Computational Intelligence and Biomedical Signal Processing: An Interdisciplinary, Easy and Practical Approach (SpringerBriefs in Electrical and Computer Engineering)

✍ Scribed by Mitul Kumar Ahirwal

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 167
• Category: Library

✦ Synopsis

This book presents an interdisciplinary paradigms of computational intelligence techniques and biomedical signal processing. The computational intelligence techniques outlined in the book will help to develop various ways to enhance and utilize signal processing algorithms in the field of biomedical signal processing. In this book, authors have discussed research, discoveries and innovations in computational intelligence, signal processing, and biomedical engineering that will be beneficial to engineers working in the field of health care systems. The book provides fundamental and initial level theory and implementation tools, so that readers can quickly start their research in these interdisciplinary domains.

✦ Table of Contents

Foreword
Preface
Acknowledgments
Contents
List of Figures
List of Tables
About the Authors
Chapter 1: Biomedical Signals
1.1 Introduction
1.2 Basics of Digital Signal Processing
1.2.1 Signals
1.2.2 Digital Signals
1.2.3 Digital Filters
1.3 Electrocardiogram (ECG) Signal
1.3.1 ECG Terminology and Recording
1.3.2 ECG Processing
1.3.3 Common Problems
1.3.4 ECG Applications
1.4 Electroencephalogram (EEG) Signal
1.4.1 Basic Terminology and Recording
1.4.2 EEG Processing
1.4.3 Common Problems
1.4.4 EEG Applications
References
Chapter 2: Fundamentals of Adaptive Filters
2.1 Introduction
2.1.1 Why to Use Adaptive Filters
2.1.2 Models of Adaptive Filters
2.1.3 Algorithms for Adaptive Filter
2.1.3.1 Least Mean Square (LMS) Algorithm
2.1.3.2 Normalized Least Mean Square (NLMS) Algorithm
2.1.3.3 Example Cases
2.1.3.4 Fidelity Parameters for Observation
2.2 Application of Adaptive Filter over EEG Signals
2.2.1 EEG Dataset
2.2.2 Adaptive Filter Model for EEG
2.2.3 Observation
2.3 Application of Adaptive Filter over ECG Signals
2.3.1 ECG Dataset
2.3.2 Adaptive Filter Model for ECG
2.3.3 Observation
2.4 Adaptive Filter as Optimization Problem
References
Chapter 3: Swarm and Evolutionary Optimization Algorithm
3.1 Introduction
3.2 Basics of Optimization Problem
3.2.1 Types of Optimization Problems
3.2.2 Classification of Optimization Problems
3.2.3 Classification of Algorithms
3.3 Swarm Intelligence/Evolutionary Techniques
3.3.1 Extract and Common Procedure in SI/ET
3.3.2 Genetic Algorithm
3.3.3 Artificial Bee Colony Algorithm
3.3.4 Particle Swarm Optimization Algorithm
3.4 Objective Functions
3.5 Implementation of Real Genetic Algorithm
3.5.1 Visualization of Chromosome over Plot of Objective Function
3.5.2 Performance Measure of Optimization Algorithm
3.5.3 Simple Modification in Real GA
3.6 Application to Adaptive Filters
References
Chapter 4: Prediction and Classification
4.1 Introduction
4.2 Prediction and Regression
4.2.1 Simple Linear Regression
4.2.2 Linear Regression Using Gradient Descent
4.2.3 Multiple Linear Regression
4.3 Classification
4.3.1 Logistic Regression
4.3.2 ECG Classification Using Logistic Regression
4.3.2.1 ECG Feature Extraction
4.3.2.2 ECG Classification
4.4 Basics of Artificial Neural Networks (ANN)
4.4.1 ANN Architecture and Its Training
4.4.2 Activation Functions
4.4.3 Multiclass Classification
4.5 Some Useful Tips
References
Chapter 5: Basics of Matlab Programming
5.1 MatLab Programming Basics
5.1.1 Calculations in Command Window
5.1.2 Arithmetic Operators
5.1.3 Managing the Workspace and Miscellaneous Commands
5.1.4 Mathematical Functions and Predefined Constants
5.1.5 Working with Matrix
5.1.6 Figure Plotting
5.1.7 Steps in MatLab Programming
5.2 Conditional Statements and Loops
5.3 Fundamental Concepts of Programming for Signal/Image Processing
5.3.1 Load and Plotting of 1D Data (Signals)
5.3.2 Filtering over Data
5.3.3 Loading and Plotting 2D Data (Images)
5.3.4 Load and Display Image
5.3.5 Filtering of Images
References
Index