Applications of deep learning algorithms in biomedical signal processing - pros and cons Online publication date: Thu, 09-Dec-2021
by Gyana Ranjan Patra; Saumendra Kumar Mohapatra; Mihir Narayan Mohanty
International Journal of Biometrics (IJBM), Vol. 14, No. 1, 2022
Abstract: The recently introduced deep neural networks are artificial neural networks that contain multiple hidden layers and can facilitate computational models to acquire data representation with multiple levels of abstraction, thereby showing consistently good results in the areas of speech/visual object recognition, object detection, and many more. In this paper, a study on different biomedical signals is carried out with a closed look on cardiac signal since cardiac failure gets reported a lot, and research on this signal necessitates a special attention. The authors have approached the ECG classification with deep learning model with the study of their pros and cons. A methodology for ECG classification using a 10-layer convolutional neural network has been proposed. Results have shown that the proposed model is capable of performing better than some of the other methods like support vector machine and wavelet transformation methods.
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