Detection of Cardiovascular Diseases Using Machine Learning and Deep Learning Techniques

Abstract

CVDs are still a significant health problem worldwide and there is a need for an accurate and non-invasive diagnostic system for early clinical intervention. Automatic interpretation of electrocardiogram (ECG) images is difficult due to background interference (grid lines, waveform distortion and scanning artifacts). To overcome these issues, in this work, Present a Wavelet-CNN Hybrid (WCNN-H) framework for automatic multi-class classification of cardiovascular diseases from ECG images. The proposed method is a combination of two dimensional Discrete Wavelet Transform (DWT) with Daubechies (db2) mother wavelet for noise suppression and waveform enhancement and deep residual convolutional neural network for hierarchic feature extraction and classification. The framework was evaluated on the ECG Images Dataset of Cardiac Patients with 928 ECG image records in four diagnostic classes. The experiment achieved an overall classification accuracy of 98.10% using five-fold cross-validation, and precision, recall and F1-score for all classes were above 97%. The proposed framework has a high potential for intelligent computer aided cardiovascular screening applications.