  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.
