Identification of cardiovascular diseases through ECG classification using wavelet transformation

TL;DR

This paper demonstrates that wavelet transformation-based feature extraction from ECG signals can effectively classify cardiovascular diseases with high accuracy, advancing automated diagnostic techniques.

Contribution

It introduces a wavelet-based feature extraction method combined with various classifiers for improved ECG-based cardiovascular disease detection.

Findings

Achieved up to 96% accuracy in classifying ECG signals

Wavelet features significantly improve disease prediction

Support Vector Machines performed best among tested classifiers

Abstract

Cardiovascular diseases are the leading cause of mortality globally, necessitating advancements in diagnostic techniques. This study explores the application of wavelet transformation for classifying electrocardiogram (ECG) signals to identify various cardiovascular conditions. Utilizing the MIT-BIH Arrhythmia Database, we employed both continuous and discrete wavelet transforms to decompose ECG signals into frequency sub-bands, from which we extracted eight statistical features per band. These features were then used to train and test various classifiers, including K-Nearest Neighbors and Support Vector Machines, among others. The classifiers demonstrated high efficacy, with some achieving an accuracy of up to 96% on test data, suggesting that wavelet-based feature extraction significantly enhances the prediction of cardiovascular abnormalities in ECG data. The findings advocate for further exploration of wavelet transforms in medical diagnostics to improve automation and accuracy in disease detection. Future work will focus on optimizing feature selection and classifier parameters to refine predictive performance further.