REAL TIME ELECTROENCEPHALOGRAM BASED

QUANTITATIVE ESTIMATION OF BALANCED

ANESTHESIA IN PATIENTS UNDERGOING

ORTHOPEDIC AND LAPAROSCOPIC SURGERY

by

SANJEEV KUMAR

Centre for Biomedical Engineering

Submitted

In fulfillment of the requirements of the degree of

DOCTOR OF PHILOSOPHY

to the

INDIAN INSTITUTE OF TECHNOLOGY DELHI DECEMBER 2012

Dedicated to my beloved

Parents for giving me the

Precious gift of education

CERTIFICATE

This is to certify that the thesis entitled “Real time Electroencephalogram

based Quantitative Estimation of Balanced Anesthesia in patients

undergoing Orthopedic and Laparoscopic surgery” being submitted by

Sanjeev Kumar to the Indian Institute of Technology Delhi, India for the award of

the degree of Doctor of Philosophy in Biomedical Engineering, is a record of

bonafide research work carried out by him under our supervision and guidance.

To the best of our knowledge the thesis has reached the requisite standard. The

results presented in this thesis have not been submitted in part or in full to any other

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

(Amod Kumar, Ph.D.) Chief Scientist

CSIR - Central Scientific Instruments Organisation Chandigarh 160030, INDIA.

(Sneh Anand, Ph.D.) Professor

Centre for Biomedical Engineering Indian Institute of Technology Delhi

New Delhi 110016, INDIA.

(Anjan Trikha, MBBS, DA, MD.) Professor

Department of Anesthesiology All India Institute of Medical Sciences

New Delhi 110029, INDIA.

i

ACKNOWLEDGEMENTS

Successful completion of a task is a fruit which everyone strives to taste. The

sweetness in that fruit is the result of efforts by many people who nurture it. I

consider my dissertation to be the fruit which has been nurtured by many

stalwarts over the years. I know I am breaking the rules to write these

acknowledgements conventionally, but then, I may not get a second opportunity

to express my feelings again. So, let the words come out from my heart.

Before I express my heart-felt gratitude towards all my mentors, first and

foremost, I would like to thank my greatest teacher and of course of all: God. He

has given me the power to believe in myself and my passion to pursue this study.

I could never have done this without His blessings.

I take great pleasure in expressing my deep sense of gratitude and indebtedness

towards my supervisor – Professor Sneh Anand for her untiring support and

guidance in different stages of this work. Her way of giving me the solution to

each and every problem by just closing her eyes for a moment, whenever, I went

to her was like a divine transfer of her knowledge into my mind. I can never forget

those moments and my vocabulary deceives me to express my feelings for her.

I would like to thank Professor Anjan Trikha, my co-supervisor, for supporting

me in all situations during the dissertation. He always said “don’t worry, come to

me with problems, I have the solutions” with a cool, calm and refreshing smile on

his face. He was always a stress-buster to me and I was at my wits’ end to

understand how he facilitated me in the operation theatre during all data

collection and guidance despite being on toes due to excessive load and

responsibilities. He is a gem of a person.

I would like to express my warm and sincere gratitude to Dr Amod Kumar, my

co-supervisor who always left me wondering whether he was a guide or an elder

brother or a friend to me whom I admire a lot. He always supported me

throughout the work by motivating me from time to time. It would have been very

difficult for me to come out with results without his invaluable suggestions.

ii

I am thankful to Professor Satinder Gombar for helping me in all respects

during data collection and mentoring me during the process of writing research

articles.

I am thankful to my seniors Dr. Nand Kumar, Dr. Tamalika and my colleagues

Dr. Deepak, Dr. Tapan, Vijay, Sharvan, Sonal, Perisamy, Nagananda,

Chhaya, Ramandeep, Jean and Amit for making my stay and visits to IIT Delhi

to be cherished forever.

My special thanks to Sumita, Anil Pandey, Rajesh and other official staffs for

helping me in all the logistic issues during my research tenure at IIT Delhi.

I am thankful to my parents for supporting me in my education and motivating

me whenever I showed signs of losing my patience. I am thankful to my fiancée

Indu, who always believed in my promises to share my free evenings with her

being fully aware that all these promises were a bundle of lies. I hope that I can

fulfill those promises now.

I am thankful to my seniors in my lab Mrs. Shashi Sharma, Mrs. Sudesh

Bachhal, Amit and my students Reeta, Sumit, Sukhwinder, Atul, Ashwath,

Harikanth, Prashanth, Divya, Raj, Aditya, Mythri, Puneet, Karanjeet, Ravish,

Monika, Ritu and Kunal who were with me during data collection, parameter

extraction, analysis and making the GUI. Their footprints will remain tattooed in

my heart forever.

I would like to thank the authors of the literature I have gone through without

which it would have been impossible for me to carry out this exciting research.

Although I have tried to express my gratitude to every person who

contributed in this work directly or indirectly, there may still be someone

hiding behind the veils of my forgetful part of memory. Last but not the least; I

would like to thank all such souls.

(Sanjeev Kumar)

iii

ABSTRACT Keywords: General Anesthesia, Electroencephalogram, Step Discriminant

Analysis, Prediction Probability, Principle Component Analysis, Fractal

Dimension, Hypnosis index, Analgesia index, Balanced Anesthesia.

While administering General Anesthesia (GA), it is essential to maintain adequate

Depth of Anesthesia (DoA) to avoid intra operative awareness. This is usually

carried out by subjective clinical signs or by utilizing recently available

consciousness monitors like BIS, Entropy, Auditory Evoked Potential (AEP) and

Narcotrend Monitor. Conventional subjective methods of assessing anesthetic

depth involve monitoring of several clinical surrogate markers which are not fully

reliable. Patient awareness during surgery with anesthetic agents has been

reported time and again. Patient’s safety is an important aspect for which optimum

control of anesthetic depth is of paramount importance. This requires balanced

control of all the four components of GA viz hypnosis, analgesia, amnesia and

neuromuscular blockade. Electroencephalogram (EEG), having a non-linear

relationship with anesthetic depth, has been found to be a reliable means to

determine the real anesthetic state of patients during surgery. In the recent past,

several EEG based scores as discussed above become available for assessing DoA

objectively. As yet, a universally accepted EEG index for DoA is not available as all

these scores reflect only hypnosis component of GA while providing no information

about the pain experienced by the patient.

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The present study was initiated to record EEG from various regions of the brain

during varying levels of consciousness and in states on induced pain or surgical

pain, extracting features from it and to correlate with the hypnosis and analgesia

components of GA to enable quantitative assessment indicating the level of

unconsciousness and perception of pain respectively.

All necessary approvals from the hospital’s ethics committee and informed written

consent from volunteers/patients were obtained. The study was conducted on

humans (10 volunteers for laboratory study and 31 patients undergoing elective

orthopedic or laparoscopic surgery). EEG electrodes were placed at different regions

of brain as per the standard 10-20 EEG electrode system. Electrodes for other

physiological and BIS monitoring were placed as per standard practice. EEG data,

other physiological parameters, BIS values and Visual Analogue Scale (VAS) score

of pain perception were obtained in three different phases: baseline EEG in

conscious relaxed state (normal conscious state with eyes closed), unconscious

state (in case of volunteer: normal sleep and in case of patient: anesthetic sleep)

and during the state of pain (in case of volunteer: induced experimental pain and for

patient: post surgical pain).

Identified EEG parameters in Time, Frequency, Wavelet and Bi-spectral domain

were calculated for different regions of brain for each phase. Correlation of

parameters was done for both hypnosis and analgesia components. Best set of

parameters for hypnosis and analgesia was identified by pruning of parameters.

v

Classification of different epochs was done using a Feed-forward Artificial Neural

Network (ANN) with Back-propagation learning algorithm. Best sets of parameters

were used further to compute graded values for hypnosis and analgesia indices

using Fuzzy Logic approach.

Hypnosis index was estimated on a scale of 0-100 where 0 denote flat line EEG and

100 corresponds to fully conscious state. The index was compared with the

standard BIS values. Analgesia index was quantified on a range of 0-10 where 0 and

10 denotes ‘no pain’ and ‘extreme pain’ respectively. The index was benchmarked

with the reported VAS score.

Variance, co-variance and probability of test statistics (p-value) were calculated for

hypnosis and analgesia indices and significant correlation was found when

compared with the standards values.

vi

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

ABSTRACT iii

TABLE OF CONTENTS vi

LIST OF FIGURES xi

LIST OF TABLES xv

ACRONYM xvii

Chapter 1: INTRODUCTION AND LITERATURE SURVEY 1 - 33 1.1 BACKGROUND 1 1.2 TYPES OF ANESTHESIA 2 1.3 PHASES OF GENERAL ANESTHESIA 3 1.4 ASSESSMENT OF DEPTH OF ANESTHESIA (DoA) 5

1.4.1 Assessment of DoA By Subjective Methods 5

1.4.1.1 Monitoring of Autonomic response 6

1.4.1.2 Patient Response to Surgical Stimulus (PRST Score 7

1.4.1.3 Isolated Forearm Technique (IFT) 8

1.4.2 Objective Assessment of DoA 9

1.4.2.1 Spontaneous Surface Electromyogram (SEMG) 9

1.4.2.2 Lower Esophageal Contractility (LOC) 9

1.4.2.3 Heart Rate Variability (HRV) 10

1.4.2.4 Electroencephalogram and Derived Indices 10

a) Compressed Spectral Array

b) Bispectral Index (BIS) Monitor

c) Entropy Monitor

d) Narcotrend Monitor

e) Patient State Index (PSI)

f) Snap Index

g) Cerebral State Index (CSI)

vii

1.4.2.5 Auditory Evoked Potentials (AEP) Monitor 15

a) Somatosensory evoked potential (SEP)

b) Visual evoked potentials (VEP)

c) Auditory evoked potential (AEP)

1.5 EEG PARAMETERS AND THEIR CORELATION WITH DOA 21

1.5.1 Time Domain Parameters 21

1.5.1.1 Root Mean Square Value (Vrms) 21

1.5.1.2 Burst Separation Ratio (BSR) 22

1.5.1.3 Activity, Mobility & Complexity (Hjorth Parameters) 23

1.5.1.4 Approximate Entropy (ApEn) 24

1.5.1.5 Average Frequency 24

1.5.1.6 Lempel Ziv (LZ) Complexity 24

1.5.1.7 Higuchi Fractal Dimension (HFD) 25

1.5.2 Frequency Domain Parameters 26

1.5.2.1 Total Power of the Signal 26

1.5.2.2 Band-Powers 26

1.5.2.3 - Ratio 26

1.5.2.4 - Ratio 27

1.5.2.5 to (BA) -Ratio 27

1.5.2.6 Median Power Frequency (MF) 27

1.5.2.7 Spectral Edge Frequency (SEF) 28

1.5.2.8 Spectral Entropy (SpEn) 28

1.5.2.9 Modified Cumulative Power Spectrum (MCPS) 29

1.5.3 Time-Frequency Domain Parameters 29

1.5.3.1 Wavelet Probability Density Function 29

1.5.4 Bispectral Parameters 29

1.5.4.1 Synchronous Fast Slow (SFS) and Bispectral Index (BIS) 29

1.6 RESEARCH AIM & OBJECTIVES 30 1.7 OVERVIEW OF THE THESIS 32

viii

Chapter 2: RESEARCH PROTOCOLS 34 - 41 2.1 EEG RECORDING 34

2.1.1 Group I : Study on Volunteers 35

2.1.2 Group II : Study on Patients 38

2.2 SPECIAL PRECAUTIONS 40

Chapter 3: EEG PARAMETERS EXTRACTION, CORRELATION

WITH COMPONENTS OF ANESTHESIA, ANALYSIS AND ESTIMATION OF INDICES 42 - 82

3.1 EEG SIGNAL PROCESSING 43

3.1.1 Data Segmentation 44

3.1.2 EEG Artifacts and their removal 46

3.1.2.1 Removal of EMG Artifact 47

3.1.2.2 Removal of eye-blink Artifact 48

3.1.2.3 Removal of 50 Hz Power Line Artifact 48

3.2 EEG PARAMETERS EXTRACTION 51

3.3 NORMALIZATION OF EEG PARAMETERS 51

3.4 PRUNING OF PARAMETERS 54

3.4.1 Step–Wise Discriminant Analysis (SDA) 54

3.4.1.1 ANOVA (Analysis of Variance) 56

3.4.1.2 MANOVA (Multivariate Analysis of Variance) 56

3.4.1.3 Discriminant Function Analysis (DFA) 56

3.4.2 Score Graph Technique 57

3.4.3 Prediction Probability 58

3.5 CORRELATION OF EEG PARAMETERS WITH COMPONENTS OF 59

ANESTHESIA

3.5.1 Correlation of EEG Parameters with Hypnosis 60

3.5.1.1 Prediction of Conscious/Unconscious State of 60

Patients by Neural Network Using SDA Parameters

3.5.1.2 Prediction of Conscious/Unconscious State of 63

ix

Patients by Neural Network Using Score Graph

Parameters

3.5.1.3 Principal Component Analysis (PCA) 64

3.5.1.4 Prediction of Conscious/Unconscious State of 69

Patients by Neural Network Using PCA Parameters

Parameters

3.5.1.5 Justification of Selected EEG Parameter of EEG by 71

Prediction Probability for the prediction of hypnosis

state Parameters

3.5.1.6 Hypnosis index calculation by Fuzzy Logic 71

3.5.1.7 Higuchi Fractal Dimension (HFD) of Frontal region EEG 73

3.5.1.8 Modified Cumulative Power Spectrum (MCPS) of EEG 74

for the prediction of conscious and unconscious state

of the patient

3.5.1.9 Automatic Estimation of hypnosis drug based on the 75

hypnosis index

3.5.2 Correlation of EEG Parameters with Analgesia 77

3.5.2.1 Prediction of pain experience by the patient using 78

Neural Network with SDA Parameters

3.5.2.2 Prediction of pain experience by the patient using 79

Neural Network with Parameters by Score Graph

Technique

3.5.2.3 Justification of Selected EEG Parameter for the 79

prediction of analgesic state by Prediction Probability

3.5.2.4 Calculation of Analgesia index by Fuzzy Logic 80

3.5.2.5 Higuchi Fractal Dimension (HFD) of parietal region EEG 82

Chapter 4: RESULTS AND DISCUSSIONS 83 - 99

4.1 HYPNOSIS INDEX 83

4.1.1 Assessment of Hypnosis State by Fractal Dimension 88

x

4.1.2 Assessment of Hypnosis State by Modified Cumulative Power 89

Spectrum (MCPS)

4.1.3 Graphical User Interface for Hypnosis Index 91

4.1.4 Automatic Estimation of Hypnosis Drug 91

4.2 ANALGESIA INDEX 92

4.2.1 Assessment of Pain State by Fractal Dimension 97

4.2.2 Graphical User Interface for Analgesia Index 98

4.3 COMBINED MONITOR FOR HYPNOSIS AND ANALGESIA 99

MONITORING

Chapter 5: CONCLUSION AND FUTURE 100 – 106 SCOPE

5.1 FINDINGS 100

5.1.1 Hypnosis 100

5.1.2 Analgesia 102

5.2 IMPLICATIONS OF THE PROPOSED TECHNIQUE 103

5.2.1 Flexibility to Providers/Anesthesiologists 103

5.2.2 Healthcare System 104

5.2.3 Payers 104

5.3 LIMITATIONS OF THE STUDY 104

5.3.1 Small Sample Size 105

5.3.2 Ethical Issues 105

5.4 FUTURE SCOPE 105

REFERENCES 107

ANNEXURE 123

LIST OF PUBLICATIONS BASED ON THE THESIS 149

BIODATA 152