Table Of ContentAnalysis of Medical Modalities
for Improved Diagnosis in
Modern Healthcare
Analysis of Medical Modalities
for Improved Diagnosis in
Modern Healthcare
Edited by
Varun Bajaj
G. R. Sinha
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Dedicated to my late father Mahendra Bajaj and family members.
Varun Bajaj
Dedicated to my late grand parents, my teachers
and Revered Swami Vivekananda
G. R. Sinha
Contents
Preface.......................................................................................................................ix
Acknowledgments ..................................................................................................xiii
Editors ......................................................................................................................xv
Contributors ...........................................................................................................xvii
Chapter 1 Classification of Alertness and Drowsiness States Using the
Complex Wavelet Transform-Based Approach for EEG Records ........1
Sachin Taran, Ravi, Smith K. Khare, Varun Bajaj,
and G. R. Sinha
Chapter 2 Stochastic Event Synchrony Based on a Modified Sparse
Bump Modeling: Application to PTSD EEG Signals ........................17
Zahra Ghanbari and Mohammad Hassan Moradi
Chapter 3 HealFavor: A Chatbot Application in Healthcare ..............................41
Sivaji Bandyopadhyay, Abdullah Faiz Ur Rahman Khilji,
Sahinur Rahman Laskar, Partha Pakray, Rabiah Abdul Kadir,
and Maya Silvi Lydia
Chapter 4 Diagnosis of Neuromuscular Disorders Using Machine
Learning Techniques ..........................................................................63
Abdulhamit Subasi
Chapter 5 Prosthesis Control Using Undersampled Surface
Electromyographic Signals .................................................................89
Hamid Reza Marateb, Farzad Ziaie Nezhad,
Marjan Nosouhi, Zahra Nasr Esfahani, Farzaneh Fazilati,
Fatemeh Yusefi, Golnaz Amiri, Negar Maleki Far,
Mohsen Rastegari, Mohammad Reza Mohebbian,
Khan A. Wahid, Mislav Jordanić, Joan Francesc Alonso López,
Miguel Ángel Mañanas Villanueva, and Marjan Mansourian
Chapter 6 Assessment and Diagnostic Methods for Coronavirus
Disease 2019 (COVID-19) ................................................................113
M. B. Malarvili and Alexie Mushikiwabeza
vii
viii Contents
Chapter 7 Predictive Analysis of Breast Cancer Using Infrared Images
with Machine Learning Algorithms ................................................133
Aayesha Hakim and R. N. Awale
Chapter 8 Histopathological Image Analysis and Classification
Techniques for Breast Cancer Detection ..........................................161
Gaurav Makwana, Ram Narayan Yadav, and Lalita Gupta
Chapter 9 Study of Emotional Intelligence and Neuro-Fuzzy System .............193
Mohan Awasthy
Chapter 10 Essential Statistical Tools for Analysis of Brain
Computer Interface ...........................................................................209
K. A. Venkatesh and K. Mohanasundaram
Chapter 11 Brain Computer Interfaces: The Basics, State of the Art,
and Future.........................................................................................237
Muhamed Jishad. T. K and M. Sanjay
Chapter 12 Oriented Approaches for Brain Computing and Human
Behavior Computing Using Machine Learning ...............................271
Monali Gulhane and T. Sajana
Chapter 13 An Automated Diagnosis System for Cardiac
Arrhythmia Classification ................................................................301
Allam Jaya Prakash, Saunak Samantray, C. H. Laxmi Bala, and
Y. V. Narayana
Index ......................................................................................................................315
Preface
In modern healthcare, various medical modalities play an important role in improving
the diagnostic performance in healthcare systems for various applications, such as
prosthesis design, surgical implant design, diagnosis and prognosis, and detection of
abnormalities in the treatment of various diseases. This book also discusses the uses
of analysis, modeling, and manipulation of modalities, such as EEG, ECG, EMG,
PCG, COP, EOG, MRI, and FMRI for an automatic identification and classification
for diagnosis of any disorder and physiological states. For addressing a wide range
of modalities of medical imaging, their analysis and applications for post-processing
and diagnosis are much-needed topics for a number of researchers and faculty mem-
bers all across the world attempting to conduct research in this area.
Therefore, this book emphasizes the real-time challenges in medical modalities
for a variety of applications for analysis, classification, and identification of different
states for the improvement of healthcare systems. Each chapter starts with the intro-
duction, need and motivation of the medical modality, and a number of applications
for the identification and improvement of healthcare systems. Moreover, the chapters
can be read independently by research scholars, graduate students, faculty members,
and R&D engineers who wish to explore research in the field of computer sciences,
electronics, medical sciences, and biomedical engineering.
The chapter-wise description of this book as follows: Chapter 1 presents the dual-
tree complex wavelet transform (DTCWT)-based approach for the classification of
alertness and drowsiness states utilizing EEG signals. The DTCWT decomposes the
EEG signal into different band-limited sub-bands. The characteristics of DTCWT
provide sub-bands that are extracted in terms of several time-domain measures.
Further, the measures are tested over several machine learning algorithms to effec-
tively classify the two important states as alertness state and drowsiness state of EEG
signals. In Chapter 2, stochastic event synchrony (SES) is studied as an approach of
quantifying synchrony between two time series. The time-frequency transform of
each signal is approximated as a sum of half-ellipsoid basis functions, referred to as
“bumps.” Each bump is considered as an event on the time-frequency plane. Method
is applied to combat related post-traumatic stress disorder (PTSD) EEG signals, in
addition to healthy controls, and trauma-exposed none-PTSD veterans. EEG signals
are collected in two resting states (eyes-open and closed). In Chapter 3, the authors
have carried out an in-depth survey of all the prevalent and nonprevalent health-
care chatbot systems, and discussed in detail regarding a suitable dataset and an
underlying deep learning model for the said chatbot application. Chapter 4 presents
a framework composed of the three main modules. In the first module, EMG signals
are denoised by using MSPCA denoising technique. In the second module, the coef-
ficients of wavelet-based time-frequency methods are calculated for each category of
EMG signal, and then statistical values of each sub-band are computed. In the last
module, the extracted features are employed as an input to a classifier to diagnose
different neuromuscular disorders. In Chapter 5, the upper-limb prostheses and their
control are discussed. The results of some experiments are further provided showing
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