Abstract
The number of heart disease cases as well as the death associated with it are rising in numbers every year. It is now more important than ever to diagnose heart abnormalities quickly and correctly to ensure proper treatment is provided in time. A common tool for diagnosing heart abnormalities is the Electrocardiogram (ECG). The ECG is a procedure that requires electrodes to monitor and records the activity of hearts as a form of signal. In this paper, a method is proposed to classify standard 12-lead ECG signals using continuous wavelet transform (CWT) and convolutional neural network (CNN). At first, CWT is used to extract and represent features of the ECG signals in 2-dimensional (2D) RGB images. Later, the RGB images are classified into normal and abnormal cases using a pre-trained CNN. The proposed method is evaluated using a dataset containing ECG signals from 18,885 subjects. The maximum accuracy, precision, recall, F1 score, and AUC obtained are 74.78%, 78.968%, 71.003%, 72.957%, and 0.81126 respectively.
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Mayabee, T.T., Haque, K.T., Alam, S.B., Rahman, R., Amin, M.A., Kobashi, S. (2023). ECG Signal Classification Using Transfer Learning and Convolutional Neural Networks. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_21
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DOI: https://doi.org/10.1007/978-981-19-9483-8_21
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