Offers extensive coverage of current and cutting-edge ECG diagnostics
Reviews filtering techniques, aortic pressure measurement, visualization of data and more
Assesses future developments and proposes new research topics
Includes supplementary material: sn.pub/extras
This book provides a comprehensive review of progress in the acquisition and extraction of electrocardiogram signals. The coverage is extensive, from a review of filtering techniques to measurement of heart rate variability, to aortic pressure measurement, to strategies for assessing contractile effort of the left ventricle and more. The book concludes by assessing the future of cardiac signal processing, leading to next generation research which directly impact cardiac health care.
|Various disciplines have been bene?ted 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 re?ected in the shape of ECG waveform and heart rate. However, by the very nature of biosignals, this re?ection 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.
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.
This book deals with the acquisition and extraction of the various morphological features of the electrocardiogram signals.
In the first chapters the book first presents data fusion and different data mining techniques that have been used for the cardiac state diagnosis. The second part deals with heart rate variability (HRV), a non-invasive measurement of cardiovascular autonomic regulation.
Next, visualization of ECG data is discussed, an important part of the display in life threatening state. Here, the handling of data is discussed which were acquired during several hours.
In the following chapters the book discusses aortic pressure measurement which is of significant clinical importance. It presents non-invasive methods for analysis of the aortic pressure waveform, indicating how it can be employed to determine cardiac contractility, arterial compliance, and peripheral resistance. In addition, the book demonstrates methods to extract diagnostic parameters for assessing cardiac function. Further the measurement strategies for contractile effort of the left ventricle are presented.
Finally, the book concludes about the future of cardiac signal processing leading to next generation research topics which directly impacts the cardiac health care.
The editors thank Biocom Technologies for the provided scientific material and help in writing the book.
Inhaltsverzeichnis
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.
Klappentext
Various disciplines have been bene?ted 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 re?ected in the shape of ECG waveform and heart rate. However, by the very nature of biosignals, this re?ection 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.
Includes supplementary material: sn.pub/extras
