AN INNOVATIVE TECHNIQUE TO ENHANCE PSYCHO –

COGNITIVE PARAMETER ASSESSMENT IN PREGNANCY

M. S. NAGANANDA

CENTRE FOR BIOMEDICAL ENGINEERING

INDIAN INSTITUTE OF TECHNOLOGY DELHI

FEBRUARY 2016

© Indian Institute of Technology Delhi, New Delhi. 2016

AN INNOVATIVE TECHNIQUE TO ENHANCE PSYCHO –

COGNITIVE PARAMETER ASSESSMENT IN PREGNANCY

by

M. S. NAGANANDA

Centre for Biomedical Engineering

Submitted

In fulfillment of the requirements of the degree of

DOCTOR OF PHILOSOPHY

to the

INDIAN INSTITUTE OF TECHNOLOGY DELHI

NOVEMBER 2015

ii

CERTIFICATE

This is to Certify that the Thesis entitled “An Innovative Technique to Enhance Psycho-

Cognitive Parameter Assessment in Pregnancy” being submitted by M S Nagananda to the

Indian Institute of Technology, Delhi (India) for the award of the Doctor of Philosophy in

Biomedical Engineering is a bonafide research work carried out by him under our guidance

and supervision. To the best of our knowledge, the thesis has reached the requisite standard.

We hereby declare that the content of the thesis, in full or in part, have not been submitted to

any other Institute or University for the award of any degree or diploma.

Sneh Anand, PhD

PhD Professor,

Centre for Biomedical Engineering,

Indian Institute of Technology Delhi,

New Delhi 110016, INDIA.

Amit Sengupta, PhD

Adjunct Professor,

Centre for Biomedical Engineering,

Indian Institute of Technology

Delhi,

New Delhi 110016, INDIA,

Consultant Gynecologist.

Jayashree Santhosh, PhD

Senior Lecturer,

Department of Biomedical Engineering,

Faculty of Engineering Building,

University of Malaya, 50603

Kuala Lumpur, MALAYSIA

iii

ACKNOWLEDGEMENTS

I was one of those fortunate few, who could get an opportunity to work under Prof. Sneh

Anand, Prof. Amit Sengupta and Dr. Jayashree Santhosh. They introduced me into to the

field of Biomedical Engineering, nurtured me new ideas and helped me to develop interest in

this field. Their meticulous guidance, incessant encouragement and watchful supervision

were instrumental in carrying out this investigation. I remain immensely grateful to them.

Prof. Sneh Anand has been an excellent supervisor. I would like to thank her for

encouraging my research and for allowing me to grow as a research scientist. Her enthusiasm

and scientific curiosity combined with encouraging stimulation discussions, have served to

heighten my interest and motivation in this work. I am grateful for her affection,

understanding and moral support.

I am truly indebted to Prof. Amit Sengupta, for his untiring support and guidance throughout

the different stages of this work. My co-supervisor has positive leadership and keen eye for

practical details. In his natural parental and friendliness way of interactions, have provided

constant support and encouragement in successful completion of this work.

Dr. Jayashree Santhosh, Faculty, Department of Biomedical Engineering, Kuala Lumpur,

Malaysia, my co-supervisor was always very supportive throughout my experiments and

documentation. Her guidance, comments and encouragements were always motivated me

throughout this research work.

I feel deeply indebted to Prof. A. M. Khan, Professor, NIHFW, New Delhi, I would like to

thank him for encouraging me to grow my interest in Statistical Analysis and Social Science.

His passion and scientific inquisitiveness combined with encouraging inspirational

discussions, have served to heighten my interest and motivation in this work. He was very

iv

supportive in all my experiments, technical paper reviews, and very particularly in

documentation. Wish to thank him for the useful comments and directions in developing this

thesis to the present form.

Prof. S. M. K Rahman, and Dr. B. K. Panigrahi SRC members, I am thankful for their

support, guidance, advice and encouragement throughout the period of this research. The

ideas presented in this thesis were developed and evaluated in collaboration with my

supervisors, SRC members and many other co-workers in the Centre for Bio-Medical

Engineering, IITD, and Bio-Medical Engineering Unit, AIIMS, New Delhi, India.

I am thankful to my Department Faculty members, especially Prof. Veen Koul (Professor &

Head, CBME), Prof. Harpal Singh (CBME), Prof. Nivedita Karmakar, Prof. L. K. Das

(IDDC, IITD), Prof. Vyas (IDDC, IITD), Dr. Tamalika, and, Mr. Promit.

I am extremely thankful to Prof. B. N. Gangadhar (Dean Academics, NIMHANS,

Bangalore), Prof. Rajesh Sagar (Psychiatrist, AIIMS, New Delhi), Prof. B. S. Nagi,

Statistician, and Dr. Arvind (ISRO, Bangalore), for their Guidance, Support, and

Encouragement.

I am extremely thankful to Dr. Prajapathi, Dr. Vidya (Gynecologist, NMMC), Dr. Praveen

Katke (NMMC), Duty Doctors, Staff Nurse and Health Workers helping me in data

collection at different gynecology clinics at NMMC, Navy Mumbai. I would also like to

acknowledge my gratitude to “Project Sure Start” for providing me subjects. I greatly

indebted to all my subjects without their participation, support and keen interest this research

work would not have been completed.

I would like to thank the Management of RASHREEYA SIKSHANA SAMITHI TRUST

(RSST) and the Principal, R V College of Engineering, Bangalore for permitting me to carry

v

out Doctoral Research work at IITD, New Delhi and Prof. Kalra, QIP co-ordinators, IIT

Delhi, for their co-operation and allowing me to continue my research work. Friends, Duty

Doctors at various clinics of Navi Mumbai, Professors, Faculties, Colleagues, supporting

staff at IITD, AIIMS, New Delhi, ACTREC, MMMC Govt. Hospital, Navy Mumbai, and R

V College of Engineering, Bangalore had supported, associated, encouraged, and criticized in

this long journey of Doctoral Research at IIT Delhi. I wish to thank all those people who

directly or indirectly helped me during this research work.

I would like to thank the Management of Mahatma Gandhi Vidya Peetha Education Trust

(MGVP), Dr. S C Sharma, Honorable Director, DSCE & DSATM, Professor of Eminence

and Dr. CPS Prakash, Principal, DSCE, Bangalore, and Dr. D. Narayana Dutt, Professor,

Medical Electronics Department, DCSE, Bengaluru for their support for my research work at

IIT Delhi.

I am really fortunate to have colleagues like Mr. Sanjeev Kumar Kubakaddi, Mr. Nitin

Agarwal, Dr. Sarvan Pahuja, Dr. Yogeshwar Rao, Dr. Deepak, Dr. Sonal, Dr. Tapan, Dr.

Bikesh Nirala, Dr. Periya Samy, Mrs. Chaaya, Dr. Sanjeev, and Dr. Vijay, Mrs. Jean for their

wonderful company.

I am thankful to my junior lab colleagues, for helping me out in every possible way. Mr.

Raman, Ms. Sweety, Ms. Sarul, Mr. Anoop, Mr. Mariswaran, Mr. Peeush, and Mr. Anirban.

My special thanks to Mrs. Sumita, Mr. Rajesh, Mr. Anil and Ms. Amajit and other office

staffs for their help, whenever I am in need.

I would like to thank my friends Dr. Jeevanand (Electrical), Dr. Hitesh Sirmali (Electrical),

Mr. Veerabadrappa (IDDC) for their continuous support and research discussions at IIT

Delhi.

vi

My very special thanks go to my close friends Prof. C. R Raja Gopal, Dr. Charu Chandra, and

Major. Vasist, for their support and encouragement.

Many thanks to my good friends Mr. Sanjeev, Mr. Harsha, Mr. Rajashaker, Mr. Kiran, Mr.

Dushant, Mr. Ninge Gowda, Mr. Vishwanath and Mr. Raghavendra at IIT Delhi.

Many thanks to my good students Mr. Apoorvagiri, Mr. Pradeep G, Mr. Nitin, Mr. Sreehari at

RVCE Bangalore and Ms. Nikita Valke, Intern student at Dayanada Sagar College of

Engineering Bangalore.

Finally and most importantly, I wish to thank my family especially, my parents, elder brother

Mr. S. Byrappa and five elder sisters namely Smt. S. Nagaratna Shivaramaiha, Smt.

Kowstubhamani, Ms. Pushpa, Ms. Sridevi., Smt. Bharathi and younger brother Mr.

Yogananda who always surrounded me with stability, warmth, affection, their everlasting

love and unquestionable support.

Last but not least, I would like to thank the reviewers of our published papers, authors of the

literature related to this work.

M. S. Nagananda

vii

I dedicate this thesis to my parents, Sri A. N. Sanne Gowda, Freedom Fighter & Retd.

Teacher and Smt. Devamma for their constant support and unconditional love.

M. S. Nagananda

viii

ABSTRACT

Keywords: Pregnancy, Cognitive Dysfunctions, Stress, Anxiety, Depression, Psycho-

somatic Disorders, Non Linear Methods, Entropies, Dimensions, Exponents

Pregnancy is an important event in the life of every woman. She undergoes various

physiological as well as psychological alterations or adjustments during different stages of

pregnancy that are essential for the normal growth and development of a fetus. While

adjusting to such changes, majority of them suffer from some minor or major unwanted or

undesirable ailments that may at times lead to death. Therefore, there is a constant need to

innovate and develop newer, safer and affordable technologies to detect and diagnose such

unwanted physiological, bio-physical, postural, social, emotional and psychological

alterations. This could help medical care professionals in rendering better services to the

woman during pregnancy. It is essential to assess and monitor vital parameters throughout

the pregnancy. These parameters are interrelated. Most of the currently available state of the

art techniques that are available to assess psychological and cognitive changes provide useful

information, but many techniques are expensive, cumbersome and their sensitivity and

specificity are not sufficient in identifying cognitive dysfunctions. The benefits of various

biomedical devices need to percolate and diffuse into our society particularly for the welfare

of a woman during pregnancy. In this context it has been proposed to develop an affordable,

non-invasive integrated qualitative and quantitative solution to assess cognitive dysfunctions

during pregnancy for maternal well-being. This research work has been primarily undertaken

to:

(i) Develop qualitative techniques for identifying prospective bio-markers for

measuring cognitive dysfunctions during pregnancy.

ix

(ii) Establish the relationship of qualitative measures of psycho – cognitive

parameters with quantitative measures, and

(iii) Develop quantitative measures to identify cognitive dysfunctions during

pregnancy.

(iv) To explore relaxation techniques during pregnancy and to study the efficacy of

YOGA Relaxation Technique (YRT) in pregnant women.

Cognitive dysfunctions are usually characterized in terms of stress, frustration, memory loss,

loss of interest, motivation, attention, and drowsiness. The cognitive assessment has been

first measured using Structured Questionnaire Set (SQS) as a non invasive qualitative

measure. Quantitative measures have been then obtained from physiological signals such as

Heart Rate, Pulse Rate, Oxygen Saturation, Pulse Plesthesmography, Body Mass Index, and

Electroencephalograph to identify cognitive dysfunction. The relationships between

qualitative and quantitative measures have been established by using an appropriate statistical

measure.

SQS to measure cognitive dysfunctions among pregnant women has been primarily

developed in three stages. In the first stage: a comprehensive review of literature on the

variables falling under cognitive dysfunctions has been carried out. In second stage: for

developing the measures of cognitive dysfunctions well established scales such as Perceived

Stress Scale, Interpersonal Support Evaluation List, Prenatal Self Evaluation Questionnaire,

Beck’s Anxiety Inventory, Edinburg Depression Scale, and Index of Nausea and Retching

have been considered. The knowledge gained using all these scales has been utilized for the

purpose of developing qualitative measures. In third stage: quantitative measures using

physiological signals and nonlinear features of EEG have been used to extract the hidden

information from EEG signal to study cognitive dysfunctions.

x

For pregnant mother, experimental design has been used; data have been captured using pre-

post experimental research design. Both qualitative and quantitative measures have been

used for both experimental and control group. Experimental group consisting of 22 pregnant

women and control group consisting of 21 pregnant women have been considered in the

study. Intervention package of Yogic Relaxation Technique (YRT) has been carried out

starting from the day of registration till the delivery.

The salient features of the results obtained from the studies on pregnant women are as

follows:

1. The cognitive dysfunctions have been found to be significantly lower in the experimental

group as compared to control group.

2. The Alpha band, reflector of attention and concentration, increased significantly in the

experimental group as compared with control group. The increased Alpha band have been

found after YOGA Relaxation Technique (YRT)

3. Increased amplitude has been observed on Alpha bands in pregnant women undertaking

YRT.

4. There are close relationships between objective descriptors i.e. Quantitative Measures

[QTM] such as BMI, Heart Rate, SpO2, Blood Pressure, and EEG with subjective

perspectives i.e. Qualitative Measures [QLM] measured by Structured Questionnaire Set

(SQS).

5. Similar results were found even in case of non linear features of EEG such as entropies,

dimensions, and exponents.

6. Pearson’s Correlation coefficient has been used to find out the relationship between the

measures; the relationship has been highly significant and it is linear in nature. A very

close relationship has been evident in both qualitative measures and quantitative

measures.

xi

7. These findings are suggestive of broader application of qualitative measures in detecting

cognitive dysfunctions. Another salient feature which has emerged from the study was

the reliability of qualitative measures, which can broadly be utilized for a larger

population as an effective measure of assessing cognitive dysfunctions.

xii

TABLE OF CONTENTS

DETAILS PAGE NO.

CERTIFICATE ii

ACKNOWLEDGEMENTS iii

DEDICATION vii

ABSTRACT viii

TABLE OF CONTENTS xii

LIST OF FIGURES xvi

LIST OF TABLES xviii

ACRONYM xxi

CHAPTER 1 01 - 25

INTRODUCTION AND REVIEW OF THE LITERATURE

1.1. Introduction 01

1.2. Review of literature 02

1.3. Psychological disorders during pregnancy 06

1.4. Prevalence of cognitive dysfunction 08

1.5. Research gap 21

1.6. Research design 22

1.7. Research Objectives 23

1.8. Thesis Outcome 24

1.9. Organization of the Thesis 25

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CHAPTER 2 26 - 46

PRAGMATIC STUDIES ON PREGNANCY: QUALITATIVE ASSESSMENT

2.1. Introduction 26

2.2. Review of literature 27

2.3. SQS for pregnant women 32

2.4. Ethical guidelines for qualitative measures 33

2.5. Study design 34

2.6. Data collection 34

2.7. Results 36

2.8. Summary of qualitative assessment 46

CHAPTER 3

47 – 80

QUANTITATIVE TECHNIQUES FOR COGNITIVE ASSESSMENT

3.1. Introduction 47

3.2. Review of literature 47

3.3. Physiological parameters 55

3.4. Interventions 57

3.5. Experimental Protocol 58

3.6. Methodology and tools used 58

3.7. Data collections steps in pregnant women 61

3.8. Prototyping the EEG data acquisition system 70

3.9. Method and materials for portable EEG DAS 71

3.10. Instrumentation amplifier 72

3.11. Filter section 74

3.12. Power source 76

xiv

3.13. Portable data acquisition system 76

3.14. Summary of quantitative assessment 80

CHAPTER 4

81 – 137

INNOVATIVE TECHNIQUE FOR COGNITIVE ASSESSMENT IN

PREGNANCY USING EEG FEATURES

4.1. Introduction 81

4.2. Review of literature 81

4.3. Entropies as a measure of complexity 85

4.4. Dimensional complexity 100

4.5. Exponent analysis 111

4.6. Classifiers 121

4.7. Summary of EEG features from NLMs 137

CHAPTER 5

139 - 170

EFFICACY OF RELAXATION TECHNIQUES AND RESULTS

5.1. Introduction 139

5.2. Review of literature 140

5.3. Relaxation Techniques 142

5.4. Types of RT 146

5.5. Results 153

5.6. Statistical treatment of data 154

5.7. Results and observations during pregnancy 170

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CHAPTER 6 171 - 179

CONCLUSIONS AND FUTURE DIRECTIONS

6.1. Conclusions 171

6.2. Future scope 178

6.3. Recommendations 179

REFERENCES

180 – 203

APPENDIX - A 204 – 227

APPENDIX - B 228 – 246

APPENDIX - C 247 – 281

APPENDIX - D 282 - 352

PUBLICATIONS 353 - 355

BIO DATA 356 - 356

xvi

LIST OF FIGURES

Figure 1.1: Postural changes during Pregnancy

15

Figure 1.2: Pelvic Girdle Pain (PGP) and Anatomy of pelvis 15

Figure 1.3: Symptoms of PGP 16

Figure 14: Recommended sleeping and relaxation postures during pregnancy 16

Figure 1.5: Weight lifting during pregnancy 17

Figure 1.6: Management of muscle cramp 17

Figure 1.7: Correcting upright postures during pregnancy 18

Figure 3.1: Yoga Postures during Pregnancy

54

Figure 3.2: Physiological Data Acquisition System 56

Figure 3.3 Vital physiological parameters 56

Figure 3.4: standard International 10 – 20 system of EEG electrode placement 57

Figure 3.5 EEG Bands 59

Figure: 3.6. Images of physiological data collection 60

Figure: 3.7. Block diagram of the integrated EEG system 70

Figure: 3.8. Schematics of the EEG Amplifier 72

Figure: 3.9. Circuit diagram of the Instrumentation Amplifier 72

Figure: 3.10. Output of the Instrumentation Amplifier 74

Figure: 3.11. Circuit diagram of the Drive Right Leg Circuit 74

Figure: 3.12. Circuit diagram of the filter section 75

Figure: 3.13. Frequency response of the filter section 78

Figure: 3.14. Output if 100 mV is given to the system 78

Figure: 3.15. Noise analysis using AD620 78

Figure: 3.16. PCB of single channel EEG DAS 79

xvii

Figure 3.17: Portable EEG data acquisition system with laptop 79

Figure 4.1: Nonlinear EEG feature extraction 85

Figure 4.2 Flow chart of approximate entropy 88

Figure 4.3: Flow chart to compute sample entropy 96

Figure 4.4: Flow chart to compute K entropy 100

Figure 4.5: Flow chart to compute correlation dimension 103

Figure 4.6: Flow chart to compute fractal dimension 108

Figure 4.7: Flow chart to compute Lyapunov Exponent 113

Figure 4.8: Flow chart to compute Hurst Exponent 118

Figure 4.9: Flow chart to K-means Algorithm 122

Figure 5.1: Subject details of the Study 157

Figure 5.2: Qualitative and Quantitative analysis in pregnant women 158

Figure 5.3: Subject details for without intervention 161

Figure 5.4: Subject details for within intervention 162

Figure 5.5: Approximation Entropy details 165

Figure 5.6: Sample Entropy 165

Figure 5.7: KS Entropy 166

Figure 5.8: Largest Lyapunav exponent 166

Figure 5.9: Hurst exponent 167

Figure 5.10: Delta band 167

Figure 5.11: Theta band 168

Figure 5.12: Beta 1 band 168

Figure 5.13: Beta 2 band 169

Figure 5.14: Beta Hf band 169

xviii

LIST OF TABLES

Table 2.1: Background details of pregnant women 39

Table 2.2: Responses (Scores) of Pregnant Women for SQS 40

Table 2.3: t-test for equality means 41

Table 2.4: Independent Sample test 42

Table 2.5: Paired sample test between CG and EG 43

Table 2.6: paired differences between CG and EG 43

Table 2.7: Normalized Responses of Pregnant Women for SQS 44

Table 2.8: Summary of Qualitative Measures 45

Table 3.1: Quantitative Measures using Physiological Parameters 61

Table 3.2: Heart rate, Pulse rate, SpO2, BP, BMI 62

Table 3.3: Summary of physiological parameters 64

Table 3.4: Summary of frequency components (Bands) 65

Table 3.5: Physiological Parameters for control group (n = 21) 66

Table 3.6: Physiological Parameters for experimental group (n = 22) 67

Table 3.7: Frequency Components (Bands)_CG 68

Table 3.8: Frequency Components (Bands)_EG 69

Table 4.1: Approximate Entropy (n = 21)_Control Group 92

Table 4.2: Approximate Entropy (n = 22)_EG 93

Table 4.3: Sample Entropy (n = 21) 94

Table 4.4: Sample Entropy (n = 22) 95

Table 4.5: KS Entropy (n = 21) 98

Table 4.6: KS Entropy (n = 22) 99

xix

Table 4.7: Correlation Dimension (n = 21) 104

Table 4.8: Correlation Dimension (n = 22) 105

Table 4.9: Fractal Dimension (n = 21) 109

Table 4.10: Fractal Dimension (n = 22) 110

Table 4.11: Largest Lyapunav Exponent (n = 21) 114

Table 4.12: Largest Lyapunav Exponent (n = 22) 115

Table 4.13: Hurst Exponent (n = 21) 119

Table 4.14: Hurst Exponent (n = 22) 120

Table 4.15: Study group design 127

Table 4.16: LDA and NB Classifier’s details of AP_EN and SP_EN 128

Table 4.17: LDA and NB Classifier’s details of KS_EN and CD

129

Table 4.18: LDA and NB Classifier’s details of FD and HE

130

Table 4.19: LDA and NB Classifier’s details of LLE and EEG Bands

131

Table 4.20: LDA and NB Classifier’s details of QLM and QPHY

132

Table 4.21: Ranges of parameter used in classification

133

Table 4.22: Accuracy, Sensitivity and Specificity of classifiers

134

Table 5.1: Subject clustering 158

Table 5.2: Comparison between groups 159

Table 5.3: Correlations between QLM, QTM-I, QTM-II 160

Table 5.4: Statistically significant parameters 163

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ACRONYM

HR Heart Rate PGP Pelvic Girdle Pain

PR Pulse Rate MMR Maternal Mortality Ratio

SpO2 Oxygen Saturation PPC Psycho-Physiological Changes

PPG Plesthesmography SQS Structured Questionnaire Set

BMI Body Mass Index AD Activities of Daily Life

EEG Electroencephalogram PSS Perceived Stress Scale

SpEn Sample Entropy IPSEL Interpersonal Support Evaluation List

ApEn Approximation Entropy PSEQ Prenatal Self Evaluation Questionnaire

K Kolmogorov-Sinai Entropy BAI The Beck’s Anxiety Inventory

FD Fractal Dimension WA Water Availability

D2 Correlation Dimension VN Ventilation

LLE Largest Lyapunov Exponent BA Bathing Accident

H Hurst Exponent ME Means of Escape

hPL Human Placental Lactogen FD Financial Dependent

LH Luteinizing Hormone DO Depending on Others

FSH Follicle Stimulating Hormone PD Partially Dependent

CRH Corticotrophin BP Blood pressure

ACTH Adreno-Corticotrophin ES Eye Sight

IN Insomnia SLS Short and Long Sight

SI Sacroiliac Joints RMBC Reduced Mind-Body Coordination

BPC Body Posture Change M MALE

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ACRONYM CONTD…

MD Multiple Disorders EQ Equation

F Female RGS Reduced Grip and Stability

RGS Reduced Grip and Stability RMS Reduced Muscular Strength

SE Sharp Edges HPF high-pass Filter

OB Obstacle HPF Low Pass Filter

LT Lighting NLM Non-Linear Methods

RT Relaxation Technique DRL Drive Right Leg

NA Not Applicable CG Control Group

ND Not Dependent EG Experimental Group

WHO World Health Organization NMMC Navy Mumbai Municipal

Corporation

Hcg Human Chorionic Gonadotrophin YRT Yogic Relaxation Technique

QLM Qualitative Measures QTM Quantitative Measures

GST Gamma State GS GARBHA SANSKAR

SAD Stress, Anxiety, and Depression

RMANOVA Repeat Measure Analysis of Variance

EPDS The Edinburgh Postnatal Depression Scale

INVR Index Of Nausea, Vomiting and Retching

CDQM Cognitive Dysfunction Qualitative Measures

SAP Senescence, Adolescence, and Pregnancy