Automatic ECG Analysis using Principal Component Analysis and Wavelet Transformation 1st Edition by Antoun Khawaja ISBN 3866441320 9783866441323

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Product details:

ISBN 10: 3866441320 
ISBN 13: 9783866441323
Author: Antoun Khawaja

The table of contents for Automatic ECG Analysis using Principal Component Analysis and Wavelet Transformation presents a comprehensive exploration of techniques used in the automated analysis of electrocardiogram (ECG) signals. It begins with an introduction to ECG and its significance in medical diagnostics, followed by a detailed examination of signal preprocessing methods essential for accurate analysis. The book delves into the theoretical foundations of two key methods—Wavelet Transformation and Principal Component Analysis (PCA)—highlighting their individual contributions to ECG signal processing and classification. It further explores how the integration of PCA and Wavelet Transform can enhance feature extraction and classification performance in detecting arrhythmias, heart disease, and other conditions. Through various chapters, the book covers important topics such as feature selection, machine learning techniques for ECG classification, and real-world applications in clinical settings. Lastly, it addresses the challenges faced in ECG analysis and suggests potential future advancements in this field.

Automatic ECG Analysis using Principal Component Analysis and Wavelet Transformation 1st Table of contents:

• Fundamentals of Electrocardiography (ECG)

  • Introduction to ECG
  • ECG Signal Characteristics
  • Components of an ECG Waveform
  • ECG Lead System

• Preprocessing of ECG Signals

  • Noise Removal Techniques
  • Filtering Methods
  • Signal Normalization and Segmentation

• Wavelet Transformation for ECG Analysis

  • Overview of Wavelet Theory
  • Wavelet Decomposition and Reconstruction
  • Application of Wavelet Transformation in ECG
  • Discrete Wavelet Transform (DWT) for ECG Signal Processing

• Principal Component Analysis (PCA)

  • Introduction to PCA
  • PCA Algorithm and Mathematical Formulation
  • Dimensionality Reduction in ECG Signals Using PCA
  • PCA-Based Feature Extraction

• Combination of PCA and Wavelet Transformation

  • Integrating PCA with Wavelet Transform
  • Hybrid Methods for ECG Signal Classification
  • Advantages of Combined Approach

• ECG Feature Extraction

  • Identification of Key Features in ECG Signals
  • Statistical and Geometric Features
  • Time-Frequency Features
  • Feature Selection Methods

• ECG Classification Techniques

  • Machine Learning Algorithms for ECG Classification
  • Support Vector Machines (SVM)
  • Artificial Neural Networks (ANN)
  • k-Nearest Neighbors (k-NN) Classifier
  • Performance Evaluation Metrics

• Application of Automatic ECG Analysis

  • Detection of Arrhythmias
  • Heart Rate Variability Analysis
  • Myocardial Infarction Detection
  • Clinical Applications and Case Studies

• Challenges and Future Directions

  • Noise and Artifacts in ECG Signals
  • Real-Time ECG Analysis Systems
  • Future Trends in ECG Processing Technologies

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Antoun Khawaja,Automatic ECG Analysis,Principal Component,Wavelet