Cardiac disease classification from ecg signals using hybrid recurrent neural network method

Abstract

Medical practitioners and cardiologists frequently employ electrocardiogram (ECG) analysis to assess cardiac health. An automated ECG categorization systems have difficulty detecting and analysing the signals of numerous waves of populations, particularly electrocardiogram (ECG) data. A variety of learning strategies have been researched for the interpretation of ECG categorization. One drawback of machine learning is the use of heuristic features with shallow feature learning architectures. In order to overcome this difficulty, a deep learning approach is used to automatically learn features as opposed to using conventional handmade features. In this work, the use of Long Short-Term Memory (LSTM) and Gated Recurrent Neural Network (GRNN) techniques is proposed to develop an accurate ECG categorization and monitoring system. The purpose of the gated recurrent neural network to processing the sequential data and check the error presented or not in the ECG signals. The long short-term memory is the type of the neural network approaches to identify the complex sequence prediction problems. The GRNN and LSTM models then receive the learnt features that were first captured by the CNN model. There are no custom features required for the ECG categorization in the model. The results section lists various cutting-edge models that the CNN-LSTM and CNN-GRNN models outperformed. The CNN-LSTM and CNN-GRNN designs outperform conventional ECG classification architectures with comparable hyper-parameters, according to the comparison results.