High Performance and Power Efficient Electrocardiogram Detectors

✍ Scribed by Ashish Kumar, Manjeet Kumar, Rama S. Komaragiri

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 205
• Series: Energy Systems in Electrical Engineering
• Edition: 1
• Category: Library

✦ Synopsis

This book details the characteristics of an ECG signal through the functionality and electrical activity of the human heart. This book provides a basic introduction and needs for developing implantable cardiac pacemaker systems. This book provides comprehensive details on ECG signal processing techniques that are useful for fast and accurate diagnosis of cardiovascular diseases. The book discusses the characteristics and parameters of a typical ECG signal and various noises that can corrupt an ECG signal. It also covers various challenges involved in different stages of signal acquisition, preprocessing, and detection of an ECG signal. The book also presents a detailed survey of various ECG signal detection and data compression techniques. The book contains detailed information on ECG signals and various noises that corrupt an ECG signal. It also includes de-noising techniques, ECG peak detection techniques, and ECG data compression techniques. It also includes step-by-step details to design various filters in MATLAB. This book, through detailed explanations, provides the reader with necessary information on ECG signal, ECG signal acquisition process, noise removal techniques, and the detection of ECG peaks.

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Authors
1 Introduction
1.1 Development of Implantable Cardiac Pacemaker System
1.2 Need and Motivation
1.3 Identifying the Research Problem
1.4 Introduction to Electrocardiography
1.4.1 Basic Introduction to Heart
1.4.2 Functions of the Human Heart
1.4.3 Overview of Implantable Cardiac Pacemaker System
1.4.4 Electrocardiogram Signal Characteristics
1.4.5 Parameters of ECG Signal
1.4.6 Common Noises in an ECG Signal
References
2 Existing Methods to Evaluate Pacemaker Device Performance
2.1 Algorithmic Structures of Different ECG Detection and Data Compression Techniques
2.2 Databases to Benchmark ECG Detection Algorithm
2.3 Evaluation and Comparison of ECG Detection and Data Compression Techniques
2.4 Discussion: Challenges and Gaps
2.5 Summary
References
3 ECG Signal Denoising Techniques for Cardiac Pacemaker Systems
3.1 ECG Signal Denoising
3.1.1 Criterion to Select Wavelet Transform for ECG Signal Analysis
3.1.2 Criterion for Selecting Wavelet Filter Bank Architecture
3.1.3 Simulation Results and Performance Evaluation of the Proposed Modified 3.1 Wavelet Transform-Based Wavelet Filter Bank
3.2 Demand-Based Wavelet Filter Bank
3.2.1 Criterion to Select Wavelet Decomposition Level
3.2.2 Wavelet Thresholding Techniques
3.2.3 Simulation Results and Performance Evaluation of the Proposed Demand-Based Wavelet Filter Bank
3.3 Summary
References
4 ECG Signal Detection and Lossless Data Compression Techniques for Implantable Cardiac Pacemaker Systems
4.1 ECG Signal Detection
4.1.1 Simulation Results and Performance Evaluation of the Proposed Soft-Thresholding-Based QRS-Complex Detection Technique
4.1.2 Dynamic Dual Thresholding-Based ECG Signal Detection
4.1.3 Simulation Results and Performance Evaluation of the Proposed Dynamic Dual Thresholding-Based ECG Signal Detection Technique
4.1.4 Adaptive Thresholding-Based ECG Signal Detection Technique
4.1.5 Simulation Results and Performance Evaluation of the Proposed Adaptive Thresholding-Based ECG Signal Detection Technique
4.2 Lossless Data Compression
4.2.1 Simulation Results and Performance Evaluation of the Proposed RLE-Based Lossless Data Compression Technique
4.2.2 LZMA-Based Lossless Data Compression Technique
4.2.3 Simulation Results and Performance Evaluation of the Proposed LZMA Lossless ECG Data Compression Technique
4.2.4 Biorthogonal 3.1 Wavelet Transform-Based Lossless ECG Data Compression Technique
4.2.5 Simulation Results and Performance Evaluation of the Proposed Biorthogonal 3.1 Wavelet Transform-Based Lossless ECG Data Compression Technique
4.3 Three-Tap Wavelet Filter Bank-Based Lossless ECG Data Compression Technique
4.3.1 Simulation Results and Performance Evaluation of the Proposed Three-Tap Wavelet Filter Bank Based on Lossless ECG Data Compression Technique
4.4 Summary
References
5 FPGA Implementation of Combined ECG Signal Denoising, Peak Detection Technique for Cardiac Pacemaker Systems
5.1 FPGA Implementation of an ECG Signal Detection Technique
5.2 Selection of Wavelet Transform
5.2.1 Energy and Shannon Entropy
5.2.2 Mutual Information and Relative Entropy
5.2.3 Cross-Correlation
5.2.4 Minimum Description Length (MDL)
5.3 Selection of Wavelet Filter Bank Architecture
5.4 ECG Signal Detection
5.5 Simulation Results
5.5.1 Input ECG Data
5.5.2 ECG Signal Denoising
5.5.3 ECG Signal Detection
5.6 Implementation of the ECG Signal Detector on FPGA
5.7 Summary
References
6 Digital ECG Signal Watermarking and Compression
6.1 Basics of ECG Signal Watermarking and Compression
6.2 ECG Signal Watermarking and Compression Technique
6.3 Performance Results
6.4 Discussion
6.5 Summary
References
7 Basic Formation on Wavelet Transforms
7.1 Wavelet Families, Coefficients and Their Shapes
7.2 Introduction to Wavelet Toolbox
7.2.1 Basic Introduction to Wavelet Families Using MATLAB®
7.2.2 ECG Signal Analysis Using Wavelet Toolbox
References
8 Conclusion and Future Work
Annexure A
Annexure B