ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency.Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform ۱D ECG signals into ۲D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from physionet.org and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals.Material and Methods: This analytical observational research employed CWT to generate spectrograms from ۱D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F۱-score.Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of ۱۰۰% for normal signals, an average specificity of ۱۰۰%, an average recall of ۹۷.۶۵%, an average overall accuracy of ۹۸.۶۷%, and an average F۱-score of ۹۸.۸۱%. This model achieves an approximate average overall accuracy of ۹۸.۶۷%, highlighting its effectiveness in detecting CVD. Conclusion: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

Keywords:

Cardiovascular Disorder , CWT , DCNN , Electrocardiography , Signal Processing , Computer-Assisted , Machine Learning