This book describes the sources and characteristics of different biopotential signals and provides an understanding of how a range of signals can be modelled and analysed for a number of applications. It is a valuable guide for all researchers and practitioners who are engaged in studies and research in this area.

Biopotential signals are often used by physicians to measure the activities of organs and tissues in the human body. This book describes the sources and characteristics of different biopotential signals and provides an understanding of how a range of signals can be modelled and analysed. The resulting information can be used to assist in the identification of disorders such as epilepsy, schizophrenia, PTSD and heart disease, among others. An emphasis is placed on the real challenges in biopotential signal processing due to the complex and non-stationary nature of signals.

Following on from volume one, this book starts with a collection of chapters covering some of the latest developments in electroencephalography (EEG) signal analysis, then moves on to applications of electrocardiography (ECG) and otoscope signals. The volume concludes with a discussion of other monitoring techniques. The chapters include biomedical examples and discussions of how each method can be used to study human organs. It is a valuable guide for all researchers and practitioners who are engaged in studies and research in the area of biomedical signals and their applications.

Key Features

• Modelling and acquisition of biomedical signals for different disorders
• Implementation of methodologies and their impact on different cases
• Case studies and research directions
• Design and simulation examples

Preface
Acknowledgements
Editor biographies
Contributors
Chapter 1 - Classification of motor-imagery tasks from EEG Signals using the rational dilation wavelet transform
Chapter 2 - A deep learning framework for emotion recognition using improved time-frequency image analysis of electroencephalography signals
Chapter 3 - Multivariate phase synchrony based on fuzzy statistics: application to PTSD EEG signals
Chapter 4 - A study on the influence of meditation and music therapy on the vital parameters of the human body through EEG signal analysis: a review
Chapter 5 - Cross-wavelet transform aided focal and non-focal electroencephalography signal classification employing deep feature extraction Chapter 6 - Local binary pattern based feature extraction and machine learning for epileptic seizure prediction and detection Chapter 7 - Increasing the usability of the Devanagari script input based P300 speller Chapter 8 - A comprehensive review of the fabrication and performance evaluation of dry electrodes for long-term ECG monitoring Chapter 9 - Effective cardiac health diagnosis using event-driven ECG processing with subband feature extraction and machine learning techniques Chapter 10 - Analysis of heart patients using a tree based ensemble model
Chapter 11 - Heartbeat classification using parametric and time–frequency methods Chapter 12 - Segmentation of ECG waves using LSTM networks
Chapter 13 - Deep convolutional neural network based diagnosis of COVID-19 using x-ray images Chapter 14 - Otitis media diagnosis model for tympanic membrane images processed in two-stage processing blocks
Chapter 15 - Modelling and analysis for active infrared thermography for breast cancer screening Chapter 16 - Photoacoustic microscopy: fundamentals, instrumentation and applications Chapter 17 - Rigorous performance assessment of computer-aided medical diagnosis and prognosis systems: a biostatistical perspective on data mining