occur, (2) capac-

itive displacement sensing is not possible on a mattress, and (3) capacitive deformation

sensing results in the largest capacitance change in the simulated respiratory waveform.

Therefore, the Respiration Pad works by capacitive deformation sensing as hypothesized.

Having verified that the Respiration Pad works by capacitive deformation sensing, the

following text describes how electrode geometry optimization may be achieved using the


computational model in future work. Figure 4.1 shows the interdigital capacitance sensor

(IDCS) design that was selected as the basis for the Respiration Pad. The IDCS utilizes

multiple electrode fingers to control the strength of the output signal by changing the

number (N), width (s), and separation (2g) of the electrode fingers. To optimize for the

electrode geometry, five models need to be developed; one model for each IDCS with

N = 2, 3, 4, 5, 6 electrode fingers. This leaves two parameters (s and g) for each model.

However, electrode separation (2g) is not an independent variable since the maximum

IDCS width was restricted to 30 cm (see chapter 4). Also, the IDCS length does not

affect the results of optimization so it can be taken as unity. Therefore, each model will

have only one independent variable, electrode width (s), to optimize for, with electrode

separation (2g) becoming a function of the geometry. Finding the optimal electrode

geometry means selecting a specific s, 2g, and N yielding the largest signal-to-noise ratio

(SNR), which is calculated as follows:

SNR = 10 log10

(Psignal

Pnoise

)∝ ∆C (3.3)

where Psignal and Pnoise are signal power and noise power, respectively. Since noise power

is independent of electrode geometry, the noise term drops out of the equation leaving

behind signal power as the only term. Furthermore, signal power can be reduced to

capacitance change ∆C. From this we understand that SNR is maximized when ∆C is

maximized. Therefore, optimization is possible by selecting the specific electrode geometry

that will yield the largest ∆C.

3.5 Summary

Three computational models were developed to verify the hypothesis that the Respiration

Pad works by capacitive deformation sensing. Results indicate that capacitive deformation

sensing had the strongest response, while capacitive volume sensing had no response.

Although, the model for capacitive displacement sensing produced a small response,

intuitively, we know that displacement sensing is not possible on a mattress because there

is no displacement of the torso from the electrode surface. Therefore, the results validate

the hypothesis and refute the original explanation of the sensing mechanism.

Chapter 4

Technology Redesign

4.1 Introduction

Hart et al. [15] made a significant contribution by developing a functional prototype of the

Respiration Pad to show that capacitance measurement of respiratory rate using a planar

capacitive sensor was possible. However, the initial prototype had several limitations:

• Non-durable: Electrodes made of conductive paint would flake off and deteriorate

after a few weeks of repeated use.

• Limited portability: Sensor head was a large and heavy yoga mat which is

difficult to seamlessly integrate with a hospital bed.

• Unsafe: Electronic circuits were breadboarded and not electrically isolated in an

enclosure, which exposes the electronics to damage and puts the patient at risk. No

electromagnetic or electrical safety assessment was done.

• No real-time operation: Software was buggy, slow, and requires manual user

interaction, which prevented the sensor from functioning automatically and inde-

pendently without user intervention.

A major component of this work was improving and refining the initial prototype by

addressing these limitations. By doing so, the current iteration of the technology is more

durable, safe, portable, and allows for real-time operation. The following section describes

the design choices that were made to achieve these improvements.

21

Chapter 4. Technology Redesign 22

4.2 Design choices

The interdigital capacitance sensor (IDCS) design, shown in figure 4.1, was selected

for the electrode configuration because it is versatile, extensively studied in literature,

and allows for electrode geometry optimization to improve signal quality by adjusting

the number (N), width (s), and separation (2g) of the electrode fingers. In this work,

electrode geometry optimization was not performed; instead, the selection of the number,

width, and separation of the fingers was chosen through trial and error. It was found that

N = 3 fingers, width of s = 2.5 cm, and separation of 2g = 10 cm yielded satisfactory

results and so was selected for the final design.

Figure 4.1: Interdigital capacitance sensor with electrode width (s), electrode separation(2g), number of electrode fingers (N), and applied voltage (±V ).

To improve the durability and portability of the sensor, four different sensor heads were

investigated. All together, four dielectric materials (plastic, cloth, thick fabric, and rubber)

and three conductive materials (conductive thread, conductive fabric, and conductive foil)

were used to build four distinct sensor heads shown in figure 4.2: Bedsheet Pad, Yoga

Pad, Fabric Pad, and Plastic Pad. Each sensor head was also qualitatively evaluated

using a human subject throughout development.


a: Bedsheet Pad. b: Yoga Pad.

c: Fabric Pad. d: Plastic Pad.

Figure 4.2: Comparison of different sensor heads designed and constructed, includingsensor heads with (a) conductive fabric stitched onto a bed sheet, (b) conductive fabricglued and taped onto a yoga mat, (c) conductive thread stitched onto a thick fabric, and(d) conductive foil heat-sealed between two plastic sheets.

The best design was the Bedsheet Pad because it was durable, portable, and enabled

seamless integration with a hospital bed. Qualitative testing showed that this Pad

produced the best signal, which makes sense because the Bedsheet Pad fits very snugly

onto a hospital mattress enabling accurate measurement of mattress deformation due

to breathing. The Plastic Pad performed poorly because it would crumple up and

distort heavily when a body was present leading to inaccurate measurements of mattress

deformation. The Fabric Pad is similar to the Bedsheet Pad, but utilizes heavier cloth

and conductive thread as opposed to conductive fabric. However, its limitation was that

it was difficult to create wide electrodes using thin thread without leaving gaps or uneven

spacing leading to distortions in the electric field lines resulting in undesirable non-linear

effects. Finally, the Yoga Pad was created to directly improve upon the original design.


Instead of silk-screened conductive paint, durable fabric electrodes were taped and glued

onto the mat surface. This solved the problem of the flaking paint; however, it was

difficult for the electrodes to remain firmly fixed on the mat surface after repeated use.

Furthermore, the yoga mat doesn’t address the problem of portability because it is still

relatively heavy and bulky. Ultimately, the Bedsheet Pad was chosen as the best of the

four designs.

The electronics of the Respiration Pad were custom designed from scratch, fabricated

in a foundry, and enclosed in a hard plastic enclosure, which improved durability of the

electronics and improved patient safety by reducing risk of electric discharge. Furthermore,

signal quality was improved by the addition of a hardware filter to increase noise immunity.

Real-time operation was made possible by developing a new integrated piece of software

from the ground up without relying on external applications. The new software was built

using Matlab and allowed for continuous and real-time capture, storage, filtering, and

processing of data from the Respiration Pad wirelessly via Bluetooth. The software works

by sending a command to the device to initiate transfer. The capture, storage, filtering,

and processing of data happens transparently in the background and, when complete,

plots of the respiratory signal showing the calculated respiratory rate is presented to

the user. Signal filtering was achieved by consecutively applying a lowpass and highpass

filter on the raw signal to produce a clean, filtered signal. The highpass filter has a

cut-off frequency of 0.05 Hz (corresponding to 3 breaths/min) and the lowpass filter has

a cut-off frequency of 1 Hz (corresponding to 60 breaths/min). The filters have a very

desirable sharp drop-off at the cut-off frequencies with minimal phase disturbance in the

frequency band of interest, which corresponds to respiratory rates in the range of 5 to 35

breaths/min. With the new software, real-time operation was made possible.

4.3 Final prototype

The Respiration Pad is a planar capacitive sensor that works by capacitive measurement

of small geometry fluctuations of the mattress surface due to mechanical deformation

induced by breathing. The Respiration Pad consists of three major components: sensor

head, electronics, and software.

The sensor head, shown in figure 4.3, is made from a fitted, elastic bed sheet containing

dry conductive fabric electrodes stitched onto the bed sheet. The bed sheet itself is fitted


directly on top of a hospital twin-sized foam mattress found on a typical hospital stretcher.

The sensor head may be covered with additional linen to isolate the electrodes from

the patient who lies on top of the mattress and the linen. The sensor head is the only

component of the system in contact with the patient and it is isolated from the electronics

by 5 kilovolts of optical isolation so that any surge from the main power supply will not

be an issue. Furthermore, the electric field generated by the electrodes is very small,

does not penetrate into the body, and decays exponentially with distance. Therefore,

the sensor is safe for patient use from both an electrical and electromagnetic exposure

point-of-view (see chapter 5 for more details).

Figure 4.3: Sensor head consisting of electrodes made from conductive fabric; buttons tointerface with the coaxial cable; and a fitted bed sheet, which fits onto a foam mattress.

The electronics consist of two stackable boards enclosed by a small plastic case. The

two boards communicate with each other via a board interconnect. The first board

handles wireless transmission of data via the Bluetooth module and distributes power to

the entire circuit by receiving power via the micro-USB connector. The second board

handles capacitance measurement using an off-the-shelf capacitance chip (Analog Devices

AD7746) and ensures safety of the subject through 5 kilovolts of data/power isolation

using a micro-transformer in compliance with IEC 60601-1 and IEC 60950-1 standards.

The AD7746 capacitance chip is capable of taking measurements with an accuracy of

4 fF over a dynamic range of 4 pF by using a 32 kHz square-wave excitation signal at

a maximum of 1.65 volts and 21 microamps. The capacitance chip is protected from

electrostatic discharge by ESD protection.


Figure 4.4: Sensor electronics consisting of micro-USB connector, Bluetooth module,board interconnect, micro-transformer, AD7746 capacitance chip, and ESD protection.

The electrical components, shown in figure 4.5, consists of a several components that

connect the separate pieces of the sensor together as one functional unit. The electronics

receive power through a USB cable, which connects to a medical grade wall mount power

adapter with USB charger. The power adapter complies with UL 60601-1 and IEC/EN

60601-1 electrical standards. A hardware filter, a front-end to the capacitance chip, is

used to improve signal quality by reducing the noise in capacitance measurement and

filtering out the frequencies not of interest. The coaxial cables connect the electronics to

the hardware filter and from the hardware filter to the button connectors, which snap

onto buttons found on the electrodes stitched on the sensor head. The device is powered

on automatically when the power adapter is plugged into a wall outlet.

Figure 4.5: Sensor electrical components consisting of power adapter, USB cable, hardwarefilter, coaxial cables, and button connectors.


The software was developed in the Matlab scripting language and runs on a laptop with

the Matlab runtime environment installed. The software is an integrated application

that allows for automated and continuous capture, processing, and display of data. The

software achieves this by sending the command to the Respiration Pad to initiate and

transmit capacitance measurements. The software receives the wirelessly transmitted data

by interfacing with a Bluetooth receiver connected to the laptop. Since the transmission

is wireless, the laptop need not be present in the patient vicinity and, therefore, is not

medical grade. Once wireless data capture is complete, the user has a chance to select

the segment of data to be filtered and processed. Processed data is then displayed to the

user as plots of the respiratory waveforms before and after filtering, along with the power

spectrum, and calculated respiratory rate as shown in figure 4.6.

Figure 4.6: Sensor software showing plots of the raw and filtered signal with the corre-sponding power spectrum and calculated respiratory rate.

4.4 Summary

This chapter discusses the limitations with the original Respiration Pad and the improve-

ments that were made to create a more robust and capable sensor in preparation for the

feasibility study. Design choices were discussed that led to a significant improvement

in the durability, portability, safety, and real-time operation of the technology. Work

involved evaluating new materials for the sensor head, developing custom electronics,

and creating a new integrated software to handle capture, processing, and display of the

respiration signal. Finally, the final prototype of the technology was presented.

Chapter 5

Safety Assessment

5.1 Introduction

Safety is of paramount concern for a medical device when used on patients. Before a

medical device can be approved for use on patients it must meet several safety criteria,

including (1) electromagnetic exposure safety, (2) electrical safety, and (3) medical device

classification. This chapter deals with these safety considerations in detail.

5.2 Electromagnetic exposure safety

Electromagnetic fields are composed of orthogonal electric and magnetic field components.

A strong electric field may be dangerous to patient health because, although no direct

current is injected into the body, the body may experience displacement currents induced

by the electric field which have the potential to affect biological tissues. The type of

interaction experienced by the human body depends on the frequency and strength of the

electric field. At low frequencies (3 to 100 kHz), the interaction of the displacement currents

with nervous system tissue is of concern because of the sensitivity of nervous system tissue

to these currents [36]. Above 100 kHz, the nervous tissue becomes less sensitive to direct

stimulation by electromagnetic fields; but the heating effect due to electromagnetic fields

becomes the major mechanism of interaction [36]. Since the Respiration Pad operates

below 100 kHz, the heating effect is not of concern.

The Respiration Pad generates non-ionizing radiation in the form of a 32 kHz electric field

28

Chapter 5. Safety Assessment 29

to make measurements, which may have the potential to adversely affect human health.

To ensure that the Respiration Pad is safe for human use, it has been assessed against

Health Canada’s Safety Code 6 regulations [37]. Specifically, the maximum electric field

strength generated by the sensor is compared against the maximum permissible exposure

limit. Although, the Respiration Pad is a “contactless” device in that no direct skin

contact is required for operation, in order to account for accidental direct skin contact,

the contact current limits were also considered. As shown in table 5.1, the maximum

electric field strength of 1.65 V/m, for the Respiration Pad, is orders of magnitude smaller

than the maximum permissible exposure limits of 600 V/m and 280 V/m for occupational

and general public limits, respectively. Similarly, the maximum current of 0.021 mA,

that may accidentally be exposed to the patient, is also orders of magnitude below the

contact current limits of 32 mA and 14.4 mA for occupational and general public limits,

respectively. This shows that the Respiration Pad is well under the prescribed exposure

limits and, therefore, is safe for use.

Exposure Limits

Safety Limits Occupational General Public Respiration Pad Levels

MPE (V/m) 600 280 1.65

Contact Current (mA) 32 14.4 0.021

Table 5.1: Comparison of electromagnetic maximum exposure and contact current limitsfrom Health Canada’s Safety Code 6 against those of the Respiration Pad.

5.3 Electrical safety

The Respiration Pad has received equipment certification to the medical electrical standard

(ANSI/AAMI ES60601-1:2005 [38]) and was classified as a class 2 BF equipment. Class

2 equipment means that no single failure of this device can result in dangerous voltage

becoming exposed to the patient such that it may cause an electric shock. Furthermore,

this is achieved without relying on an earthed metal casing. BF means that the device is

a type B equipment with a type F applied part. Type B equipment is one that provides a

particular degree of protection against electrical discharge through grounding. Type F

applied part is one that extends from the patient into the equipment and is isolated from

all other parts of the equipment.

Chapter 5. Safety Assessment 30

5.4 Medical device classification

Medical devices in Canada are subject to varying degrees of regulation depending on

its risk category. Indicators of risk include degree of invasiveness, duration of contact,

body systems affected, and local versus systemic effects [39]. The Respiration Pad is a

medical device because it is a medical instrument intended for diagnosis of abnormal

respiratory states in patients. The Respiration Pad is a non-invasive, contactless, and

active device with a measuring function. Non-invasive means that no part of the body is

penetrated; no body orifice is contacted; it’s not for surgical use, and it’s not implantable.

Contactless means that there is no direct skin contact with the patient. Active means

that an external power source is used and energy conversion only happens within the

device itself. No energy conversion takes place between the device and human tissue or

within human tissue. Finally, a measuring function means that the device is intended to

quantitatively measure a physiological parameter such that the result of the measurement

is displayed in legal units.

The Respiration Pad received a preliminary medical device assessment as a class 2

medical device. The justification is that the intended purpose of the device plus software

implies accuracy where non-compliance could adversely effect patient safety. Furthermore,

predicate devices are class 2.

5.5 Summary

Electromagnetic exposure safety was assessed by ensuring that the generated electric

field and leakage current are within tolerable levels according to Health Canada’s Safety

Code 6. The assessment showed that the Respiration Pad operated within the prescribed

thresholds. Electrical safety was assessed by a qualified technician to ensure that the

Respiration Pad conformed to CSA standards for electrical equipment. The Respiration

Pad successfully passed certification and was classified as a class 2 BF equipment. Finally,

the Respiration Pad was classified as a class 2 medical device.

Chapter 6

Feasibility Study

6.1 Introduction

The purpose of this feasibility study was to determine whether the Respiration Pad can

produce measurements that agree with those from an established method sufficiently

well for clinical purposes. The hypothesis was that the Respiration Pad does statisti-

cally agree sufficiently well for clinical purposes. The actual clinical gold standard is

respiratory inductance plethysmograph (RIP). However, the Respiration Pad cannot be

used simultaneously with RIP because the metal in the RIP interferes with capacitance

measurement by the Respiration Pad. Therefore, an airflow meter (TSI 4040), which

allowed for continuous and synchronized data capture was used as a substitute. For the

purposes of this study, this substitution was considered satisfactory.

6.2 Methods

Figure 6.1 describes the experimental setup. The study simultaneously measured res-

piratory rates with the PAD and TSI using 1 healthy adult subject under 6 different

combinations of clothing (either t-shirt or coat) and added weights (0 lbs, 10 lbs, or 20 lbs

extra). Each measurement was taken for 15 seconds across 4 different respiratory rates

(RR) of 5, 15, 25, 35 breaths/min and 3 different tidal volumes (TV) of shallow, normal,

and deep breathing corresonding to clinical values of 250, 500, and 1000 mL/breath,

respectively [40]. Each simulated body type combination was taken as an independent

31

Chapter 6. Feasibility Study 32

measurement. A metronome was used to set breathing at the appropriate RR by beeping

at every inhale and exhale. A separate spirometer (by Datex) ensured that the subject

respired at a consist TV by visually displaying the tidal volume measurements on a

monitor in real-time.

a: Subject position b: Respiration readings

Figure 6.1: (a) shows the subject lying supine with the electrodes positioned between theshoulders and hip; (b) shows the readings from the PAD.

Many studies use the Pearson’s correlation coefficient (PCC) as an indicator of agreement

between two devices. However, this is statistically incorrect because the PCC measures

the strength of a relation between two variables, not the agreement between them

[29]. Therefore, to properly validate the hypothesis, several analyses were conducted to

statistically quantify the degree of agreement between the two devices which both measure

a continuous, numerical variable. The analyses used in this study were Bland-Altman

analysis, a single-factor analysis of variance (ANOVA), paired t-test, intraclass correlation

coefficient (ICC), and the British Standards Institute reproducibility coefficient (BSI

coefficient). The PCC is included for completeness.

6.3 Results

Boxplots of the results were generated in figure 6.2 where misread values, shown in figure

6.3, were removed and measurements were repeated until a proper reading was taken.

Figure 6.4 shows the spread of the data from the line of equality for each TV. The

Bland-Altman plots in figure 6.5 show the distribution of these average RR measures. Of


Shallow Normal Deep0

5

10

15

20

25

30

35

40

45

Tidal Volume

Re

sp

ira

tory

Ra

te (

min

−1)

Figure 6.2: Boxplots of the results were generated for both the TSI and PAD measurementswith TV along the x-axis and RR along the y-axis. Measurements were grouped in pairsto facilitate visual comparison between the devices. Each pair has the TSI measurements(coloured black) on the left and PAD measurements (coloured blue) on the right.

the 72 pairs of RR measurements from the TSI and PAD, only 1.4% (95% CI, -3.0% to

5.8%) of the values measured by the PAD exceeded the clinically significant threshold of

4 breaths/min.

Slicing the results by RR, as shown in table 6.1, we see that the mean difference for any

RR does not exceed 1 breath/min. The mean difference across all RR is on average -0.02

(95% CI, -0.34 to 0.31), which includes 0 in the confidence interval and, therefore, this

difference is not statistically significant. This is also confirmed by using paired t-tests,

which failed to reject the null hypothesis of equivalence (P > 0.92 on average for 5%

significance, n− 1 = 5 degrees of freedom, and two-tails probability). The BSI coefficient

indicates the maximum likely difference between a pair of readings, which in our case

should not exceed the clinically significant threshold of 4 breaths/min. The average BSI

coefficient across all RR is 2σ = 2.76, which is below the clinical threshold. The largest

BSI coefficient occurs at the highest RR of 35 breaths/min with a value of 4.05 which is

reasonably close to the threshold.


0

1

2

3

4

5

6

7

8

shallow normal deep

# o

f M

isre

ad

s

Tidal Volume

coat tshirt

Figure 6.3: Sometimes, during the study, the PAD would misread the RR and, therefore,repeated measurements were taken until a proper reading occurred.

RR (min−1) Mean Difference BSI Coefficient Paired T-Test (P-value)

5 0.20 (-0.05 to 0.45) 1.00 0.11

15 0.01 (-0.49 to 0.51) 2.01 0.98

25 -0.26 (-1.04 to 0.52) 3.15 0.49

35 -0.01 (-1.02 to 1.00) 4.05 0.99

Average -0.02 (-0.34 to 0.31) 2.76 0.92

Table 6.1: Results sliced by RR.

Slicing the results by TV, as shown in table 6.2, no significant difference was found between

the PAD and TSI (P > 0.96 for all TV) based on single-factor ANOVA. Measurements

from the PAD and TSI are in high agreement with an average ICC value of 0.9926 (95%

CI, 0.9882 to 0.9954). As a point of reference, the flash mob research paper [6] calculated

the average ICC for RR as 0.26 (95% CI, 0.16 to 0.35) and for pulse rate as 0.65 (95%

CI, 0.58 to 0.71). Although, it is statistically incorrect to use the Pearson’s correlation

coefficient (PCC), the PCC values are near unity and comparable to the ICC values.


0 5 10 15 20 25 30 35 40 450

5

10

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fro

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a: Shallow

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d: All

Figure 6.4: A scatter plot was generated for each TV with the TSI measurements onthe x-axis and the corresponding PAD measurements on the y-axis for all RR. Theseplots show the spread of the data from the line of equality for each TV. The spread istechnically not a measure of agreement but of correlation. The smaller the spread thehigher the correlation between the measurements.


4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7−5

−4

−3

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a: 5 breaths/min

14 14.5 15 15.5 16 16.5−5

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b: 15 breaths/min

22.5 23 23.5 24 24.5 25 25.5 26 26.5−5

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c: 25 breaths/min

32.5 33 33.5 34 34.5 35 35.5 36 36.5 37 37.5−5

−4

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d: 35 breaths/min

Figure 6.5: Bland-Altman plots of the results were generated using individual measurementdifferences in RR between the TSI and PAD (y-axis, “Difference in RR”) and the bestestimate of the actual RR (x-axis, “Average RR”). The middle blue dashed line showsthe average RR difference (0.20, 0.01, -0.26, and -0.01 breaths/min for A,B,C, and D,respectively) while the upper and lower red dashed lines depict the clinically significantthreshold for the difference of ±4 breaths/min.


TV ICC PCC ANOVA (P-value)

Shallow 0.9828 (0.9609 to 0.9925) 0.9824 (0.9590 to 0.9925) 0.963

Normal 0.9957 (0.9902 to 0.9982) 0.9957 (0.9900 to 0.9982) 0.975

Deep 0.9997 (0.9993 to 0.9999) 0.9997 (0.9993 to 0.9999) 0.975

Average 0.9926 (0.9882 to 0.9954) 0.9925 (0.9880 to 0.9953) 0.993

Table 6.2: Results sliced by TV.

6.4 Discussion

The aim of this study was to determine the level of agreement between the PAD and

TSI to validate the hypothesis that the PAD, when compared to the gold standard (TSI),

performs sufficiently well for simple measurement of respiratory rate. All the results

indicate this is so.

The boxplots and scatter plots visually show strong agreement between both devices. The

ICC and PCC values both quantify the strength of agreement as being very strong and

the BSI coefficient indicates that the maximum likely difference between a pair of readings

does not exceed the clinically significant threshold of 4 breaths/min. Furthermore, the

ANOVA and paired t-tests confirm that there is no statistical difference between the PAD

and TSI measurements. Therefore, it is safe to conclude that the Respiration Pad can

accurately measure respiratory rate for a wide range of RR from 5 to 35 breaths/min and

TV from 250 mL to 1000 mL.

In the study, different body types were simulated using clothing and additional weights

placed on top of a thin subject. From figure 6.3, we see that misreadings occur with

the PAD when the subject wears a large coat. Misreadings refer to instances when

the Respiration Pad is not able to properly measure respiration. Misreadings can be

identified when the power in the signal is overwhelmed by the power in the noise. This

may occur for numerous reasons including stray noise in the electrodes and circuitry

due to electromagnetic interference from the environment, static discharge from patient

clothing or mattress linen corrupting capacitance measurement, and motion and breathing

artefacts. Unfortunately, the actual capacitance values are extremely small in the 0.1

to 1 picofarad (pF) range and the actual size of the waveform measured as a change in

capacitance, ∆C, is even smaller in the 0.01 to 0.1 pF range. Therefore, the fact that


misreadings occur is not very surprising and may even be worse outside the controlled

setup of this study.

In this study, measurements were repeated until no misreading occurred. Admittedly,

this may improperly bias the results in favour of the Respiration Pad. Misreadings occur

due to interference in the capacitive measurement due to environmental factors. The

difficulty here is that it may not be possible to differentiate a misreading from sleep

apnea experienced by the patient or from a sensor malfunction, for example, due to a

disconnected wire. Therefore, misreads will reduce the overall reliability of the Respiration

Pad.

Ideally, there shouldn’t be any misreadings, but there are several ways to mitigate the

potential for misreadings. The first approach is to reduce the potential for misreadings

by improving the sensitivity and robustness of the sensor by optimizing the electrode

geometry, investigating use of different materials that will reduce electrical discharge

causing capacitive interference, and by incorporating active shielding into the sensor design

to isolate the electrical field to a particular sensing region to limit external electromagnetic

interference. Furthermore, the Respiration Pad is intended for continuous use, therefore,

even if misreadings do occur once for every 15 seconds of capture, there will be 4 captures

ever minute and over the span of a few minutes, the misreadings can be removed by signal

analysis and the actual trend in the respiratory rate can be identified.

The amount of misreadings are highest for shallow breathing but decrease as tidal volume

increases. This inverse relationship makes sense since shallow breathing is most difficult

to detect whereas deep breathing is easier to detect. Misreadings did not occur when the

subject wore a t-shirt, which indicates that the size of the patient affects the capacitance

measurement by the PAD. Also, the TSI did not have any misreadings as expected, since

it is the clinical gold standard.

One of the original concerns that prompted this feasibility study was whether the Respi-

ration Pad was sensitive enough to accurately measuring RR during shallow breathing.

Despite the misreadings, results show that the new Respiration Pad is sensitive enough

even during shallow breathing with a mean difference in measured values of -0.20 (95%

CI, -0.05 to 0.45) breaths/min. Therefore, this feasibility study confirms that there has

been an improvement in the technology since the original Respiration Pad.

Currently, the Respiration Pad has been designed to exclusively measure respiratory

rate. However, from a clinical point of view, there is significant value in also measuring


quantities such as minute volume (MV) and tidal volume (TV) in non-intubated patients.

This limits the clinical value of the technology in practice since patients in need of

monitoring typically have substantial cardiopulmonary co-morbidities such as asthma,

chronic obstructive pulmonary disease, obstructive sleep apnea, and congestive heart

failure [41]. For example, obese patients suffering from obstructive sleep apnea may

experience a situation where there is chest wall motion without air exchange due to

chronic airway obstruction. Such a situation may present itself as breathing to a health

care provider observing a patient, but would result in consistently low TV or MV readings,

which could be used as an early indicator of impending respiratory failure [42]. Therefore,

to improve the clinical utility of the Respiration Pad, the device needs the capability to

measure TV or MV.

It has been shown using the computational model that the Respiration Pad works through

capacitive deformation sensing, which means that the sensor detects capacitance changes

caused by changes in electrode geometry on the mattress surface as a person breaths.

Therefore, what is actually being measured is respiratory effort of the patient indirectly

through body motion. This is a very indirect method of respiratory measurement and is

sensitive to confounding environmental factors such as mattress elasticity and presence of

metal in the sensing region as well as patient body size and electromagnetic interference

from the environment.

This indirect method of respiration measurement coupled with the limitations of the

technology suggest that it may be difficult for this technology to accurately measure tidal

volume. Typically, what would be done to measure tidal volume, is to calibrate the device

against a spirometer for each patient so that the measured amplitude of the respiration

signal can be multiplied to get the tidal volume. However, with the Respiration Pad, it

may not be possible to ensure a consistent amplitude between use because the Respiration

Pad is sensitive to not only mattress elasticity, patient clothing, and patient size, but

potentially also to patient orientation, posture, position, and motion artefacts.

In contrast, the RIP technology uses two elastic bands placed around the chest and

abdomen to measure changes in cross-sectional area, which is linearly related to lung

volume. This allows the RIP to more directly measure respiratory effort while minimizing

environmental effects. Another advantage is that the RIP is less sensitive to distortion

of the chest and abdomen compartments during motion or posture change; therefore,

this reduces the effect of patient posture, orientation, position, and motion artefacts

on the measured signal. Furthermore, studies using RIP show that in normal subjects,


the measured value remains within ±10% after calibration for 93% of the breaths [43],

thus, making RIP a more reliable method for monitoring tidal volume in spontaneously

breathing patients than the Respiration Pad.

The Respiration Pad aims to be a contactless alternative to the RIP technology. The

major difference being that the Respiration Pad does not require electrodes or bands

attached to the patient’s torso. However, this trade-off results in many limitations and

weaknesses in the Respiration Pad technology and may not be justifiable.

As mentioned earlier, the Respiration Pad indirectly measures respiratory effort. However,

respiratory effort alone cannot be used to detect more complex respiratory disorders

such as paradoxical breathing. During paradoxical breathing, the airflow is out of

phase with respiratory effort and chest movement is unsynchronized, or paradoxical,

to breathing efforts. Detecting paradoxical breathing requires airflow measurement or

at least measurement of the volume change of the abdomen and chest compartments

[44]. This is currently not possible with the Respiration Pad because it has only one

measurement channel, but it is possible with the RIP. Even if the capability to measure

two compartments was added to the Respiration Pad, the device will still suffer from

the limitations mentioned previously, especially the fact that the measured variable (i.e.

deformation of the mattress surface) is not linearly related to lung volume. Therefore,

accurately detecting lung volume may not be possible, which makes the technology unable

to detect complex respiratory disorders like paradoxical breathing.

This feasibility study has some major limitations. Only 1 healthy human subject was

used to simulate 6 different body types by using weights and clothing, which is not very

realistic. Furthermore, no diverse patient population was used. No long-term evaluation

was performed; ideally, an overnight study would help show that the Respiration Pad is

capable of continuous, long-term measurement of respiratory rate. Also, no evaluation

of the effect of motion and breathing artefacts on the measured signal was performed.

Finally, no evaluation of the effect of patient posture, orientation, or position was explored

in this study.

Some of these limitations can be addressed first by further improving the technology then

performing a proper feasibility study. The signal quality can be improved by optimizing the

electrode geometry through a computation model. Other improvements include, adding

shielding to allow the sensor to work on a spring mattress, as well as, adding multiple

channels to allow detecting paradoxical breathing. Finally, a proper feasibility study can

be performed by repeating the study with a diverse patient population; performing longer


breathing captures; investigating the effect of motion and breathing artefacts on signal

quality; analyzing the effect of patient posture, position and orientation on measurement

accuracy; and investigating the sensitivity and specificity of detection of paradoxical

breathing using multiple channels.

6.5 Summary

This chapter describes a major aspect of this work, which was to validate the feasibility

of this technology. The aim was to validate the hypothesis that the Respiration Pad can

produce measurements that agree with those from an established method sufficiently well

for clinical purposes.

A feasibility study was conducted using a single human subject with an airflow meter as

the clinical reference method. The results showed that the Respiration Pad is an accurate

and unbiased method for respiratory rate measurement. Accurate means that there is

strong agreement with the clinical reference method with an average ICC value of 0.99

and an average BSI coefficient of 2.76 < 4 breaths/min clinical threshold. Unbiased means

that the mean difference between the two methods is -0.02 breaths/min on average.

However, the feasibility study also revealed many of the technology’s limitations. The

Respiration Pad cannot currently measure tidal volume or be used to detect complex

breathing disorders such as paradoxical breathing, and it may not be possible to accurately

do so. Furthermore, the measured signal is extremely small and is prone to misreadings.

Finally, the technology cannot currently be used on spring mattresses and may not work

in the presence of metal from other instruments, wires, or clothing. Therefore, even

though the feasibility study has shown that the Respiration Pad can accurately measure

respiratory rate even at shallow tidal volumes, the technology may not be useful for more

complex respiratory measurements.

Chapter 7

Conclusion

A contactless planar capacitive sensor was designed and developed. A feasibility study

was used to evaluate the performance of the sensor against an airflow meter using a single

person. Results indicate that the Respiration Pad is useful for simple measurement of

respiratory rate and that the measurements are unbiased and accurate. However, the

study also revealed that due to limitations of the technology, the Respiration Pad is not

suitable for measurement of tidal volume or complex breathing disorders. Ultimately, the

goal of this work was to create a technology that would potentially improve respiratory

rate documentation within hospitals. Despite the limitations, results indicate that the

Respiration Pad is adequate for this purpose.

7.1 Original contributions

This work has a substantial engineering component with a small clinical component in

the form of a feasibility study to evaluate the technology. The original contributions of

this work are described as follows:

1. Computational model: Showed that the Respiration Pad works by capacitive

deformation sensing using a computational model.

2. Technology redesign: Significantly improved the durability, portability, safety,

and enabled real-time operation of the technology by testing new materials for the

sensor head, and developing the electronics and software from scratch.

3. Safety assessment: Ensured Respiration Pad was safe for patient use by assessing

42

Chapter 7. Conclusion 43

the technology for both electromagnetic exposure safety using Health Canada’s Safety

Code 6 and electrical safety through certification to CSA standard. Furthermore,

the device received medical device assessment as a class 2 medical device, which is

required before the technology can be evaluated on patients.

4. Feasibility study: Showed that the Respiration Pad is accurate (i.e. strong

agreement with clinical reference method with an average ICC value of 0.99 and

an average BSI coefficient of 2.76 < 4 breaths/min clinical threshold) and unbiased

(i.e. average mean difference of -0.02 breaths/min) by conducting a feasibility study

using a human subject with an airflow meter as the clinical reference method.

7.2 Limitations of work

Due to large scope of this project and the difficult engineering challenges in the development

of the technology, several limitations in this work exist. These limitations include:

1. Sensor optimization: No optimization of the electrode geometry using the com-

putational model was performed.

2. Feasibility study: Only 1 health human subject was used to simulate 6 different

body types by using weights and clothing, which is not very realistic. No long-term

evaluation was performed; ideally, an overnight study would help to show that the

Respiration Pad is capable of continuous, long-term measurement of respiratory

rate. Finally, no evaluation of the effect of motion and breathing artefacts, patient

posture, orientation, and position on the measured signal was explored.

7.3 Limitations of technology

Although the results of this work are positive and show that the Respiration Pad is

capable of accurate and unbiased measurements of respiratory rate, several key limitations

of the technology have been identified. These limitations severely limit the potential of

this technology for clinical use. The limitations of the technology include:

1. Tidal volume: Measurement of tidal volume is required to characterize a patient’s

ventilation status, which is a better indicator of a patient’s physiological condition


than simply respiratory rate alone. Since the Respiration Pad does not currently

have the capability to measure tidal volume, it’s clinical utility is limited.

2. Qualitative and non-linear: RIP is a quantitative method for respiratory mea-

surement because it measures changes in cross-sectional area of the chest, which is

linearly related to lung volume. In contrast, the Respiration Pad measures capacitive

deformation of the mattress surface, which is non-linearly related to lung volume

and therefore cannot be considered quantitative. Furthermore, the Respiration

Pad is sensitive to not only mattress elasticity, patient clothing, and patient size

but potentially also to patient orientation, posture, position, and motion artefacts.

Therefore, it will be difficult to maintain a calibrated state over time.

3. Misreadings: The measured capacitance values are extremely small and, therefore,

sometimes electromagnetic interference, noise, or motion artefacts in the signal can

cause misreadings to occur. Furthermore, results showed that the signal becomes

corrupted in the presence of metal, which makes the Respiration Pad unusable on a

spring mattress. It may be possible to overcome this using shielding to remove the

effect of metal in the spring mattress; however, this does solve the problem when

metal is present above the Respiration Pad near the patient area such as in patient

clothing or on the mattress itself due to electrodes and wires from other devices.

4. Complex breathing: The Respiration Pad has only 1 measurement channel,

which means its use in sleep apnea monitoring and detecting complex breathing

disorders is limited. For example, paradoxical breathing, which occurs in obstructive

sleep apnea patients requires two independent channels measuring signals of both

the chest and abdominal walls for proper assessment. Furthermore, sleep apnea

monitoring requires a more complete picture of ventilation status, which includes

tidal volume measurement. Since the Respiration Pad may not have the ability to

reliably and accurately measure tidal volume, maintain a calibrated state, and avoid

misreadings, it will be of very little clinical use in monitoring complex breathing

disorders including sleep apnea.

7.4 Directions for future work

With these limitations in mind, the clinical usefulness of this technology beyond simple

respiratory rate monitoring is limited. However, if respiratory rate measurement alone is


sufficient, then there is potential for improvement in this area. Directions for future work

include:

1. Technology improvement: Signal quality may be improved by optimizing the

electrode geometry through a computation model. Other improvements include

upgrading the wireless transmitter to Bluetooth low energy to reduce size and power

consumption, adding shielding to allow the sensor to work on a spring mattress,

and adding multiple channels to potentially allow detecting more complex breathing

disorders such as paradoxical breathing and sleep apnea.

2. Clinical pilot: Address limitations of feasibility study by repeating the study with

a diverse patient population, performing longer breathing captures, investigating

the effect of motion and breathing artefacts on signal quality, and analyzing the

effect of patient posture, position and orientation on accuracy.

3. Sleep quality screening: There is potential for sleep quality assessment in the

home by measuring proxy variables such as restlessness during sleep, bed-exit events,

and respiratory variability during sleep.

Overall, this work was a major step in pushing the Respiration Pad forward. The

conclusion is that the Respiration Pad has promise for simple respiratory rate monitoring

to improve vital signs documentation in hospitals and has the sensitivity to accurately

measure respiratory rate even during shallow breathing.

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Design and Development of a Contactless Planar Capacitive ... · Design and Development of a Contactless Planar Capacitive Sensor Thuvatahan Sivayogan ... who lives in the hearts

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