| Summary: | Patients with cardiovascular disease typically need constant monitoring, and this is made possible by analyzing their electrocardiogram (ECG) signals to determine the specific pattern of variation associated with each disease. The various malfunctions of the internal parts of the organ generates multiple ECG signals with different commonalities, which can then be used to classify the sub-categories of each disease. In this study, a deep learning network for classifying ECG signals were presented and features were extracted to improve disease classification. Totally 5,655 ECG recordings was used. Initially all 30-s signals were converted to RGB images using Continuous Wavelet Transform (CWT), and then we those images were fed to AlexNet that was trained using slightly modified parameters. According to the results, the proposed method not only outperforms the state-of-the-art methods (98.82% accuracy) but also has higher recall (98.9%) and precision (97.9%) in the aggregate.
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