ARRHYTHMIA CLASSIFICATION USING DEEP LEARNING

The importance of ECG classification is on the rise with its many current medical applications of the same. Currently, there are many machine learning approaches contributed towards analysing and classifying ECG data. The main setbacks of these ML results are use of hand-crafted or inadequate feature learning architectures which often relies on the probability of finding the appropriate features. One of the proposing solutions is to use deep learning architectures where first layer uses LSTM which behave as feature extractors and summarized with some fully-connected (FCN) layers are used for predicting the final conclusion about classes in ECG.