Coherence Analysis between ECG and EEG Signals A DISSERTATION Submitted in partial fulfilment of the Requirements for the award of the degree Of MASTER OF TECHNOLOGY In CONTROL AND INSTRUMENTATION ENGINEERING By GAVENDRA SINGH (Regd. No. 09206106) Under the guidance of Dr DILBAG SINGH (Associate Professor)
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Coherence Analysis between ECG and EEG Signals
A DISSERTATION
Submitted in partial fulfilment of the
Requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
In
CONTROL AND INSTRUMENTATION ENGINEERING
By
GAVENDRA SINGH(Regd. No. 09206106)
Under the guidance of
Dr DILBAG SINGH(Associate Professor)
DEPARTMENT OF INSTRUMENTATION AND CONTROL ENGINEERING
Dr B R AMBEDKAR NATIONAL INSTITUTE OF TECHNOLOGY
JALANDHAR – 144011, PUNJAB (INDIA), JUNE 2011
CANDIDATE’S DECLARATION
I hereby declare that the work which is being presented in this dissertation entitled
“Coherence Analysis between ECG and EEG Signals ” submitted towards the partial
fulfilment of the requirements for the award of the degree of the Master of Technology in
Control and Instrumentation Engineering from Dr B R Ambedkar National Institute of
Technology Jalandhar, India, is an authentic record of my own work carried out from August
2010 to June 2011 under the supervision of Dr Dilbag Singh, Associate Professor,
Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute
of Technology Jalandhar.
This matter in this dissertation report has not been submitted by me for of any other
degree or diploma.
Place: NIT Jalandhar Gavendra Singh
Date: June 2011
CERTIFICATE
This is to certify that the above statement made by the candidate is correct to the best of my
knowledge.
Dr Dilbag Singh
(Associate Professor)
Department of ICE
NIT Jalandhar-144011
i
Dr B R AMBEDKAR NATIONAL INSTITUTE OF TECHNOLOGY
JALANDHAR, (PUNJAB)
CERTIFICATE
This is to certify that dissertation entitled
“Coherence Analysis between ECG and EEG Signals”
Submitted By
GAVENDRA SINGH(Regd. No. 09206106)
May be accepted for the partial fulfilment for award of
Master of Technology in “Control and Instrumentation Engineering”
Internal External HODExaminer Examiner Department of ICE
Date:
ii
“Dedicated to my mother, Smt Rajvala Devi and my Father, Mr Kanchhi Singh for their
continued Inspiration, Encouragement, Love and Support”
iii
ACKNOWLEDGEMENT
At this momentous occasion of completing my research I would like to acknowledge the
contribution of all those benevolent people, I have been blessed to associate with. All the data
collection, theories, models would have failed to serve their purpose for me if blessing of the
Almighty would not have joined hands with my efforts.
My first and foremost offering of thanks goes to the architect who shaped my dream into the
reality, my guide and mentor Dr Dilbag Singh, Associate Professor, Department of
Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of
Technology, Jalandhar. Perseverance, exuberance, positive approaches are just some of the
traits he has imprinted on my personality. He steered me through this journey with his
invaluable advice, positive criticism, stimulating discussions and consistent encouragement.
He took care to shine light of knowledge, when I was groping in the darkness of ignorance. If
I will stand proud of my achievements then undeniably he is the main creditor. It is my
privilege to be under his tutelage.
I express my sincere thanks to Dr A K Jain, Head, Department of
Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of
Technology, Jalandhar. He provided me continuous help and guidance to complete my
dissertation.
With a grateful heart, I acknowledge the noble and gentle hand of support lent to me by Mr
Buta Singh, Research Scholar, for his valuable guidance at every step and cooperation for
data collection and analysis.
When talking about cooperation and help to complete this work how can I go without the
name of my arch-batch met throughout my journey, Mr Varun Gupta for her valuable
suggestions, consistent encouragement and to keep my approaches positive and my senier Mr
Madhwendra Nath Tripathi for his good help.
iv
Dated: June 2011 GAVENDRA SINGH
v
List of Tables and Figures
List of Tables and Figure
Table 1.1: Rhythmic brain activity
Table 1.2: Average respiratory rates, by age
Table 2.1: Experimental Hardware Setup
Table 2.2: Specification of data acquisition unit Biopac Inc. MP100
Table 2.3: ECG100C Specifications
Table 2.4: EEG100C Specifications
Table 2.5: RSP100C Specifications
Table 4.1: Different parameters of the acquired signals
Table 4.2: Signals Acquisition Settings
Table 4.3: Coherence analysis of results at different respiratory rates
Table 4.4: ECG and EEG signals from 1 to 25 subjects statistics
Table 4.5: ECG and EEG signals from 26 to 50 subjects statistics
Table 5.1: Coherence and phase coherence measure parameters for first subject
Table 5.2: Coherence and phase coherence measure parameters for second subject
Table 5.3: Coherence and phase coherence measure parameters for third subject
Table A.1: Lead Type Length Usage Note
Table A.2: TSD201 Specifications
Fig. 1.1: The Human Heart with Coronary Arteries
Fig. 1.2: Heart Valves
Fig. 1.3: Cardiac Conduction System
Fig. 1.4: The lobes and sulci of the cerebrum.
Fig. 1.5: Functional areas of the cerebrum
Fig. 1.6: Rhythmic brain activity
Fig. 1.7: Willem Einthoven, The string galvanometer that he invented in 1903.
Fig. 1.8: Experimental Setup of 12 Lead ECG Acquisition from Atria 6100
Fig. 2.1(a): Block Diagram of Multi-channel Data Acquisition System Biopac Inc. MP100
Fig. 2.1(b): Hardware Components of Multi-channel Data Acquisition System MP100
Fig. 2.2: Graph of Experimental Data Acquired using MP100 and Acqknowledge3.9.0
Fig. 2.3: Snap of Subject and Technician during Data Acquisition
Fig. 2.4: The electrode connections to the ECG100C for the measurement of Lead I
time for the three leads and then the recording will switch to the next column which will
record the heart beats after that point. It is possible for the heart rhythm to change between
the columns of leads.
Figure 1.8: Experimental Setup of 12 Lead ECG Acquisitions from Atria 6100
1.4.2 History of Electrocardiogram (EEG):
A timeline of the history of EEG is given by Swartz. Richard Caton (1842–1926), a
physician practicing in Liverpool, presented his findings about electrical phenomena of the
exposed cerebral hemispheres of rabbits and monkeys in the British Medical Journal in 1875.
In 1890, Polish physiologist Adolf Beck published an investigation of spontaneous
electrical activity of the brain of rabbits and dogs that included rhythmic oscillations altered
by light.
In 1912, Russian physiologist, Vladimir Vladimirovich Pravdich-Neminsky published
the first animal EEG and the evoked potential of the mammalian (dog). In 1914, Napoleon
Cybulskiand Jelenska-Macieszyna photographed EEG-recordings of experimentally induced
seizures.
German physiologist and psychiatrist Hans Berger (1873–1941) recorded the first human
EEG in 1924.
The acquisition of EEG is described in the next chapter 2 (Physiological data
acquisition).
1.4.3 Work done so far in Coherence analysis of Physiological Signals:1.4.3.a A Study of Heart Rate and Brain System Complexity and Their Interaction in Sleep-
Deprived Subjects by AK Kokonozi, EM Michail, IC Chouvarda, and NM Maglaveras
Progressive Alterations in Corticomuscular Coupling by Qi Yang, Vlodek Siemionow,
Wanxiang Yao, Vinod Sahgal, and Guang H. Yue
In 2010, voluntary muscle fatigue is a progressive process. A recent study
demonstrated muscle fatigue-induced weakening of functional corticomuscular coupling
measured by coherence between the brain [electroencephalogram (EEG)] and muscle
[electromyogram (EMG)] signals after a relatively long-duration muscle contraction.
Comparing the EEG-EMG coherence before versus after fatigue or between data of two long-
duration time blocks is not adequate to reveal the dynamic nature of the fatigue process. The
purpose of this study was to address this issue by quantifying single-trial EEG-EMG
coherence and EEG, EMG power based on wavelet transform. The energy of both the EEG
and EMG signals decreased significantly with muscle fatigue. This provides extra
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Abstract
information to demonstrate a time course of dynamic adaptations of the functional
corticomuscular coupling, as well as brain and muscle signals during muscle fatigue [9].
1.4.3.e On the Recording Reference Contribution to EEG Correlation, Phase Synchorony,
and Coherence by Sanqing Hu, Matt Stead, Qionghai Dai, and Gregory A. Worrell
The degree of synchronization in electroencephalography (EEG) signals is commonly
characterized by the time-series measures, namely, correlation, phase synchrony, and
magnitude squared coherence (MSC). However, it is now well established that the
interpretation of the results from these measures are confounded by the recording reference
signal and that this problem is not mitigated by the use of other EEG montages, such as
bipolar and average reference. In this paper, we analyze the impact of reference signal
amplitude and power on EEG signal correlation, phase synchrony, and MSC. We show that,
first, when two nonreferential signals have negative correlation, the phase synchrony and the
absolute value of the correlation of the two referential signals may have two regions of
behaviour characterized by a monotonic decrease to zero and then a monotonic increase to
one as the amplitude of the reference signal varies in ¿. It is notable that even a small change
of the amplitude may lead to significant impact on these two measures. Second, when two
nonreferential signals have positive correlation, the correlation and phase-synchrony values
of the two referential signals can monotonically increase to one (or monotonically decrease to
some positive value and then monotonically increase to one) as the amplitude of the reference
signal varies in ¿. Third, when two nonreferential signals have negative cross-power, the
MSC of the two referential signals can monotonically decrease to zero and then
monotonically increase to one as reference signal power varies in¿. Fourth, when two
nonreferential signals have positive cross-power, the MSC of the two referential signals can
monotonically increase to one as the reference signal power varies in¿. In general, the
reference signal with small amplitude or power relative to the signals of interest may decrease
or increase the values of correlation, phase synchrony, and MSC.
However, the reference signal with high relative amplitude or power will always
increase each of the three measures. In our previous paper, we developed a method to identify
and extract the reference signal contribution to intracranial EEG (iEEG) recordings. In this
paper, we apply this approach to referential iEEG recorded from human subjects and directly
investigate the contribution of recording reference on correlation, phase synchrony, and
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Abstract
MSC. The experimental results demonstrate the significant impact that the recording
reference may have on these bivariate measures [2].
1.5. Objective of this Thesis Work:The main objectives of this thesis work are:
Acquisition of Electrocardiogram, Electroencephalogram and Respiratory signal
simultaneously.
Calculation of respiratory rate simultaneously with the ECG, EEG and Respiration
Analysis of coherence and phase coherence between the ECG and EEG signals
acquired from the different brain regions to investigate that which region of brain is
more functionally associated to the corresponding heart signal.
The study of heart and heart sound chosen for Thesis work due to following reasons:
According to the world health organisation (WHO), Indians are the much greater risk
of contracting heart diseases and brain disorders than other nationalities.
As per the recent report of THE TIMES OF INDIA, dated April 28, 2010, Dr. Pratap
C. Reddy, Founder Chairman, Appllo Hospital, Says, “Reports point to India
becoming the heart disease capital of the world, if we haven’t become it already. This
is a dubious distinction. ”
According to recent report of the Indian health ministry, 10 percent of adults suffer
from hypertension and the country is home to 25-30 millions diabetics. The number of
deaths from heart attack is projected to increase to two millions in 2010.
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Abstract
Chapter 2
PHYSIOLOGICAL DATA ACQUISITION
Physiological signals like ECG, EEG, Heart Rate, Air flow and respiration rate etc.
are measured by using Biopac Inc. MP100 System. The MP100 unit takes incoming signals
and converts them into digital signals that can be processed with your computer. Data
collection generally involves taking incoming signals (usually analog) and sending them to
the computer, where they are (a) displayed on the screen and (b) stored in the computer’s
memory (or on the hard disk). In this dissertation work, ECG, EEG and respiratory signals
are measured by using mainly MP100 system simultaneously. The ECG, EEG and respiratory
signals are recorded for different subject and for the same subject under different condition.
The physiological signals are acquired from 65 persons in the age group 19-36 years for this
study. The physiological signals are acquired in the software environment of Biopac
Acqknowledge3.9.0 on the computer that is connected through the USB data cable.
2.1 MP100 System:
2.1.1 Introduction
The MP100 system is computer based data acquisition system that perform many of
the same function as a chart recorder or other data viewing device, but is superior to such
device in that it transcends the physical limits commonly encountered (such as paper width or
speed).The MP data acquisition unit (MP100) is the heart of MP system. The MP unit takes
incoming signal and converts them into digital signal that can be processed with your
computer. MP system can be used for a wide range of application, including
ECG
EEG
EMG
EOG
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GSR
Evoked response
Plethsmography
Pulmonary Function etc.
Data collection generally involves taking incoming signal (usually analog) and
sending them to computer , where they are (a) displayed on the screen and (b)store on the
computer memory(or on the hard disk).These signals can then be stored for future
examination much as a word processor stores a document or a statistics program saves a data
file. Graphical and numerical representation of the data can also be produced for use for other
program.
The MP100 system offers USB –ready data acquisition and analysis. It record
multiple channels differing sample rates, at speed up to 70kHz .The system is designed to
satisfy the following Medical Safety Test Standards affiliated with IEC601-1:
1. Creep age and air Clearance
2. Dielectric Strength
3. Patient Leakage Current
2.1.2 MP100 Block Diagram
Figure 2.1(a): Block Diagram of Multi-channel Data Acquisition System Biopac Inc. MP100
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Abstract
Figure 2.1(b): Hardware Components of Multi-channel Data Acquisition System Biopac Inc. MP100
Table 2.1: Experimental Hardware Setup
Sr. No.
Acquisition Hardware Hardwares Name
1. Data acquisition unit MP100C2. Universal interface module UIM100C3. USB adapter USB1W(PC) or USB1M(Macintosh)4. Transformer AC100A5. Cables CBLSERA cable, CBLS100 cable set
2.1.2.a Signal source
Transducers are biopotential electrodes are the source of signal for the biopack
system. These sensors are systematically placed on the subject and connected to the signal
conditioning modules through connecting leads. Ag-Agcl disposable electrodes are used for
ECG recording.
2.1.2.b Signal conditioning modules
Signals from the sensor are given to signal conditioning module for amplification and
filtering. The MP100 system has 9 signal conditioning module for different physiological
variable. It has 3 ECG100c modules for ECG recording, two EEG100C module for ECG
recording, SKT100C is for temperature recording, and RSP100c module for respiration rate
measurement. Besides the above module it also has two general purpose amplifier modules
which can be used for displacement sensor or blood pressure measurement. No. of channel
can be extended up to 16.
2.1.2.c UIM100 C Universal Interfacing module
The UIM100C universal interfacing module is the interface between the MP100 and
external device. Typically the UIM100C is used to input preamplifier signal and digital
signal to the MP100 acquisition unit. Other signal connect to various signal condition
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Abstract
modules. The universal interface module is designed to serve as a general purpose interface
to most type of laboratory equipment. The UIM100C consist of sixteen 3.5mm miniphone
jack connector for analog input, to 3.5mm miniphone connector for analog output, and screw
terminals for the sixteen digital lines, external trigger and supply voltage.
The UIM100C allow access to sixteen analog input and two analog output on one
side, and sixteen digital i/o lines, and external triggers, and supply voltage on the other side.
The UIM100C is designed to compatible with a variety of different input device, including
the biopack series of signal conditioning amplifier.
Connection between UIM100C and the MP100 acquisition unit are made via two
cable one for analog signal and one digital signal. Use the 6 meters cables included with your
system to connect the UIM100C to the acquisition unit. UIM100C enables the main device to
communicate with other device. It control the polling and interrupt in the communication
[Appendix].
2.1.2.d MP100 data acquisition unit
MP100 system has and internal microprocessor to control the data acquisition and
communication with the computer. There are sixteen analog input channel, two analog output
channel , sixteen digital channel that can be used for either input or output , and an external
trigger input.
Table 2.2: Specification of data acquisition unit Biopac Inc. MP100
Sr. No
Parameters Corresponding Values
1. No. of Analog Channel 162. Input Voltage Range ±10V3. Accuracy ±0. 0034. A/D Resolution 16 Bits5. No. of Digital Channel 166. Output Voltage Range ±10V7. Output Derive Current ±5mA8. No. of Calculation Channel 16
The ECG100C will connect directly to any of BIOPAC Systems, Inc.’s series of Ag-
AgCl lead electrodes. Use two shielded electrodes (EL208S) for the signal inputs and one
unshielded electrode (EL258S) for the ground.
2.2.2 ECG100C Calibration
The ECG100C is factory set and does not require calibration.
Table 2.3: ECG100C Specifications
Sr. No. Parameters Corresponding Range values1. Gain 500, 1000, 2000, 5000 2. Output Selection Normal, R-wave indicator3. Output Range ±10V (analog)4. Frequency Response Low Pass
Filter35Hz, 150Hz
5. High Pass Filter 0.05Hz, 1.0Hz6. Notch Filter 50dB rejection @ 50/60Hz7. Noise Voltage 0.1µV rms – (0.05-35Hz)8. Signal Source Electrodes (three electrode leads required)9. Z (input) Differential 2MΩ10. Input Voltage Range Gain Vin (mV)
500 ±20 1000 ±10 2000 ±5 5000 ±2
11. Weight 350 grams12. Dimensions 4cm (wide) x 11cm (deep) x 19cm (high)
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Figure 2.4: The electrode connections to the ECG100C for the measurement of Lead I. Signals from this electrode montage can be used to calculate BPM and general-purpose ECG applications.
2.3 EEG100C – Electroencephalogram Amplifier Module The electroencephalogram amplifier module (EEG100C) is a single-channel, high-
gain, differential input, biopotential amplifier designed specifically for monitoring the
neuronal activity of the brain [Appendix].
2.3.1 Applications:
Conventional EEG (16 channel, unipolar or bipolar)
Sleep studies
Epilepsy investigations
Evoked responses
Tumor pathology studies
Cognition studies
The EEG100C will connect directly to any of BIOPAC Systems, Inc.’s series of Ag-
AgCl lead electrodes. Typically, EL503 electrodes are recommended for evoked response
measurements. Use two shielded electrodes (LEAD110S) for the signal inputs and one
unshielded electrode (LEAD110) for ground.
2.3.2 EEG100C Calibration
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Abstract
The EEG100C is factory set and does not require calibration. To confirm the accuracy
of the device, use the CBLCALC.
Table 2.4: EEG100C Specifications
Sr. No. Parameters Corresponding Range values1. Gain 500, 1000, 2000, 5000 2. Output Selection Normal, Alpha Wave indicator3. Output Range ±10V (analog)4. Frequency Response Low Pass
Filter35Hz, 100Hz
5. High Pass Filter 0.1Hz, 1.0 Hz6. Notch Filter 50dB rejection @ 50/60Hz7. Noise Voltage 0.1µV rms – (0.05-35Hz)8. Signal Source Electrodes (three electrode leads required)9. Z (input) Differential 2MΩ10. Input Voltage Range Gain Vin (mV)
5000 ±2 10000 ±1 20000 ±0.5 50000 ±0.2
11. Weight 350 grams12. Dimensions 4cm (wide) x 11cm (deep) x 19cm (high)
Figure 2.5(a): Bipolar EEG electrode leads placement, (b) International 10-20 electrode placement on the
The RSP100C respiration pneumogram amplifier module is a single channel,
differential amplifier designed specifically for recording respiration effort. The RSP100C is
designed for use in the following [Appendix].
2.4.1 Applications:
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Allergic responses analysis
Exercise physiology studies
Psychophysiological investigations
Respiration rate determination
Sleep studies
The RSP100C works with the TSD201 respiration transducer to measure abdominal
or thoracic expansion and contraction. The RSP100C includes a lower frequency response
selection switch that permits either absolute (DC) or relative (via a 0.05 high pass filter)
respiratory effort measurements. The following illustration shows the placement and
connections for recording thoracic respiration effort using the RSP100C and the TSD201
respiration transducer.
2.4.2 Frequency Response Characteristics
The 0.05Hz lower frequency response setting is a single pole roll-off filter. The 0.5Hz
lower frequency response setting is a two pole roll-off filter. Modules can be set for 50 or
60Hz notch options, depending on the destination country.
2.4.3 RSP100C Calibration
None required.
Table 2.5: RSP100C Specifications
Sr. No. Parameters Corresponding Range Values1. Gain 10, 20, 50, 1002. Output Selection Normal, Alpha Wave indicator3. Output Range ±10V (analog)4. Frequency Response: Low Pass Filter 1Hz, 10 Hz 5. Frequency Response: High Pass Filter 0.05 Hz, 0.5 Hz6. Notch Filter 50dB rejection @ 50/60Hz7. Noise Voltage 0.2µV rms – amplifier contribution 8. Signal Source TSD2019. Excitation Voltage ±0.5 V 10. Weight 350 grams11. Dimensions 4cm (wide) x 11cm (deep) x 19cm
(high)
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Figure 2.6: The placement and connections for recording thoracic respiration effort using the RSP100C and the TSD201 respiration transducer.
2.5. Various Functions of AcqKnowledge3.9.0 software:
(a) (b)
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(c)
(d) (d)
(e)
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(f) (g)
(h) (i)
(j) (k) (l)
Figure 2.7: (a) Transform tool bar, (b) Graph window function tool bar, (c) Acquisition set up, (d) On opening the new graph, graphic journal, (e) Setting channel label, (f) Set screen horizontal axis, (g) Set screen horizontal
For detail description goes to appendix (www.biopac.com).
Chapter 3
COHERENCE AND PHASE COHERENCE FUNCTION
3.1. IntroductionIt is necessary to be able to quantify the degree of interdependence of one process
upon another, or to establish the similarity between one set of data and another. Correlation
can be defined mathematically and can be quantify. Consider how to data sequences each
consisting of simultaneously sampled values taken from the two corresponding waveforms or
signals. If the two signals varied similarly point for point, then a measure of their correlation
might be taken by the sum of the products of the corresponding pairs of points [39].
3.1.1. Cross-correlation
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x
nj
Abstract
The Discrete cross-correlation r12 between two data sequences x1(n) and x2(n) each
containing N data might therefore be written as
r12=∑n=0
N−1
x1 (n ) x2 (n )(3.1)
The definition of cross-correlation, however produces a result whitch depends on the
number of sampling points taken. This is corrected for the normalizing the result to the
number of points by dividing by N . Alternatively this may be regarded as averaging the sum
of products. Thus an improved definition is
r12=1N ∑
n=0
N−1
x1 (n ) x2 (n )(3.2)
However, the signals are highly correlated, although they are out of phase.
Figure 3.1: One signal lags by j unit with the other signal
As illustrated in the above figure 3.1 this is equivalent to changing x2(n) to x2 (n+ j ) ,
where j represents the amount of lag which is the number of sampling points by which x2 has
been sifted to the left. An alternative, but equivalent, procedure is to sift x1 to the right. The
formula for the cross-correlation thus becomes
r12 ( j )= 1N ∑
n=0
N−1
x1 (n ) x2 (n+ j )(3.3)
r21 (− j )= 1N ∑
n=0
N−1
x2 (n ) x1 (n− j )(3.4)
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Of course, it is also possible to consider correlation in the continuous time domain,
and some analog signal correlation is implemented in this way, in the continuous domain
n → t and j→ τ and
r12 (τ )= limT →∞
1T ∫
−T /2
T /2
x1 (t ) x2(t+ τ )dt (3.5)
However, if x1 (t ) and x2 ( t ) are periodic with period T o the above equation 3.5 simplified to
r12 (τ )= limT →∞
1T o
∫−T o /2
To /2
x1 ( t ) x2(t +τ )dt(3.6)
If the signals are finite energy signals, for example non-periodic pulse-type signals,
then average evaluated over time T as T →∞ is not taken because 1/T → 0 and r12(τ ) is
always vanishingly small. For this case above equation 3.6 is used in the principle.
r12 (τ )=∫−∞
+∞
x1 ( t ) x2(t +τ )dt(3.7)
In practice, a finite record length will be processed and so equation 3.7 will be
applied:
r12 (τ )= 1T ∫
0
T
x1 (t ) x2(t+τ )dt (3.8)
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0 1000 2000 3000 4000 5000 6000-0.2
0
0.2A
mpl
itude
(V)
No. of samples of ECG Signal(a)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10
-3
No. of samples of EEG Signal(b)
Am
plitu
de(V
)
-80 -60 -40 -20 0 20 40 60 800.085
0.09
0.095
Lags b/w ECG and EEG(C3-C4)(c)
Cro
ss-C
orre
latio
n
Figure 3.2(a): ECG signal having 5006 samples with sampling rate 500 samples/sec (b) EEG Signal having 5006 samples with sampling rate 500 samples/sec (c) Cross-correlation between the ECG signal and the EEG
signal3.1.2. Autocorrelation
Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the
similarity between observations as a function of the time separation between them. It is a
mathematical tool for finding repeating patterns, such as the presence of a periodic signal
which has been buried under noise, or identifying the missing fundamental frequency in a
signal implied by its harmonic frequencies. It is often used in signal processing for analyzing
functions or series of values, such as time domain signals [38].
3.1.2.a Continuous Auto-Correlation:
The autocorrelation function gives an average measure of the time-domain properties
This is the average product of the signal, x1 (t ), and a time-shifted version of itself,
x1 (t +τ ). The expression above applies to the case of a continuous signal of infinite duration.
In practice, the intervals must be finite and it is necessary to use a modified version as given
by (3.10).
r11 ( τ )= limT → ∞
1T ∫
−T /2
T /2
x1 (t ) x1( t+τ)dt (3.10)
The autocorrelation function may be applied to deterministic as well as random
signals. Each of the frequency components in the signal x1 (t ) produces a corresponding term
in the autocorrelation function having the same period in the time-shifted variable, τ , as the
original component has in the time variable, t. The amplitude is equal to half of the squared
value of the original.
3.1.2.b Discrete Auto-Correlation:
Discrete auto-correlation is the cross-correlation between the discrete/sampled signal
and signal itself and given as
r11 ( j )= 1N ∑
n=0
N−1
x1 (n ) x1 (n+ j )(3.11)
r22 (− j )= 1N ∑
n=0
N−1
x2 (n ) x2 (n− j )(3.12)
where j is called the lag between the two sampled signals. x1 (n ) and x2 (n ) are the
discrete/sampled signals. N is the length of the sampled signal, means no. of samples taken in
the sampled signal and(0≤ n ≤ N−1)[39].
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-100 -50 0 50 1001
2
3
4
5
6
7
Lags b/w ECG and ECG itself(b)
Aut
o-co
rrela
tion
0 1000 2000 3000 4000 5000 6000-2
-1
0
1
2
3x 10
-3
No. of samples of EEG Signal(c)
Am
plitu
de(V
)
0 1000 2000 3000 4000 5000 6000-0.05
0
0.05
0.1
0.15A
mpl
itude
(V)
No. of samples of ECG Signal(a)
-100 -50 0 50 1002
2.5
3
3.5
4
4.5
5
5.5x 10
-3
Lags b/w EEG & EEG itself(d)
Aut
o-co
rrela
tion
Figure 3.3(a): ECG signal having 5006 samples with sampling rate 500 samples/sec (b) Auto-correlation of ECG signal (c) EEG Signal having 5006 samples with sampling rate 500 samples/sec (d) Auto-correlation of the
A negative correlation means that if one variable gets bigger, the other variable
tends to get smaller.
3.2. Spectral Density Functions:
Spectral density functions can be derived in several ways. One method takes the
Direct Fourier Transform of previously calculated autocorrelation and cross-correlation
functions to yield the two-sided spectral density functions given in (3.13).
Sxx ( f )=∫−∞
+∞
R xx ( τ ) e− j 2 πfτ dτ (3.13)
Syy ( f )=∫−∞
+∞
Ryy (τ ) e− j 2 πfτ dτ (3.14)
Sxy ( f )=∫−∞
+∞
R xy (τ ) e− j 2πfτ dτ (3.15)
These integrals always exist for finite intervals. The quantities Sxx(f ) and Syy ( f ) are
the auto spectral density functions of signals x(t) and y(t) respectively,
Example of auto power spectral density as:
0 1000 2000 3000 4000 5000 6000-0.05
0
0.05
0.1
0.15
No. of Samples of ECG Signal(a)
Am
plitu
de(V
)
0 0.2 0.4 0.6 0.8 1-70
-60
-50
-40
-30
-20
-10
Normalized Frequency ( rad/sample)(b)
Pow
er/fr
eque
ncy
(dB
/rad/
sam
ple) Welch Power Spectral Density Estimate
0 1000 2000 3000 4000 5000 6000-2
-1
0
1
2
3x 10
-3
No. of Samples of EEG Signal(c)
Am
plitu
de(V
)
0 0.2 0.4 0.6 0.8 1-80
-75
-70
-65
-60
-55
-50
Normalized Frequency ( rad/sample)(d)
Pow
er/fr
eque
ncy
(dB
/rad/
sam
ple) Welch Power Spectral Density Estimate
Figure 3.4(a): ECG signal having 5006 samples with sampling rate 500 samples/sec (b) Auto power spectral density estimate of ECG signal (c) EEG Signal having 5006 samples with sampling rate 500 samples/sec (d)
Auto power spectral density estimate of EEG signal
and Sxy(f )is the cross-spectral density function between x (t) and y (t ).
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Abstract
Example of cross power spectral density as:
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
No. of samples of ECG Signal(a)
Am
plitu
de(V
)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10
-3
No. of samples of EEG Signal(b)
Am
plitu
de(V
)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-100
-50
0
Normalized Frequency ( rad/sample)(c)
Pow
er/fr
eque
ncy
(dB
/rad/
sam
ple)
Welch Cross Power Spectral Density Estimate
Figure 3.5(a): ECG signal having 5006 samples with sampling rate 500 samples/sec (b) EEG Signal having 5006 samples with sampling rate 500 samples/sec (c) Cross-power spectral density between the ECG signal and
the EEG signalIn terms of physically measurable one-sided spectral density functions where f varies over
(0 , ∞), the results are given in (3.16):
G xx ( f )=2S xx ( f )(3.16)
G yy ( f )=2 S yy (f )(3.17)
G xy ( f )=2 Sxy ( f )(3.18)
Note that the cross-spectrum is a complex function with real and imaginary functions, where
C xy ( f ) is the coincident spectral density function (cospectrum) and Q xy(f ) is the quadrature
spectral density function (quad-spectrum) as shown in (3.19).
G xy ( f )=2∫−∞
+ ∞
R xx(τ )e− j2 πfτ dτ=C xy ( f )− jQ xy (f )(3.19)
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Abstract
In complex polar notation, the cross-spectral density becomes (3.20).
G xy ( f )=|G xy ( f )|e− j θxy(f )(3.20)
where
|G xy ( f )|=2√C xy2 ( f )+Qxy
2 ( f )(3.21)
θxy (f )=tan−1(Q xy ( f )C xy ( f ) )(3.22)
Here, the auto spectra of the input G xx ( f ) and the cross-spectra G xy ( f ). Nevertheless, the
complete frequency response function with gain and phase can be obtained when both G xx ( f )
and G xy ( f )are known.
3.3. Coherence Function from Spectral Analysis:
Coherence is the degree of relationship or association of frequency spectra between
the ECG and EEG signals at a particular frequency. Notes the peaks in the coherence
spectrum in the figure 3.6(a) as, at 0.01Hz, 5Hz, 31Hz, corresponding approximately to the
different respiratory rates as zero(0-4 BPM), 10-14BPM (Normal Breathing Rate) and 16-
20BPM(High Breathing Rate). The spectral content for each lead is highly similar regardless
of the lead configuration, although the actual energy at each frequency may differ. The
magnitude squared coherence estimate between two signals x (ECG Signal) and y (EEG
Signal), is
γ xy2 =Cxy ( f )=
|Pxy ( f )|2
Pxx ( f )×P yy ( f ) (3 .23)
Here C xy( f ) or γ xy2
is the magnitude squared coherence between the ECG and EEG signals.
Coherence phase is given as
θ( f )=tan−1Im Pxy Re Pxy (3 . 24 )
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Abstract
Where Pxx( f ) is the power spectral estimate of x (ECG Signal), Pyy ( f ) is the power spectral
estimate of y (EEG Signal), and P xy is the cross power spectral estimate of x and y.
Coherence is a function of frequency with C xy( f ) ranging between 0 and 1 and indicates how
well signal x corresponds to signal y at each frequency. The degree of synchronization in
electroencephalography (EEG) signal and ECG signal is commonly characterized by
coherence phase and magnitude squared coherence (MSC).
Example of coherence b/w ECG and EEG (C3−C4) signals:
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
Am
plitu
de(V
)
No. of samples of ECG Signal(a)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10
-3
No. of samples of EEG Signal(b)
Am
plitu
de(V
)
0 5 10 15 20 25 30 350
0.5
1
Coh
eren
ce
Frequency Band(Hz)(c)
Figure 3.6(a): Figure 3.5(a): ECG signal having 5006 samples with sampling rate 500 samples/sec (b) EEG Signal having 5006 samples with sampling rate 500 samples/sec (c) Coherence between the ECG signal and the
EEG signalThe coherence is a frequency domain function with observed values ranging from 0 to
1. At each frequency where the coherence function is performed, it represents the fraction of
the power output related to input. If the coherence function is less than 1, then there are three
possible explanations:
1. There is noise in the system or
2. The system has some nonlinearity generating energy at other frequencies or
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Abstract
3. There are other inputs into the system that have not be accounted for [38].
Chapter 4
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Abstract
COHERENCE ANALYSIS BETWEEN ECG AND EEG
Primarily we analysed the coherence and the phase coherence between the ECG and
EEG signals acquired at the different respiratory rates. The ECG and EEG signals acquired
from the normally healthy subject at the different respiratory rates as
Zero airflow (0-4breaths/min).
Normal airflow (10-12 breaths/min).
High airflow (16-20breaths/min).
The ECG and EEG signals acquired simultaneously with the respiratory signal. Data
collection is done in the Biomedical Instrumentation Laboratory in the department of
Instrumentation and Control Engineering, National Institute of Technology Jalandhar. The
Multi-channel data acquisition Biopac Inc. MP100 system is used to acquire data from the
healthy subject of the age group (20-35 Years).
4.1. ECG and EEG Signals:
0 1000 2000 3000 4000 5000 6000-0.5
0
0.5
1ECG Signals
0 1000 2000 3000 4000 5000 6000-0.5
0
0.5
1
0 1000 2000 3000 4000 5000 6000-0.5
0
0.5
1
Figure 4.1 ECG Signals at the respiratory rates. First signal in the figure 6.1 is at nearly zero breathing rate for 9.99 seconds and similarly second and third signals at the 10 to 12 BPM(breaths per minute) and 15 to
20 BPM.
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Abstract
0 1000 2000 3000 4000 5000 6000-0.2
-0.1
0
0.1
0.2EEG Signals
0 1000 2000 3000 4000 5000 6000-0.2
-0.1
0
0.1
0.2
0 1000 2000 3000 4000 5000 6000-40
-20
0
20
Figure 4.2 EEG Signals respiratory rates. First signal in the Figure 6.1(a) is at nearly zero breathing rate for 9.99 seconds and similarly second and third signals at the 10 to 12 BPM and 15 to 20 BPM.
Table 4.1 Different parameters of the acquired signals
Breathing) and 16-20 breaths/minute (High Breathing Rate). The EEG signals acquired from
the four different positions; the Frontal(F p1−Fp 2
), Central(C3−C4), Parietal (P3−P4) and
Occipital (O1−O2) Brain Regions.
Table 4.4 ECG and EEG signals fron 1 to 25 subjects statistics
Subjects Signals Max(V)
Min(V ) Mean(V)
Stddev(± V)
Median(V)
Subject 1 ECG 0.11200
-0.02319 0.01980 0.02905 0.00427
EEG 0.00458
-0.00153 0.00086 0.00064 0.00092
Subject 2 ECG 0.11200
-0.02777 0.01879 0.02744 0.00488
EEG 0.00549
-0.00214 0.00089 0.00077 0.00092
Subject 3 ECG 0.11108
-0.01495 0.01841 0.02668 0.00397
EEG 0.00275
-0.00183 0.00087 0.00063 0.00092
Subject 4 ECG 0.11383
-0.02411 0.01716 0.02809 0.00275
EEG 0.00244
-0.00153 0.00089 0.00059 0.00092
Subject 5 ECG 0.10376
-0.03326 0.01837 0.02854 0.00366
EEG 0.00366
-0.00122 0.00088 0.00064 0.00092
Subject 6 ECG 0.10651
-0.02045 0.01691 0.02619 0.00336
EEG 0.00366
-0.00122 0.00088 0.00065 0.00092
Subject 7 ECG 0.11200
-0.02594 0.01559 0.02653 0.00305
EEG 0.00336
-0.00275 0.00087 0.00060 0.00092
Subject 8 ECG 0.11169
-0.01678 0.01647 0.02574 0.00305
EEG 0.01099
-0.00916 0.00085 0.00196 0.00092
Subject 9 ECG 0.10254
-0.01495 0.01931 0.02817 0.00336
EEG 0.01312
-0.01343 0.00088 0.00146 0.00092
Subject 10 ECG 0.10590
-0.01648 0.01536 0.02531 0.00305
EEG 0.0061 -0.00488 0.00088 0.00081 0.00092
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Abstract
0Subject 11 ECG 0.1086
4-0.01831 0.01530 0.02511 0.00336
EEG 0.00488
-0.00214 0.00088 0.00067 0.00092
Subject 12 ECG 0.10986
-0.01740 0.01576 0.02639 0.00336
EEG 0.00397
-0.00183 0.00088 0.00060 0.00092
Subject 13 ECG 0.11230
-0.02777 0.01606 0.02724 0.00305
EEG 0.00336
-0.00153 0.00087 0.00057 0.00092
Subject 14 ECG 0.11200
-0.02319 0.01809 0.02837 0.00336
EEG 0.00397
-0.00153 0.00087 0.00057 0.00092
Subject 15 ECG 0.10193
-0.06805 0.02315 0.02898 0.00793
EEG 0.00336
-0.00092 0.00088 0.00056 0.00092
Subject 16 ECG 0.10651
-0.09705 0.01647 0.03005 0.00305
EEG 0.00305
-0.00031 0.00088 0.00055 0.00092
Subject 17 ECG 0.15625
-2.44995 -0.11052 0.38796 0.00183
EEG 0.00824
-0.00824 0.00088 0.00057 0.00092
Subject 18 ECG 0.11688
-0.06653 0.02311 0.03466 0.00397
EEG 0.00244
-0.00061 0.00088 0.00055 0.00092
Subject 19 ECG 0.10437
-0.06348 0.01668 0.02686 0.00397
EEG 0.00214
-0.00031 0.00088 0.00055 0.00092
Subject 20 ECG 0.09003
-0.00610 0.01249 0.02221 0.00183
EEG 0.00275
-0.00092 0.00090 0.00061 0.00092
Subject 21 ECG 0.09033
-0.00549 0.01157 0.02092 0.00183
EEG 0.00305
-0.00092 0.00090 0.00061 0.00092
Subject 22 ECG 0.09064
-0.00580 0.01179 0.02142 0.00153
EEG 0.00488
-0.00183 0.00090 0.00063 0.00092
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Abstract
Subject 23 ECG 0.09064
-0.00549 0.01219 0.02287 0.00153
EEG 0.00397
-0.00122 0.00089 0.00065 0.00092
Subject 24 ECG 0.09186
-0.00702 0.01139 0.02227 0.00153
EEG 0.00305
-0.00153 0.00089 0.00063 0.00092
Subject 25 ECG 0.09186
-0.00641 0.01217 0.02276 0.00183
EEG 0.00427
-0.00183 0.00090 0.00063 0.00092
Table 4.5 ECG and EEG signals from 26 to 50 subjects statistics
Subjects Signals
Max(V ) Min(V ) Mean(V)
Stddev(± V)
Median(V)
Subject 26 ECG 0.09918 -0.00610
0.01497 0.02582 0.00275
EEG 0.00275 -0.00061
0.00081 0.00050 0.00092
Subject 27 ECG 0.10071 -0.00580
0.01525 0.02598 0.00305
EEG 0.00275 -0.00061
0.00081 0.00049 0.00092
Subject 28 ECG 0.10040 -0.00580
0.01505 0.02580 0.00305
EEG 0.00244 -0.00061
0.00080 0.00049 0.00092
Subject 29 ECG 0.10468 -0.00793
0.01515 0.02763 0.00244
EEG 0.00641 -0.00244
0.00080 0.00057 0.00092
Subject 30 ECG 0.10468 -0.00793
0.01564 0.02803 0.00244
EEG 0.00519 -0.00122
0.00080 0.00054 0.00092
Subject 31 ECG 0.16174 -0.09430
0.01848 0.03851 0.00275
EEG 0.00946 -0.00732
0.00090 0.00091 0.00092
Subject 32 ECG 0.16235 -0.10895
0.02169 0.04358 0.00214
EEG 0.01740 -0.01343
0.00088 0.00108 0.00092
Subject 33 ECG 0.16327 -0.08636
0.02330 0.04173 0.00305
EEG 0.00671 -0.00275
0.00090 0.00086 0.00092
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Abstract
Subject 34 ECG 0.16052 -0.11261
0.02494 0.04402 0.00275
EEG 0.00702 -0.00275
0.00090 0.00084 0.00092
Subject 35 ECG 0.16571 -0.09796
0.02296 0.03991 0.00366
EEG 0.00732 -0.00641
0.00088 0.00111 0.00092
Subject 36 ECG 0.09460 -0.00885
0.01215 0.02384 0.00122
EEG 0.00488 -0.00183
0.00090 0.00070 0.00092
Subject 37 ECG 0.09399 -0.00793
0.01304 0.02470 0.00122
EEG 0.00458 -0.00153
0.00091 0.00069 0.00092
Subject 38 ECG 0.09277 -0.00763
0.01285 0.02398 0.00153
EEG 0.00519 -0.00183
0.00091 0.00074 0.00092
Subject 39 ECG 0.09277 -0.00824
0.01195 0.02326 0.00153
EEG 0.00549 -0.00244
0.00089 0.00113 0.00092
Subject 40 ECG 0.09216 -0.00702
0.01232 0.02333 0.00153
EEG 0.00732 -0.00275
0.00091 0.00071 0.00092
Subject 41 ECG 0.13885 -0.06561
0.01862 0.03619 0.00153
EEG 0.00549 -0.00275
0.00081 0.00126 0.00092
Subject 42 ECG 0.13855 -0.06042
0.01813 0.03632 0.00153
EEG 0.00397 -0.00214
0.00079 0.00126 0.00092
Subject 43 ECG 0.13885 -0.06073
0.01802 0.03617 0.00153
EEG 0.00641 -0.00366
0.00081 0.00128 0.00092
Subject 44 ECG 0.13794 -0.05371
0.01800 0.03600 0.00153
EEG 0.00519 -0.00305
0.00079 0.00135 0.00061
Subject 45 ECG 0.13916 -0.05127
0.01814 0.03536 0.00183
EEG 0.00519 -0.00183
0.00079 0.00122 0.00092
Subject 46 ECG 0.09308 - 0.01333 0.02417 0.00122
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Abstract
0.00702EEG 0.00641 -
0.002140.00091 0.00067 0.00092
Subject 47 ECG 0.09033 -0.00580
0.01108 0.02058 0.00153
EEG 0.00275 -0.00092
0.00090 0.00060 0.00092
Subject 48 ECG 0.09247 -0.00916
0.01256 0.02398 0.00122
EEG 0.00458 -0.00153
0.00090 0.00066 0.00092
Subject 49 ECG 0.09369 -0.00824
0.01276 0.02392 0.00122
EEG 0.00366 -0.00092
0.00090 0.00062 0.00092
Subject 50 ECG 0.09186 -0.00610
0.01120 0.02204 0.00153
EEG 0.00305 -0.00153
0.00089 0.00062 0.00092
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Abstract
Chapter 5
Results and Discussion
All the organs of the human body have some synchronism, association and correlation
to each other. In this work we investigate the coherence and phase coherence between the
ECG and EEG; means the association between the human brain and heart. These signals have
proper responses in some specific frequency bands. We acquired 50 ECG and EEG signals
simultaneously using Biopac Inc. Acqknowledge3.9.0 software and MP100 hardware for this
work. All data collected from healthy subjects under the age group (21-36 years old) at the
sampling rate is 500 samples/second. The number of samples used for the analysis of
coherence and phase coherence is 5006 for each signal. The EEG signals acquired from the
four different positions; the Frontal(F p1−Fp 2
), Central(C3−C4), Parietal (P3−P4) and
Occipital (O1−O2) Brain Regions.
5.1 Coherence analysis for first subject:5.1.1 ECG and EEG signals
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
(a)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
(b)
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
(c)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
(d)
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
(e)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-5
0
5x 10-3
(f)
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
No. of samples taken of ECG Signal(g)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
No. of samples taken of EEG Signal(h)
Figure 5.1 (a) & (b) ECG signal and corresponding EEG (Fp1-Fp2) signal (Each signal is sampled at the sampling rate 500 samples/second and No. of samples taken for each signal is 5006) of the S1. (c) & (d) ECG
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Abstract
signal and corresponding EEG (C3-C4) signal. (e) & (f) ECG signal and corresponding EEG (P3-P4) signal. (g) & (h) ECG signal and corresponding EEG (O1-O2) signal.
5.1.2 Coherence between ECG and EEG signals
Coherence between the ECG and corresponding EEG signals acquired from the four
prominent brain regions named as the Frontal(F p1−Fp 2
), Central(C3−C4), Parietal (P3−P4)
and Occipital (O1−O2) is investigated as:
It is shown in figure 5.2(a) the mean of coherence is found to be 0.14019 in the
frequency band (0-35Hz) and the maximum coherence is 0.99601 near the frequency 0.1Hz.
There are another two coherence peaks are found near the frequency range 16Hz to 21Hz.
It is shown in figure 5.2(b) the mean of coherence is found to be 0.13861 in the
frequency band (0-35Hz) and the maximum coherence is 0.99281 near the frequency 0.1Hz.
There are another two coherence peaks are found, one is near the frequency range 4.9Hz and
another near the 31Hz.
It is shown in figure 5.2(c) the mean of coherence is found to be 0.14399 in the
frequency band (0-35Hz) and the maximum coherence is 0.99142 near the frequency 0.1Hz.
There are another three coherence peaks are found, one is near the frequency 7Hz, second is
near the frequency 26Hz and third is near the frequency 35Hz.
It is shown in figure 5.2(d) the mean of coherence is found to be 0.15198 in the
frequency band (0-35Hz) and the maximum coherence is 0.99663 near the frequency 0.1Hz.
There are another two coherence peaks are found, one is near the frequency 9.5Hz and
another is near the frequency 35Hz.
Standard deviation means the variation in the coherence from the mean in both
directions (Upward or positive and Downward or negative) is generally increasing
continuously from the coherence between ECG and EEG signals from the Frontal(F p1−Fp 2
),
Central(C3−C4), Parietal (P3−P4) and Occipital (O1−O2) respectively.
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Abstract
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
(a)
Cohe
renc
e
Fp1-Fp2 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
(b)
C3-C4 y median y mean y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
Frequency Band(Hz)(c)
Cohe
renc
e
P3-P4 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
Frequency Band(Hz)(d)
O1-O2 y mean y median y std
Figure 5.2 (a) Coherence between ECG and EEG (Fp1-Fp2) in Frequency Band (0 to 35 Hz) for S1. (b) Coherence between ECG and EEG (C3-C4). (c) Coherence between ECG and EEG (P3-P4). (d) Coherence
between ECG and EEG (O1-O2).5.1.3 Phase Coherence between ECG and EEG signals
Phase coherence is the measure of the phase induced by the one signal to another
signal at a particular frequency. Here, it is measured in radians. The mean of phase coherence
is found to be maximum when it is measured between the ECG signal and corresponding
EEG signal acquired from the frontal (F p1−Fp 2
) region.
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
(a)
Cohe
renc
e Ph
ase
Fp1-Fp2 y mean y median y std
0 5 10 15 20 25 30 35-1.5
-1
-0.5
0
0.5
1
1.5
2
(b)
C3-C4 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Frequency Band(Hz)(c)
Cohe
renc
e Ph
ase
P3-P4 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Frequency Band(Hz)(d)
O1-O2 y mean y median y std
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Abstract
Figure 5.3 (a) Coherence phase between ECG and EEG (Fp1-Fp2) in Frequency Band (0 to 35 Hz) for S1. (b) Coherence phase between ECG and EEG (C3-C4). (c) Coherence phase between ECG and EEG (P3-P4). (d)
Coherence phase between ECG and EEG (O1-O2).Table 5.1 Coherence and phase coherence measure parameters for first subject
5.2 Coherence analysis for second subject5.2.1 ECG and EEG signals
0 1000 2000 3000 4000 5000 6000-0.1
0
0.1
(a)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-5
0
5x 10-3
(b)
0 1000 2000 3000 4000 5000 6000-0.1
0
0.1
(c)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
(d)
0 1000 2000 3000 4000 5000 6000-0.1
0
0.1
(e)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-5
0
5
10x 10-3
(f)
0 1000 2000 3000 4000 5000 6000-0.1
0
0.1
No. of Samples of ECG Signals(g)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-5
0
5x 10-3
No. of Samples of EEG Signals(h)
Figure 5.4 (a) & (b) ECG signal and corresponding EEG (Fp1-Fp2) signal (Each signal is sampled at the sampling rate 500 samples/second and No. of samples taken for each signal is 5006) of the S2. (c) & (d) ECG
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Abstract
signal and corresponding EEG (C3-C4) signal. (e) & (f) ECG signal and corresponding EEG (P3-P4) signal. (g) & (h) ECG signal and corresponding EEG (O1-O2) signal.
5.2.2 Coherence between ECG and EEG signals
Coherence between the ECG and corresponding EEG signals acquired from the four
prominent brain regions named as the Frontal(F p1−Fp 2
), Central(C3−C4), Parietal (P3−P4)
and Occipital (O1−O2) is investigated as:
It is shown in figure 5.5(a) the mean of coherence is found to be 0.13950 in the
frequency band (0-35Hz) and the maximum coherence is 0.99819 near the frequency 0.1Hz.
There is one coherence peak is found near the frequency 2.5Hz.
It is shown in figure 5.5(b) the mean of coherence is found to be 0.13569 in the
frequency band (0-35Hz) and the maximum coherence is 0.99569 near the frequency 0.1Hz.
There is one coherence peak is found near the frequency 1.5Hz.
It is shown in figure 5.5(c) the mean of coherence is found to be 0.16404 in the
frequency band (0-35Hz) and the maximum coherence is 0.99829 near the frequency 0.1Hz.
There are another three coherence peaks are found, one is near the frequency 21Hz, second
and third are near the frequency range 5.5Hz to 6Hz.
It is shown in figure 5.5(d) the mean of coherence is found to be 0.16092 in the
frequency band (0-35Hz) and the maximum coherence is 0.99381 near the frequency 0.1Hz.
There is coherence peaks is found, near the frequency 5Hz.
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
(a)
Cohe
renc
e
Fp1-Fp2 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
(b)
C3-C4 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
Frequency Band(Hz)(c)
Cohe
renc
e
P3-P4 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
Frequency Band(Hz)(d)
O1-O2 y mean y median y std
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Abstract
Figure 5.5(a) Coherence between ECG and EEG (Fp1-Fp2) in Frequency Band (0 to 35 Hz) for S2. (b) Coherence between ECG and EEG (C3-C4). (c) Coherence between ECG and EEG (P3-P4). (d) Coherence
between ECG and EEG (O1-O2).
5.2.3 Phase coherence between ECG and EEG signals
Phase coherence is the measure of the phase induced by the one signal to another signal at a particular frequency. Here, it is measured in radians. The mean of phase coherence is found to be maximum when it is measured between the ECG signal and corresponding EEG signal acquired from the frontal (F p1
−Fp 2) region.
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
(a)
Cohe
renc
e Ph
ase
Fp1-Fp2 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
(c)
C3-C4 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
Frequency Band(Hz)(c)
Cohe
renc
e Ph
ase
P3-P4 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Frequency Band(Hz)(d)
O1-O2 y mean y median y std
Figure 5.6 (a) Coherence phase between ECG and EEG (Fp1-Fp2) in Frequency Band (0 to 35 Hz) for S2. (b) Coherence phase between ECG and EEG (C3-C4). (c) Coherence phase between ECG and EEG (P3-P4). (d)
Coherence phase between ECG and EEG (O1-O2).Table 5.2 Coherence and phase coherence measure parameters for second subject
5.3 Coherence analysis for third subject5.3.1 ECG and EEG signals
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
(a)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
(b)
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
(c)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
(d)
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
(e)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
(f)
0 1000 2000 3000 4000 5000 6000-0.2
0
0.2
No. of Samples of ECG Signals(g)
Ampl
itude
(V)
0 1000 2000 3000 4000 5000 6000-2
0
2
4x 10-3
No. of Samples of EEG Signals(h)
Figure 5.7 (a) & (b) ECG signal and corresponding EEG (Fp1-Fp2) signal (Each signal is sampled at the sampling rate 500 samples/second and No. of samples taken for each signal is 5006) of the S3. (c) & (d) ECG
signal and corresponding EEG (C3-C4) signal. (e) & (f) ECG signal and corresponding EEG (P3-P4) signal. (g) & (h) ECG signal and corresponding EEG (O1-O2) signal.
5.3.2 Coherence between ECG and EEG signals
Coherence between the ECG and corresponding EEG signals acquired from the four
prominent brain regions named as the Frontal(F p1−Fp 2
), Central(C3−C4), Parietal (P3−P4)
and Occipital (O1−O2) is investigated as:
It is shown in figure 5.8(a) the mean of coherence is found to be 0.13443 in the
frequency band (0-35Hz) and the maximum coherence is 0.99429 near the frequency 0.1Hz.
It is shown in figure 5.8(b) the mean of coherence is found to be 0.13662 in the
frequency band (0-35Hz) and the maximum coherence is 0.99420 near the frequency 0.1Hz.
There are another two coherence peaks are found, one is near the frequency range 2Hz and
another near the 12Hz.
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It is shown in figure 5.8(c) the mean of coherence is found to be 0.14209 in the
frequency band (0-35Hz) and the maximum coherence is 0.99733 near the frequency 0.1Hz.
There is one coherence peak is found near the frequency 32.5Hz.
It is shown in figure 5.8(d) the mean of coherence is found to be 0.15163 in the
frequency band (0-35Hz) and the maximum coherence is 0.99647 near the frequency 0.1Hz.
There are another three coherence peaks are found, one is near the frequency 6.5Hz and
second is near the frequency 22Hz and third is near the frequency 24Hz.
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
(a)
Cohe
renc
e
Fp1-Fp2 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
(b)
C3-C4 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
Frequency Band(Hz)(c)
Cohe
renc
e
P3-P4 y mean y median y std
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
Frequency Band(Hz)(d)
O1-O2 y mean y median y std
Figure 5.8 (a) Coherence between ECG and EEG (Fp1-Fp2) in Frequency Band (0 to 35 Hz) for S3. (b) Coherence between ECG and EEG (C3-C4). (c) Coherence between ECG and EEG (P3-P4). (d) Coherence
between ECG and EEG (O1-O2).
5.3.3 Phase coherence between ECG and EEG signals
Phase coherence is the measure of the phase induced by the one signal to another signal at a particular frequency. Here, it is measured in radians. The mean of phase coherence is found to be maximum when it is measured between the ECG signal and corresponding EEG signal acquired from the frontal (F p1
−Fp 2) region.
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Abstract
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
(a)
Cohe
renc
e Ph
ase
Fp1-Fp2 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
(b)
C3-C4 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Frequency Band(Hz)(c)
Cohe
renc
e Ph
ase
P3-P4 y mean y median y std
0 5 10 15 20 25 30 35-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Frequency Band(Hz)(d)
O1-O2 y std y median y mean
Figure 5.9 (a) Coherence phase between ECG and EEG (Fp1-Fp2) in Frequency Band (0 to 35 Hz) for S3. (b) Coherence phase between ECG and EEG (C3-C4). (c) Coherence phase between ECG and EEG (P3-P4). (d)
Coherence phase between ECG and EEG (O1-O2).
Table 5.3 Coherence and phase coherence measure parameters for third subject
5.4 Combine coherence analysis for all three subjects
In the figure 5.10(a), (b) and (c) the maximum coherence upper quartile is in the
coherence between ECG and corresponding EEG signal (O1−O2) from all three subjects. The
number of coherence peaks (coherence value greater than ≥ 0.5) is found to be more in the
coherence between ECG and EEG signal (P3−P4) in all three subjects. It reflects that the
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heart signal has relatively more functional association or relationship to the corresponding
brain signal (P3−P4) at a particular frequency band (0 to 35Hz).
0
0.2
0.4
0.6
0.8
1
1 2 3 4(a)
Cohe
renc
e
0
0.2
0.4
0.6
0.8
1
1 2 3 4(b)
Cohe
renc
e
0
0.2
0.4
0.6
0.8
1
1 2 3 4Boxplots for ECG and EEG Signals Coherence from
four Brain Regions(c)
Cohe
renc
e
Figure 5.10 (a) Coherence between the ECG signals corresponding to the EEG signals 1-EEG (Fp1-Fp2), 2-EEG (C3-C4), 3-EEG (P3-P4), 4-EEG (O1-O2) of the First Subject (S1). (b) Coherence between the ECG
signals corresponding to the EEG signals of the Second Subject (S2). (c) Coherence between the ECG signals corresponding to the EEG signals of the Third Subject (S3).
The figure 5.11 (a), (b) and (c) show the number of coherence values fall in the
different coherence limits for first, second and third subjects respectively. Here, in figure 5.11
(a), (b) and (c) the one coherence value greater than 0.9 is fall in the coherence limit (0.9 to 1)
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Abstract
of all subjects different four types of coherence calculations between the ECG signal and the
corresponding EEG signals. No coherence value is found in the coherence limits (0.7 to 0.8)
and (0.8 to 0.9).
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
20
40
60
80
(a)
No.
of C
oher
ence
val
ues
Coherence b/w ECG & EEG(Fp1-Fp2) of S1Coherence b/w ECG & EEG(C3-C4) of S1Coherence b/w ECG & EEG(P3-P4) of S1Coherence b/w ECG & EEG(O1-O2) of S1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
20
40
60
80
(b)
No. o
f Coh
eren
ce v
alue
s
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
20
40
60
80
Coherence Range(c)
No. o
f Coh
eren
ce v
alue
s
Figure 5.11 (a) Coherence between the ECG signals corresponding to the EEG signals 1-EEG (Fp1-Fp2), 2-EEG (C3-C4), 3-EEG (P3-P4), 4-EEG (O1-O2) of the First Subject (S1). (b) Coherence between the ECG
signals corresponding to the EEG signals of the Second Subject (S2). (c) Coherence between the ECG signals corresponding to the EEG signals of the Third Subject (S3).
5.5 Combine phase coherence analysis for all three subjects
The figure 5.12(a), (b) and (c) provide the information how the coherence phase mean
vary in the three subjects among the different coherence investigated. The upper phase
coherence quartile (75 percentile of all phase coherence) is found to be maximum in the all
three subjects. The mean of phase coherence varies randomly in the three subjects’ coherence
investigated.
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Abstract
-1
0
1
1 2 3 4(a)
Cohe
renc
e Ph
ase
-1
0
1
1 2 3 4(b)
Cohe
renc
e Ph
ase
-1
0
1
1 2 3 4(c)
Cohe
renc
e Ph
ase
Figure 5.12 (a) Coherence phase between the ECG signals corresponding to the EEG signals 1-EEG (Fp1-Fp2), 2-EEG (C3-C4), 3-EEG (P3-P4), 4-EEG (O1-O2) of the First Subject (S1). (b) Coherence phase between the ECG signals corresponding to the EEG signals of the Second Subject (S2). (c) Coherence phase between the
ECG signals corresponding to the EEG signals of the Third Subject (S3).
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Abstract
Chapter 6
CONCLUSION AND FUTURE SCOPE
Conclusion:
This research is divided into two phases. In first phase we have evaluated the
coherence and phase coherence between the ECG signals and the EEG signals acquired from
the temporal region of the brain at the different respiratory rates. Here we acquired these
signals at the 500 samples/second (Sampling Rate). Number of sample points (samples) for
each signal is 5006. Data acquisition is done using Biopac Inc. MP100 and the software tool
AcqKnowledge3.9.0 by setting the corresponding calculate channel for getting respiratory
rates simultaneously with the ECG and EEG signals. We have found that the maximum
coherence mean is at the no airflow or zero respiratory rates (0-4 Breaths/min) between the
ECG and EEG signal. We have found that the minimum coherence mean is at the high
airflow or high respiratory rates (16-20 Breaths/min) between the signals. The peak of
coherence is found more than 0.5 at the respiratory frequency 0.1Hz in the coherence
between ECG and EEG signals at the zero respiratory rates and the normal respiratory rates.
In second phase, we acquired the data from 50 subjects and analyse the coherence. In
thesis we include the three subject’s data for finding the coherence and phase coherence
between ECG signals and the corresponding EEG signals acquired from the different brain
regions. The different brain regions are the Frontal(F p1−Fp 2
), Central(C3−C4), Parietal
(P3−P4) and occipital(O1−O2) from which the EEG signal acquired. The coherence and
phase coherence for each subject and each set of signals is evaluated using magnitude
squared coherence function.
For first subject, the maximum, mean of coherence is in the coherence between the
ECG and the EEG signal acquired from the (O1−O2) region. The maximum numbers of
coherence peaks are in the coherence between ECG and EEG signal acquired from (P3−P4)
region. The maximum, mean of phase coherence is in the phase coherence between the ECG
and EEG signal acquired from the frontal brain region(F p1−Fp 2
).
For second subject, the maximum, mean of coherence is in the coherence between the
ECG and the EEG signal acquired from the (O1−O2) region. The maximum numbers of
coherence peaks are in the coherence between ECG and EEG signal acquired from (P3−P4)
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Abstract
region. The maximum, mean of phase coherence is in the phase coherence between the ECG
and EEG signal acquired from the frontal brain region(F p1−Fp 2
).
Similarly for the third subject the above coherence measure is analysed.
In conclusion, the results of the investigation of interactions between spectral power
bands of ECG and EEG signals may contribute to a better understanding of physiological
mechanisms underlying the interactions between brain and heart during normal breathing but
need to be further investigated in a larger and more diverse sample of normal healthy
subjects, children, and old subjects at the unipolar EEG signals acquired from the particular
montages as F p1, Fp 2
, C3 , C z ,C 4 , P3 , P z , P4 ,O1 , O2etc . during different subject conditions.
Future Scope:
All the organs of the human body have some synchronism, association and correlation
to each other. In this work we investigate the coherence and phase coherence between the
ECG and EEG; means the association between the human brain and heart. Communication
between the heart and brain is actually a dynamic, ongoing, two-way dialogue, with each
organ continuously influencing the other's function. Research has shown that the heart
communicates to the brain in four major ways: neurologically (through the transmission of
nerve impulses), biochemically (via hormones and neurotransmitters), biophysically (through
pressure waves) and energetically (through electromagnetic field interactions).
Communication along all these conduits significantly affects the brain's activity. The
magnitude squared coherence between the two physiological signals provides the valuable
association between the corresponding physiological organs. We also analysed the phase
induced by the one physiological signal to another physiological signal. It is quantified, both
coherence and the phase coherence.
Now, we have the coherence spectrum, and we have analysed the numbers of
coherence peaks in the specified frequency band. The coherence peak reflects that the one
physiological signal is synchronised with another physiological signal at the particular
frequency. The phase coherence spectrum reflects that the one physiological signal induced,
how much phase (lead or lag corresponding to the positive phase and the negative phase) to
the another physiological signal.
REFERENCES
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Abstract
[1].Yue-Der Lin, Wei-Ting Liu, Ching-Che Tsai, and Wen-Hsiu Chen, “Coherence
analysis between respiration and PPG signal by bivariate AR model” World Academy
of Science, Engineering and Technology,2009,pp.847-852.
[2].Sanqing Hu, Matt Stead, Qionghai Dai, and Gregory A. Worrell, “On the recording
reference contribution to EEG correlation, phase synchrony, and coherence”, “IEEE
Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics”, pp.1-11, 2010.
[3].AH Khandoker, CK Karmakar, M Palaniswami The University of Melbourne, Victoria,
Australia, “Interaction between sleep EEG and ECG signals during and after
obstructive sleep apnea events with or without arousals”. “Computers in Cardiology”,
2008, pp.685-688.
[4].Naveed R. Butt and Andreas Jakobsson, “Coherence spectrum estimation from
coherence during Chinese Stroop task”, Computers in Biology and Medicine 36,
pp.1303-1315, Aug 2006.
[33]. Barry R. Greene, Geraldine B. Boylan, Richard B. Reilly, Philip de Chazal and Sean
Connolly, “Combination of EEG and ECG for improved automatic neonatal seizure
detection”, Clinical Neurophysiology 118, pp.1348-1359. March 2007.
[34]. MC Mantaras, MO Mendez, O Villiantieri, N Montano, V Patruno, AM Bianchi, S
Cerutti, “Non-parametric and Parametric Time-Frequency Analysis of Heart Rate
Variability during Arousals from Sleep”, Computers in Cardiology no.33, 2006,
pp.745-748.
[35]. Ahsan H. Khandoker, Chandan K. Karmakar, Marimuthu Palaniswami, “Analysis of
coherence between sleep EEG and ECG signals during and after obstructive sleep
apnea events”, 30th Annual International IEEE EMBS Conference, Vancouver, British
Columbia, Canada, August 20-24, 2008, pp. 3876-3879.
[36]. Mahmoud El-Gohary, James McNames, Tim Ellis and Brahm Goldstein, “Time
Delay and Causality in Biological Systems Using Whitened Cross-correlation
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Abstract
Analysis” Proceedings of the 28th IEEE EMBS Annual International Conference New
York City, USA, Aug 30-Sept 3, 2006, pp. 6169-6172.
[37]. Catarina S. Nunes, Teresa Mendonc¸a, Susana Br´as, David A. Ferreira, and Pedro
Amorim, “Modeling Anesthetic Drugs’ Pharmacodynamic Interaction on the Bispectral
Index of the EEG: the Influence of Heart Rate”, Proceedings of the 29th Annual
International Conference of the IEEE EMBS Cite International, Lyon, France August
23-26, 2007, pp. 6479-6482.
[38]. Charles S. Lessard, “Signal Processing Of Random Physiological Signals”, Coherence Function from Spectral Analysis, , First Edition, 2006 by Morgan & Claypool, pp.203-212.
[39]. Emmanuel C. Iteachar and Barrie W. Jervis, “Digital signal Processing: A Practical Approach”, Correlation and Convolution, second edition, Pearson education, pp.242-249.
APPENDIX
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Abstract
(A):
A.1. LEAD110 Series — Electrode leads
The LEAD110 Series, for use with disposable and other snap connector electrodes, are pinch
leads for easy connection between the EL500-series snap electrodes and any BIOPAC
biopotential amplifier or the GND terminal on the back of the UIM100C. Leads terminate in
standard 2 mm pin plug and connect to BIOPAC modules or to a Modular Extension Cable
(MEC series).
Table A.1: Lead Type Length Usage Note
Sr.
No.
Leads Name Type Length Procedure of Using Leads
1. LEAD110 Unshielded 1 m Works best as a ground electrode
2. LEAD110A Unshielded 3 m Works best with ground or reference electrodes
3. LEAD110S-
R
Shielded 1 m Use with recording electrodes for minimal noise
interference. The white lead plug is for the
electrode contact; the black lead pin plug is for
the lead shield.
4. LEAD110S-
W
Shielded 1 m Use with recording electrodes for minimal noise
interference. The white lead plug is for the
electrode contact; the black lead pin plug is for
the lead shield.
IMPORTANT SAFETY NOTES
1. MEC series cables are not to be used on humans when they are undergoing
electrosurgery or defibrillation. In fact, no BIOPAC equipment should be connected
to human subjects during the course of defibrillation or electrosurgery.
2. When MEC series cables are used, be careful to preserve the isolation of MP system
during defibrillation. No external lab equipment should be connected directly to the
UIM100C, IPS100C or any included amplifier module. To preserve MP system
isolation, all connections of this type should be made using INISO or OUTISO with
the HLT100C. To verify that the isolation of the recording system is intact, use a
multimeter to measure resistance from subject ground (on biopotential amplifier) to
mains ground; there should be no DC conductivity.
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Abstract
3. Do not connect the electrode leads attached to the MEC series cables directly to
defibrillator paddles. When using MEC cables, electrode leads should be connected to
the subject directly and not via the defibrillator paddles
A.1.a Common Extensions
MEC100C 100C-series Transducer amplifiers to Touchproof inputs
MEC110C 100C-series Biopotential amplifiers to Touchproof inputs
MEC111C 100C-series Biopotential amplifiers to Touchproof inputs—Protected
A.1.b Less Common Extensions
MEC100 DA100C or 100B-series Biopotential or Transducer amplifiers to 2mm socket
inputs
MEC101 100B-Series Biopotential amplifiers to 2mm socket inputs – Protected
MEC110 100B-series Biopotential or Transducer amplifiers to Touchproof inputs
MEC111 100B-series Biopotential amplifiers to Touchproof inputs—Protected
A.2 TSD201The TSD201 is a strain gauge transducer designed to measure respiratory-induced
changes in thoracic or abdominal circumference, and can therefore be used to record
respiratory effort. The TSD201 is essentially a resistive transducer and responds in a linear
fashion to changes in elongation through its length, with resistance increasing as length
increases.
The transducer is ideal for a variety of applications because it presents minimal
resistance to movement and is extremely unobtrusive. Due to its unique construction, the
TSD201 can measure extremely slow respiration patterns with no loss in signal amplitude
while maintaining excellent linearity and minimal hysteresis.
The TSD201 plugs directly into the RSP100C amplifier module (page 114). It
includes a fully adjustable nylon strap to accommodate a large range of circumferences (9 cm
to 130 cm). To attach the nylon belt to the respiration transducer, thread the nylon strap
through the corresponding slots so the strap clamps into place when tightened. Place the
transducer around the body at the level of maximum respiratory expansion. This location will
vary from the erect to supine positions (generally about 5 cm below the armpits).
Correct tension adjustment of the respiration transducer is important. For best
sensitivity, the transducer must be just slightly tight at the point of minimum circumference
(maximum expiration). To obtain proper tension, stretch the belt around the body and have
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Abstract
the subject exhale. At maximum expiration, adjust the nylon strap so there is slight tension to
hold the strap around the chest.
The transducer has three 2 mm pin plugs to connect to the amplifier. Insert the two
blue lead transducer pin plugs into the two RSP100C inputs labelled XDCR. Either blue lead
can be connected to either XDCR input. Insert the single black transducer lead into the GND
input of the RSP100C. The respiration transducer is ready for measurement.
A.2.a TSD201 Calibration
The TSD201 does not require calibration.
Table A.2: TSD201 Specifications
Sr.No
.
Parameters Sensor Parameter Range Values
1. True DC Response Yes
2. Variable Resistance Output 5-125 KΩ (increases as length increases)
3. Circumference Range 15 cm x 150 cm (can be increased with a longer strap)
4. Attachment Velcro® strap (adjustable length)
5. Sterilizable Yes
6. Sensor Weight 18 gs
7. Sensor Dimensions 66 mm (long), 40 mm (wide), 15 mm (thick)
8. Cable Length 3 m
CBLCALC Calibration Cable for 100C-series Biopotential Amplifiers
CBLCAL Calibration Cable 100-B series Biopotential Amplifiers
Use CBLCAL/C to verify the calibration of the any of the Biopotential amplifiers.
The cable (1.8m) connects between the amplifier input and the UIM100C D/A output 0 or 1.
To verify the amplifier’s frequency response and gain settings, create a stimulus signal using
AcqKnowledge and monitor the output of the amplifier connected to the Calibration Cable.
The Calibration Cable incorporates a precision 1/1000 signal attenuator.
Amplifier specification tests are performed at the factory before shipping, but a
Calibration Cable can ensure users peace of mind by permitting precise frequency response
and gain calibrations for exact measurements.
CBLCAL/C Calibration
A.2.b Hardware Setup
1. Connect the MP150/100, UIM100C and biopotential amplifiers as normal.
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2. Connect the CBLCAL/C between the selected amplifier and the UIM100C, inserting
the single 3.5mm plug into the Analog Output “0” port on the UIM100C.
3. Connect the end containing several 2mm pins into the corresponding holes on the face
of the biopotential amplifier.
4. Select a Gain setting of 1,000 for DA, ECG, EGG, EMG, and EOG, or 5,000 for EEG
and ERS.
5. Turn all filters to the desired position.
6. Select an appropriate channel on the top of the amplifier being tested (usually channel
one, as this is the default setup in the software).
A.2.c Software Setup
1. Under Channel Setup, insure that the default is set to analog channel one (A1).
2. Under Acquisition Setup
a) Choose a sampling rate of 2000Hz (or higher).
b) Choose an acquisition period of at least 5 seconds.
c) Choose Record Last mode.
3. Under Stimulator Setup
a) Select the sine wave for the shape of the output signal.
b) Set the “Seg. #1 Width’ to zero. This means that the signal will be transmitted
continuously starting at time-point zero.
c) Set “Seg. #2 Width” to 1,000 msec (one second). This is the length of the output
signal.
d) Select “Analog Output: 0.”
e) Select “Output continuously.”
f) The most important settings are the signal magnitude and frequency. Set the
magnitude to 5 Volts (i.e., 10V p-p) if the module gain setting is 1,000. If the
lowest module gain setting available is 5,000, choose 1 Volt.
g) Set the frequency to 10Hz to check the gain calibration (on a sinusoidal signal,
this setting is appropriate for all biopotential amplifiers).
A.2.d Calibration Procedure
AcqKnowledge is now set-up to check for the proper calibration of biopotential amplifiers.
1. Start the acquisition. Theoretically, since you are in record last mode and are
outputting a signal continuously, AcqKnowledge could acquire data forever.
2. Stop the acquisition when the waveform has stabilized.
3. Use the “I-beam” cursor to select the latter part of the record.
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4. Perform all your calibration measurements on the latter part of the collected record.