Description
Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition covers reliable techniques for ECG signal processing and their potential to significantly increase the applicability of ECG use in diagnosis. This book details a wide range of challenges in the processes of acquisition, preprocessing, segmentation, mathematical modelling and pattern recognition in ECG signals, presenting practical and robust solutions based on digital signal processing techniques. Users will find this to be a comprehensive resource that contributes to research on the automatic analysis of ECG signals and extends resources relating to rapid and accurate diagnoses, particularly for long-term signals.Chapters cover classical and modern features surrounding f ECG signals, ECG signal acquisition systems, techniques for noise suppression for ECG signal processing, a delineation of the QRS complex, mathematical modelling of T- and P-waves, and the automatic classification of heartbeats.- Gives comprehensive coverage of ECG signal processing- Presents development and parametrization techniques for ECG signal acquisition systems- Analyzes and compares distortions caused by different digital filtering techniques for noise suppression applied over the ECG signal- Describes how to identify if a digitized ECG signal presents irreversible distortion through analysis of its frequency components prior to, and after, filtering- Considers how to enhance QRS complexes and differentiate these from artefacts, noise, and other characteristic waves under different scenarios
Table of Contents
1. Classical and Modern Features for Interpretation of ECG signal2. ECG signal acquisition systems3. Techniques for noise suppression for ECG signal processing4. The issue of QRS detection5. Delineation of QRS complex: challenges for the development of widely applicable algorithms6. Mathematical modelling of T-wave and P-wave: a robust alternative for detecting and delineating those waveforms.7. The issue of automatic classification of heartbeats
