Automatic Classification of Heart Diseases

By V Rathikarani

ECG Preprocessing, linear prediction coefficients, mel frequency cepstral

coefficients, morphological features, Y-coordinates, R amplitude, heart beat, histogram

of oriented gradients, row pixel count, support vector machine, auto associative

neural network, gaussian mixture model, ECG classification, template matching, cardiac

arrhythmia, myocardial infarction, conduction blocks, supraventricular tachycardia,

atrial fibrillation, ventricular tachycardia, anteroseptal infarction, anterior infarction,

inferior infarction, atrioventricular blocks, left bundle branch block, right bundle

branch block, physiobank

The objective of an ECG classification system is to classify the Electrocardiogram

(ECG) signal using one or more characteristics associated with the Electrocardiogram.

This thesis presents a method for ECG signal and ECG image preprocessing, feature

extraction and classification using features extracted from normal ECG and nine categories

of cardiac diseases. ECG preprocessing refers to denoising of the ECG signal

and resizing of the ECG image. ECG classification system classifies the ECG signal

and image into one of the predefined categories namely normal and Supraventricular

tachycardia, Atrial fibrillation, Ventricular tachycardia, Anteroseptal infarction, Anterior

infarction, Inferior infarction, Atrioventricular blocks, Left bundle branch blocks,

Right bundle branch blocks.

• Format: Paperback
• Language: English
• ISBN: 4653069166
• ISBN13: 9784653069164
• Release Date: December 2022
• Publisher: Asmtm
• Length: 124 Pages
• Weight: 0.39 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"