ECG arrythmia classification using Deep Neural Network

For assessing heart electrical signals, the ECG is the most attractive and low-cost diagnostic technique used in healthcare institutions. Arrhythmia is a medical term for aberrant cardiac impulses. Cardiac arrhythmia is hazardous and, in most cases, fatal. Arrhythmias come in a variety of forms, and an ECG test detect it. Electrocardiogram analysis has received great scientific value in the medical field for the classification of different heart arrhythmias. Patients benefit from automated systems that may be tailored to test for arrhythmia categorization, and doctors benefit from them as well. Medical professionals' diagnostic errors are one of the primary causes of death. Humans can now automate patient prognosis and disease deduction from report analysis thanks to technological advancements. Data mining and artificial intelligence approaches are extremely valuable in the creation of expert systems for disease identification. CNN and LSTM are a very helpful algorithm used for various disease identification systems so we will use CNN and LSTM for the detection of ECG signal. Our proposed CNN model achieving the maximum accuracy consists of 13 layers and the LSTM model consists of just five layers but with 512 filters through which will be trained on data than we tested it in different samples. Every ECG signal is preprocessed to eliminate the baseline before being segmented using a straightforward approach. The suggested approach obtained improved precision for ECG classification in simulation experiments using the MIT-BIH benchmark database. When it results are compared to previously state-of-the-art approaches, our model give better results. Keywords: Machine Learning (ML), Deep Learning, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) Deep Neural Networks, ECG Signals, Computer-Aided Diagnosis Systems.