Detection of atrial fibrillation from cardiac signal using convolutional neural network. (8th July 2022) - Mohapatra, Saumendra Kumar; Mohanty, Mihir Narayan
Multi-disciplinary research including engineering and medicine paves the way for modern research. In this work, the authors have taken an attempt to classify four types of long-duration ECG signals. The data is of 30-60 seconds and is collected from form '2017 Physionet/Computing in Cardiology Challenge database'. The use of this database for analysis of long duration signal in terms of data mining is one of the novelties of this work. As the pre-processing of the signals, the Savitzky-Golay (SG) filter is used. The filtered signals are classified with a ten-layer convolutional neural network (CNN) model. Sensitivity, specificity, and accuracy, these measuring parameters are used for the performance evaluation. Result found from the proposed method is promising one as compared to earlier methods. 95.89% accuracy is obtained from this classifier. The proposed strategy can be useful for automatic cardiac disease classification as well as detection.