A High-Performance Adaptive Filter Instantiated from the GAADT Algorithm for Electrocardiogram Signal Processing

A High-Performance Adaptive Filter Instantiated from the GAADT Algorithm for Electrocardiogram Signal Processing

A ReviewBy Andrilene Ferreira Maciel
Michael Caine
Listen with Sir Michael Caine™ and 1,000+ voices
Length2h 55m

About this audiobook

This book was developed to meet the needs of undergraduate and graduate students in the areas of genetic algorithms, signal processing, statistics, and data mining in the area of ​​computer science. Several exercises contained in this book were extracted from the GAADT and CGAADT algorithms. The first chapter of this book aims to provide the reader with basic concepts about electrocardiogram signal processing and its effectiveness in diagnosing cardiac arrhythmia based on signal capture. The following chapter presents the basic concepts of genetic algorithms, pseudolanguage, variables, and their uses. The following chapters deal with the basic structures of the electrocardiogram, morphology, and waveform characteristics of cardiac signals. The basic structures of the GAADT and CGAADT genetic algorithms for electrocardiogram signal processing are then presented. An additional chapter dealing with the applications of CGAADT in the construction of an adaptive filter on a high-performance platform using a Graphics Processing Unit (GPU) for electrocardiogram signal processing concludes this work.

Audiobook details

GenreTechnology
Length2 hrs 55 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateAug 27, 2025
LanguageEnglish

Table of contents

1Acknowledgments
2Preface
3CHAPTER 1 INTRODUCTION
4CHAPTER 2 GENETIC ALGORITHM
52.1 History and Concept
Show all chapters
62.2 Holland’s Genetic Algorithm
72.3 Summary and Conclusions
82.4 Proposed Exercises
9CHAPTER 3 GENETIC ALGORITHM BASED ON ABSTRACT DATA TYPES (GAADT)
103.1 Basic concepts
113.2 GAADT
123.3 Basic Types of GAADT
133.4 Genetic Operators GAADT
143.4.1 GAADT mathematical model
153.4.1 Environment In GAADT, the characteristics of an environment will dictate how chromosome populations will evolve. An environment A is an 8-tuple <P, ℙ, Rq, AFG, AFC, Tx,Σ, P0>, where:
163.4.2 The Algorithm
173.6 Proposed Exercise
18CHAPTER 4 BIOLOGICAL SIGNALS OF ELECTROCARDIOGRAM (ECG)
194.1 Fundamentals
204.1.1 Electrical Activity of the Heart
214.1.2 Anatomy of the Heart
224.1.3 Basic characteristics of the electrocardiogram
234.1.4 Cardiac Arrhythmias
244.1.5 Sinus Arrhythmia
254.1.6 Atrial Fibrillation
264.1.7 Atrial Flutter
274.2 Summary and Conclusions
284.3 Proposed Exercises
29CHAPTER 5 MODELING OF THE COMPACT ALGORITHM BASED ON ABSTRACT DATA TYPES FOR ELECTROCARDIOGRAM SIGNALS
305.1 Basic Concepts
315.2 GPU Accelerated Computing
325.3 Scalable Programming Model in CUDA
335.4 Thread and Memory Hierarchy
345.5 Applications of CGAADT
355.6 Specification of CGAADT on GPU
365.7 Basic Types Definition
375.8 Genetic Operators
385.9 Functionalities: GAADT x CGAADT
395.11 Summary and Conclusions
405.12 Proposed Exercises
41CHAPTER 6 CGAADT APPLICATION
426.1 Fundamentals
436.2 Hardware Infrastructure: 6.2.1 Implementation of GAADT and CGAADT on the FPGA
446.3 Implementation of GAADT and CGAADT on GPU/CUDA
456.4 Databases
466.5 CGAADT Data Flow
476.6 APPLICATION 01: 6.6.1 COMPARISON OF THE CGAADT(GPU) AND GAADT(CPU)
486.7 APPLICATION 2
496.8 APPLICATION Nº 3: 6.8.1 Database
506.9 Summary and Conclusions
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