Advances in Cardiac Signal Processing

by U. Rajendra Acharya, Jasjit Suri (Editor), J.A.E Spaan (Editor), Shankar M. Krishnan (Editor)

Overview

Various disciplines have been benefited by the advent of high-performance computing in achieving practical solutions to their problems and the area of health care is no exception to this. Signal processing and data mining tools have been developed to enhance the computational capabilities so as to help clinicians in diagnosis and treatment. The electrocardiogram (ECG) is a representative signal containing inf- mation about the condition of the heart. The shape and size of the P-QRS-T wave and the time intervals between various peaks contains useful infor- tion about the nature of disease a?icting the heart. However, the human - server cannot directly monitor these subtle details. Besides, since biosignals are highly subjective, the symptoms may appear at random in the timescale. The presence of cardiac abnormalities are generally reffected in the shape of ECG waveform and heart rate. However, by the very nature of biosignals, this reffection would be random in the timescale. That is, the diseases may not show up all the time, but would manifest at certain irregular (random) intervals during the day. Therefore the study of ECG pattern and heart rate variability has to be carried out over extended periods of time (i. e. , for 24 hours). Naturally the volume of the data to be handled is enormous and its study is tedious and time consuming. As a consequence, the possibility of the analyst missing (or misreading) vital information is high.

Product Details

ISBN-13:	9783540366744
Publisher:	Springer Berlin Heidelberg
Publication date:	05/18/2007
Edition description:	2007
Pages:	468
Product dimensions:	6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

The Electrocardiogram.- Analysis of Electrocardiograms.- Prediction of Cardiac Signals Using Linear and Nonlinear Techniques.- Visualization of Cardiac Health Using Electrocardiograms.- Heart Rate Variability.- Data Fusion of Multimodal Cardiovascular Signals.- Classification of Cardiac Patient States Using Artificial Neural Networks.- The Application of Autoregressive Modeling in Cardiac Arrhythmia Classification.- Classification of Cardiac Abnormalities Using Heart Rate Signals: A Comparative Study.- Storage and Transmission of Cardiac Data with Medical Images.- Assessment of Cardiac Function in Filling amp; Systolic Ejection Phases: A Mathematical and Clinical Evaluation.- Arterial Wave Propagation and Reflection at a Bifurcation Site.- ECG Signal Conditioning by Morphological Filters.- Multivariate Analysis for Cardiovascular and Respiratory Signals.- Phase Space Analysis for Cardiovascular Signals.- Linear, Non-Linear and Wavelet Analysis of Cardiac Health Using Heart Rate Signals.- Soft Tissue Biomechanics of the Left Ventricular Myocardium.- Wavelets and its Application in Cardiology.- 1/f Fluctuation of Heart Rate in Postoperative and Brain-Dead Patients.- Stress During Speech Therapy.