"Practical Applications of Electrocardiogram" ed. by Umashankar Lakshmanadoss

This book provides an excellent overview of the diagnosis of abnormal electrocardiograms (ECGs) through deep learning methods. It will serve as a reference for the techniques used to obtain and process electrical signals for ECGs. This book will also function as an excellent reference for atrial and ventricular tachyarrhythmia.

These methods include optimal techniques that can link the processing and analysis of nonstationary ECG signals, the various statistical methods of converting ECG data into variant maps, and the application of various ways of identifying premature atrial beats, ECG characteristics of right and left ventricular tachyarrhythmia, and conditions producing left ventricular hypertrophy, including hypertrophic cardiomyopathy.
This book is divided into two sections, including basic and practical applications of ECGs.

Contents
1.Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning
2.Combination of the CEEM Decomposition with Adaptive Noise and Periodogram Technique for ECG Signals Analysis
3.Visualization of ECG Data on Variant Maps
4.Characteristics of Atrial Premature Beat ECG Signals on Variant Maps
5.Ventricular Tachycardia and Heart Failure
6.Genetic Polymorphisms that Playing Role in Development of Hypertrophic Cardiomyopathy