January 1, 2024Journal of Biomedical Physics and EngineeringOpen Access

ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Why the study?

Manual analysis of ECGs is labor-intensive, creating a need for automated methods to improve diagnostic accuracy and efficiency in CVD.

Does an automated ECG classification system using CWT and DCNN accurately detect abnormalities associated with cardiovascular diseases?

Structured PICO

Does an automated ECG classification system using CWT and DCNN accurately detect abnormalities associated with cardiovascular diseases?

Population

ECG datasets from PhysioNet and MIT-BIH (including MIT-BIH arrhythmia dataset)

Intervention

Automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN)

Outcome

Algorithm performance metrics (precision, specificity, recall, overall accuracy, and F1-score)surrogate

A novel deep learning approach combining CWT and DCNN demonstrated high accuracy (98.67%) in classifying ECG abnormalities, offering a potential tool for automated cardiovascular disease detection.

Implication

CWT-DCNN ECG classification may aid automated diagnosis; leaves open prospective clinical validation before practice adoption.

Abstract

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 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH 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 1D 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 F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, 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.