Adaptive Filtering for Detecting Myocardial Infarction Using Noninvasive Conducting Polymer Composite Sensors

ADAPTIVE FILTERING FOR DETECTING MYOCARDIAL INFARCTION

USING NONINVASIVE CONDUCTING POLYMER COMPOSITE SENSORS

Berney Montavon(1)(a)

, Mehmet Ergezer , Paul Lozovyy , Arun Venkatesan , Daniel Simon (2) (1) (3) (1)

(1)Cleveland State University, 2121 Euclid Ave, Cleveland, Ohio 44115

(2)Arcon Corporation, 260 Bear Hill Road, Waltham, MA 02451

(3)Cleveland Medical Polymers Inc, 1030 West Smith Road, Medina, OH 44256

(a)[email protected]

ABSTRACT Continuous electrocardiographic (ECG) monitoring using

conducting polymer composite sensors (CPS) presents a

non-invasive way to detect cardiac irregularities such as

myocardial infarction (MI). Electromyography (EMG),

which measures muscle activity in the human body, has a

frequency range that overlaps that of the ECG wave. As a

result, both EMG and ECG data are present when CPSs

collect ECG signals. When measuring ECG waves of an

individual during motion, we account for EMG by

removing the motion artifact from the ECG signal. With

the use of a normalized least mean square (NLMS)

algorithm and known signal characteristics, we show that

EMG noise can be successfully filtered from an ECG

signal that is collected using our CPSs in the standard 12

lead ECG placement. Our software produces a

diagnostic-friendly ECG signal and then determines the

patient’s heart rate. When applied to the arrhythmia

database from the Massachusetts Institute of Technology

and Beth Israel Hospital (MIT-BIH), our heartbeat

detection logic has an accuracy of 99.6% with only 199

false beats and 240 missed beats out of 109,494 total

heartbeats taken from 48 individual recordings.

KEY WORDS

Signal processing of physiological signals, wearable

devices, biomedical signal processing, medical signal

processing.

1. Introduction

Myocardial infarction (MI), which is a disruption of blood

flow to the heart, is the leading cause of death among

firefighters in the United States [1]. The need to carry

heavy equipment long distances is a type of anaerobic

exercise capable of pushing heart-rate to 80-90%

maximum capacity [2]. As first responders providing an

essential service to society, it is important to develop tools

to allow firefighters to do their job and to keep them safe.

Our primary focus in this research is to develop a non-

invasive electrocardiographic (ECG) monitoring system

that can be used to automatically detect potential cardiac

emergencies in firefighters on the job.

Electrocardiography is the measurement of

electrical activity across the heart. The depolarization of

the heart reduces a small electrical charge that we

measure using surface electrodes attached to the skin.

The placement of the electrodes is a standard

configuration that collects signals from 12 different

angles to the heart. The recorded signals are visible in

waveform through the use of an ECG amplifier. The

basic components of the ECG waveform are the P wave,

QRS segment, and T wave. We also refer to the gap

between the S wave and the T wave as the ST-segment

[3]. Doctors use the ECG to diagnose abnormal heart

activity. Irregular ECG waveforms, such as an elevated

ST-segment, could be an indicator of MI or other ailments

[4]. MI is commonly referred to as a heart attack, and is

caused by an interruption of blood flow through the heart.

Our goal is that through non-invasive monitoring of ECG

waves, we can accurately detect MI and other heart

arrhythmias in workers of occupations where heart

activity is strenuous. We discuss this further in Section

2.1.

Although the human body is an overall good

conductor of electricity, the outer layer of skin has high

impedance that can limit the ability to measure ECG

waves. Conventional methods call for the need to remove

or penetrate this superficial layer of skin, and to use

conductive gels when attaching the leads to the skin [5].

This improves strength of ECG signals, but it is a

somewhat invasive process that is typically performed by

a medical professional. In recent years however, more

use has been made of advanced materials like CPSs with

carbon nanotube (CNT) additives to measure ECG

signals. The unique properties of the CPSs include the

ability to measure the electrical impulses of the heart. We

have developed an athletic shirt with embedded CPS

sensors arranged in the standard 12 lead layout. We use

the shirt to measure ECG signals without the need to

prepare the skin in any way. We discuss the data

collection further in Section 2.

We find that the primary challenge in developing

our monitoring system is the filtering of noise embedded

within the ECG signal [6]. In Section 3, we first apply a

NLMS algorithm, similar to those developed by Rahman

[7] Mandic [8] and Valin [9], to a sine wave embedded

with random noise as a testing benchmark. We then filter

out electromyography (EMG) noise from a CPS recorded

Proceedings of the IASTED International SymposiaImaging and Signal Processing in Health Care and Technology (ISPHT 2012)May 14 - 16, 2012 Baltimore, USA

DOI: 10.2316/P.2012.771-023 143

ECG signal. EMG is the measurement of electrical

impulses across muscle cells and EMG waves can be

visible in many ECG signals due to the fact that they have

overlapping frequency ranges [10]. As a result, we are

careful when removing the EMG noise so that we do not

eliminate vital components of the ECG signal [11].

Thakor and Zhu introduce an EMG algorithm that we

have added to our filter bank which reduces the EMG

noise [12]. The normalized least mean square (NLMS)

minimizes the difference between the desired ECG signal,

which is an ECG with little or no EMG noise, and the

actual ECG signal recorded with the CPS sensors, which

has embedded EMG signals.

In Section 4, we take the first step in detecting

any arrhythmia by establishing a baseline heartbeat. Pan

and Tompkins accomplished this by isolating the QRS

sequence in the ECG waveform, and then applying peak

detection logic [13]. The QRS sequence is also useful in

measuring R-R intervals and ST-segment length, both of

which will be used to automatically detect MI. We then

apply an original peak detection logic which counts the

number of heartbeats in a given signal. The algorithm is

tested through the use of the PhysioNet online library

[14], which houses a collection of various ECG

arrhythmia databases and provides a good testing

platform for our code.

2. Detecting Myocardial Infarction

The estimated parameters of the ECG allow us to detect

MI [15]. For instance, a Q-wave that is more than 1/4 the

size of the S-wave could be an indication of a possible MI

[5]. It may be difficult to measure the difference between

the two waves for a person in motion because of motion

artifact, but the estimated difference can still be included

in our detection algorithm as supplementary logic. A

more realistic approach is the one mentioned in the

introduction, ST elevation. The American Heart

Association defines ST elevation as being “0.1 mV in at

least two contiguous precordial or adjacent limb leads”

[16]. We also know that the ST segment should be

approximately 80-120 ms in duration. Using this fact, our

detection algorithm will monitor ST lengths and flag

potential abnormalities. Arrhythmias other than MI, such

as tachycardia and bradycardia will also be monitored

using the heartbeat detection software described in

Section 4.

2.1 Data Collection

We collect data using our CPS embedded athletic shirt

and a Texas Instruments Incorporated ADS1298™ ECG

amplifier (Figure 1). Our current development

configuration is for prototyping, and requires a hard-wired

connection to the shirt. Further research will use a

wireless data transmission setup which will be necessary

for most practical applications.

The amplifier collects data at a sampling rate that

can be configured to either 250 or 500 samples per

second. We configure it to collect data at 250 Hz. After

we collect data, we employ MATLAB® for all signal

processing and filter application. CPS properties can vary

immensely depending on the alignment. With the proper

formulation and processing, we are able to produce CNT

based sensors that can detect and measure the voltage

differential across the surface of the heart.

We have effectively incorporated CNT’s and

other additives into a polymer matrix to make a

proprietary material. These conducting polymer

composites are then processed in bulk quantities to

manufacture the sensors, and are robust enough to be

attached to fabric or other support materials to make user-

transparent sensor assemblies. The CPS’s are dry contact

sensors that require no conductive gel or skin preparation

for ECG capture. The CPS embedded athletic shirt is one

such assembly to enable real-life biotelemetry. Figure 1 - CPS embedded athletic shirt along with the Texas Instruments

Incorporated ADS1298™ ECG amplifier.

Our athletic shirt consists of 10 CPS electrodes. Four of

them are placed on the left arm (LA), right arm (RA), left

leg (LL), and the right leg (RL). The shirt also contains

the 6 precordial electrodes (V1 to V6) that are placed on

the chest and left side of the torso [5]. The RL electrode

is used as a reference point.

At this point, it is important to distinguish the

difference between a lead and an electrode. The

electrodes are the actual CPS sensors attached to our shirt.

The leads represent ECG recordings taken from different

angles to the heart. The standard 12 lead configuration (6

precordial, 3 limb, and 3 augmented) gets its name from

the total number of different ECG recordings that are

available with these 10 electrodes. We start with the

calculation of the three limb leads: lead I, lead II, and lead

III [10]. The limb leads are calculated using the

following equations:

(1)

(2)

(3)

144

Next, we calculate the three augmented leads. The

augmented leads are derived using the same electrodes

used to calculate leads I, II, and III. We find the

precordial leads do not require augmentation due to their

close proximity to the heart [10].

(4)

(5)

(6)

The output data of the ADS1298 are the precordial leads

as well as leads I and II, giving us only 8 of the 12 leads,

but our code computes the missing limb lead III and the 3

augmented leads using the LA, RA and LL electrodes and

Eq. 3-6..

3. Elimination of Motion Artifact

The removal of non-ECG data from our recorded signal is

the primary obstacle in our research. Body motion will

corrupt our ECG signal with noise [17], but so will

respiration and muscle flexing even while sitting still

[18]. Our approach is to create a bank of filters that will

address each type of noise we encounter [19]. Since

baseline wander is common even in conventional ECG

data collection, this is where we begin the filtering

process.

3.1 Band Pass Filtering

Baseline wander is a common ECG effect which is often

caused by respiration [10]. As our research focuses on

data collection from active individuals, it is a type of

noise that is present in almost every signal we encounter.

We apply a band pass filter to focus on the frequency

spectra of ECG signals. The standard range for adults is

0.67 to 150 Hz [10]. By eliminating the frequencies

outside this range through the use of a 4th

order bandpass

Butterworth filter, we are able to successfully reduce the

impact of baseline wander.

3.2 Normalized Least Mean Square Algorithm

The intent of the NLMS algorithm is to limit the mean

square error of the input signal. That is, it seeks to

minimize the difference between the noisy signal and the

real but unknown ECG signal. We do this by finding the

minimized value of the square of the estimated ECG

signal, s(k). We begin with Eq. 7 below and see that the

noisy signal embedded in the ECG, is equal to the

product of the reference inputs, a(k), and the error

coefficients, w(k). The T superscript indicates that we are

taking the transpose.

(7)

In our research, accelerometer data or estimated EMG

measurements using augmented leads act as our reference

inputs, as discussed further in the subsequent sections.

The error coefficients are important, because correctly

estimating their values means we can find the noise

infecting our ECG. We use Eq. 8 below to adaptively

update the estimate of the error coefficients, w(k). Note

that with some abuse of notation we have used w(k) to

indicate both the true coefficients in Eq. 7 and the

estimated coefficients in Eq. 8. The adaptive step size,

u(k), is calculated in Eq. 9 and μ is an input variable that

we vary from 0.1 to 0.001. We find that a larger step size

decreases the convergence time it takes the algorithm to

find the correct coefficients, but at the cost of overall

accuracy.

= w + u(k) (8)

=

(9)

The estimated ECG, s(k), can now be calculated

using Eq. 10 below. We simply subtract the estimated

noise, n(k), from the CPS recorded ECG signal, d(k).

= d n(k) (10)

We test our algorithm by applying uniformly distributed

noise to a sine wave. In this test, we use a step size of

0.01 and we use a signal-to-noise ratio of 0.5. The results

of the test are in Figure 2. The first plot in the figure

shows the learning rate of NLMS as the algorithm learns

the error coefficients of the noise that we are adding to the

sine wave. The second plot, which simulates a noisy ECG

signal, is the noisy sine wave. The final plot is the NLMS

filtered signal. The error coefficients begin to level out at

approximately 3.5 seconds indicating that they have

converged to the correct values. It is clear that by the 10

second mark, the filter is correctly estimating most of the

randomly generated noise and recovering the clean sine

wave.

Figure 2 - Results of filtering a noisy sine wave using the NLMS algorithm. The top figure shows the convergence of one of the NLMS

weight coefficients. The middle figure is the noisy sine wave as input to

the filter, and the bottom figure is the filtered sine wave.

0 1 2 3 4 5 6 7 8 9 100

1

2

3

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0

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0

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Time (seconds)

Magnitude (

voltage)

145

3.3 EMG Cancellation sing Augmented Leads u

Seeing that our electrodes are placed at very specific

locations, we can gather EMG data using leads that are

orthogonal to guarantee uncorrelated inputs to the NLMS

filter [12]. We also know that the difference between

leads aVR and aVL is orthogonal to lead aVF. We then

use Eqs. 4, 5, and 6 to calculate the augmented leads and

input them into the NLMS filter. Lead aVF is the input

signal and (aVR – aVL) is used as the reference input.

We conduct a test where the subject is wearing

the CPS sensor shirt while ECG data is being collected.

Figures 3-5 shows the results of this test. The subject

flexes both arms and chest to produce EMG noise at the

30-35 second mark and this interruption is clearly visible

in the raw data collected from leads I and II in Fig. 3.

Fig. 4 plots the input signal (aVF) and the reference signal

(aVR aVL). Finally, Fig. 5 shows the results of the

NLMS filtered signals with noticeable improvement

compared to the noisy ECG signals input into the

algorithm. For this test, our step size was 0.1.

Figure 3 – ECG signals as collected with the CPS embedded shirt. Note the baseline wandering after the 30 second mark due to a muscle flex.

Figure 4 – Calculated augmented leads that are

input into the NLMS filter.

Figure 5 - Results of EMG cancellation with lead

aVF as the input into the NLMS filter.

3.4 Using Accelerometers to Reduce Motion Artifact

Another approach that we apply is the use of

accelerometers to capture body motion that corrupts the

ECG signals we record. By measuring body acceleration

from arm movement during light exercise, we attempt to

remove the motion artifact from our ECG signal by

inputting the accelerometer data into our NLMS filter as

the reference signal.

The accelerometer was mounted on the upper

back for convenience. Tests were also performed with the

accelerometer mounted on the lower back, but the

location of the accelerometer did not make any significant

difference. At first, we selected a 3-axis analog

accelerometer with a range of . While running tests

with this device, we ran into a problem of not having

enough resolution as the measured acceleration signal

lacked precision at lower accelerations. We then

switched to a 3-axis accelerometer that performed

well for tests involving casual walking. If we experiment

with jumping or running up and down stairwells (which

are tasks commonly asked of firefighters), the

accelerometer might saturate at its 1.5g limits.

Accelerometer data was recorded at 100 samples per

second, so it had to be interpolated to match the ECG data

sampling rate of 250 samples per second.

4. Heartbeat Detection

Using the procedure outlined by Pan and Tompkins, we

begin the process of detecting the heartbeats by isolating

the QRS sequence [13]. By counting the number of QRS

sequences in a signal, we can determine the number of

heartbeats. A normal QRS sequence has a frequency

range of 0 to 40 Hz, but in our testing, accuracy increases

with a bandpass filter with a range of 5 to 90 Hz. After

filtering, we differentiate our signal. The differentiating

of the ECG signal increases the magnitude of the QRS

sequence. We then square every data point in the signal

which eliminates all the negative portions of the

waveform. The final step in QRS isolation is performing

moving-window integration. Our window size is 150 ms

and moves down the entire length of the signal combining

the areas under the curve and results in a waveform where

we can now detect local maximum values to look for

0 10 20 30 40 50 600.055

0.06

0.065

Vo

ltag

e (

mV

)

Lead I

0 10 20 30 40 50 60

0.009

0.01

0.011

0.012

Time (s)

Lead II

0 10 20 30 40 50 60-0.024

-0.022

-0.02

Input Lead - aVF

0 10 20 30 40 50 60-0.1

-0.09

-0.08

Time (s)

Vo

ltag

e (

mV

)

Reference Lead (aVR-aVL)

0 10 20 30 40 50 60-1

-0.5

0

0.5

1x 10

-3

Time (s)

Voltage (

mV

)

NLMS Filtered Signal

146

heartbeats. The plots in Figure 6 show each phase of the

process. Figure 6 – The original ECG wave is differentiated, squared and

integrated when isolating the QRS sequence.

4.1 Peak Detection and Decision Logic

Our implemented algorithm for peak detection is a

variation of Pan and Tompkins [13] and starts by

searching the waveform for slope changes. Any point

where the slope changes sign, that location is collected as

a possible peak. Many of the false peaks are eliminated

immediately by setting a minimum threshold which we

set equal to 60% of the mean value of our integrated

waveform. Due to the limits of our ADS1298 interface

with MATLAB, our initial code is developed to operate

with prerecorded signals and will need to be modified

when we implement real time applications. With

elimination of motion artifacts being our primary focus,

our non-real-time method meets our needs in these early

stages of our research.

We then define a gap equal to 0.3 seconds

because we know that it is not physiologically possible

for a heart to beat more than once in such a short time

period [10]. As a result, more false peaks are eliminated

if they appear within 0.3 seconds of the prior or

subsequent detection.

4.2 Heartbeat Detection Results

PhysioNet is commonly used by biomedical researchers.

The website has a large library of physiological signals

published under the GNU General Public License (GPL).

One of the databases on PhysioNet is the MIT-BIH. This

database includes the ECG records of 48 patients which

were annotated by cardiologists to identify both the

number of heartbeats and the types of arrhythmias present

in the signals [14]. These databases are widely used as

benchmarks when performing tests on any sort of

automated heartbeat counter or arrhythmia detection

algorithm [20].

We download the entire record of each patient

(approximately 30 minutes of data exists in each record)

and run the signals through our heartbeat detection

algorithm. The MIT-BIH has a total of 109,494

heartbeats with records that include normal healthy beats

and abnormal beats interwoven throughout each record.

Our algorithm detected 109,254 of the heartbeats present

and falsely detected 199 heartbeats, 170 of which were

detected from one record that shows extended periods of

ventricular flutter in which no normal beats are actually

counted. Our results for this process are detailed in

Table 1. We note that 41 of the 48 records had a variation

of less than 10 heartbeats, and only 2 of the records had a

variance that exceeded 23 heartbeats (records 203 and

207). Annotations in the MIT-BIH database note that

record 203 has significant muscle artifact and baseline

shifts which makes it a difficult record for humans to read

properly. There are also extended periods of ventricular

flutter in record 207, which are not considered heartbeats

by cardiologists, which causes the high number of false

heartbeats.

5. Conclusion

The goal of this research is to detect MI in firefighters

using a non-intrusive method. To achieve this, a CPS-

embedded athletic shirt is employed to collect ECG data.

The collected noisy ECG signal is filtered using an

NLMS algorithm, and then a novel heartbeat detection

algorithm is introduced to search for irregular heartbeats. Our research shows that noises such as EMG and motion

artifact can be successfully filtered from an ECG signal.

The NLMS filter is first tested with a sine wave that has

five to one signal to noise ratio and is found to perform

well after ten seconds. Once the filter’s performance is

shown on the sine wave, the NLMS filter was tested on

noisy data ECG data collected from a patient. The tests

show that the filter successfully removes baseline wander

and EMG noise. We use the augmented leads of aVF,

aVL, and aVR as inputs to the NLMS filter which results

in an ECG waveform that can then be input to our

heartbeat detection algorithm for future arrhythmia

detection algorithms.

The heartbeat detection algorithm proved to be

very accurate by correctly detecting 99.6% of the

heartbeats in the MIT-BIH database. We see a deviation

of less than nine beats in 41 out of 48 records. This

shows that we can accurately detect heart rate with a very

small percentage of errors in subjects with both normal

and abnormal heart rhythms.

While we’ve had some success at eliminating the

EMG noise embedded in ECG signals, future work is

needed in removing motion artifact through the use of

multiple accelerometers. By placing accelerometers on

both arms in addition to the back, we can better detect the

noise present in an ECG that results from arm movement.

We found that the shirt assembly with its dry gel-free

CPS’s was very convenient for repetitive ECG testing,

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

1000

1200

Unfiltered ECG

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

-20

0

20

Derivative

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

500

1000

Square

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

100

200

Time (s)

Vo

lta

ge

(m

V)

Integration

147

with no skin irritation problems. While it provided an

expedient way to run tests, we also must address the issue

of electrode motion artifact which results from the CPS

electrodes not being attached to the body. More work is

also required in applying an adaptive threshold in our beat

detection algorithm. The algorithm works very well in

either high noise or low noise environments, but when

noise in the ECG is not consistent throughout the signal,

the accuracy lessens and we see greater error.

Acknowledgments

The work for this project was funded by the Cleveland

State University Provost’s Office and by the National

Science Foundation under Grant No. 0826124 and was

completed in the Embedded Control Systems Research

Lab at Cleveland State University located in Cleveland,

Ohio. Work was also done in partnership with the

Cleveland Clinic and Dr. Mirela Ovreiu.

Table 1 – Results from the 48 patients in the MIT-BIH database show an

overall success rate of 99.60% with only 240 missed beats and 199 false

beats.

Record

Number

Actual

Heartbeats

Detected

Heartbeats

False

Beats

Missed

Beats

100 2273 2272 0 1

101 1865 1866 1 0

102 2187 2187 0 0

103 2084 2083 0 1

104 2229 2235 6 0

105 2572 2575 3 0

106 2027 2018 0 9

107 2137 2136 0 1

108 1763 1765 2 0

109 2532 2529 0 3

111 2124 2126 2 0

112 2539 2540 1 0

113 1795 1793 0 2

114 1879 1882 3 0

115 1953 1952 0 1

116 2412 2389 0 23

117 1535 1536 1 0

118 2278 2279 1 0

119 1987 1988 1 0

121 1863 1863 0 0

122 2476 2476 0 0

123 1518 1517 0 1

124 1619 1619 0 0

200 2601 2602 1 0

201 1963 1956 0 7

202 2136 2132 0 4

203 2980 2888 0 92

205 2656 2642 0 14

207 1860 2030 170 0

208 2955 2934 0 21

209 3005 3003 0 2

210 2650 2633 0 17

212 2748 2748 0 0

213 3251 3245 0 6

214 2262 2259 0 3

215 3363 3359 0 4

217 2208 2207 0 1

219 2154 2154 0 0

220 2048 2047 0 1

221 2427 2425 0 2

222 2483 2469 0 14

223 2605 2602 0 3

228 2053 2057 4 0

230 2256 2256 0 0

231 1571 1570 0 1

232 1780 1783 3 0

233 3079 3074 0 5

234 2753 2752 0 1

Totals 109494 109453 199 240

148

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