Sparse based recurrent neural network long short term memory (rnn-lstm) model for the classification of ecg signals

In recent years, with the advancement of classical signal processing approaches, numerous works have been performed on the automatic ECG detection schemes for the enhancement of the effectiveness of the identification of the type of ECG heartbeats. One common issue faced by the previous works is the complexity of signal processing. In order to resolve the computational and complexity issues of existing techniques of signal processing, this research work introduces Sparse representation technique for extracting feature. In this study, adaptive thresholding technique combined with Sparse-based Recurrent Neural Network – Long Short Term Memory (RNN-LSTM) model is employed for the classification of ECG signals. P-QRS-T peaks are identified by employing Adaptive thresholding technique. Statistical features are obtained for each signal and are employed in the process of dictionary learning of sparse decomposition. Sparse representations of the incoming ECG signals are used in training the RNN-LSTM network. The trained classifier will give a classified result on giving a test ECG input signal. The performance indices for the process of classification such as accuracy, precision, error rate, sensitivity, and F-score are calculated. The performance of the proposed Sparse based RNN-LSTM classifier is found to be better in comparison with the existing RNN classifier, K-Nearest Neighbor (K-NN) classifier, and Decision Tree (DT) classifier. Furthermore, for validating the performance of the proposed framework, this approach is tested experimentally with real-world raw ECG data acquired using the AD8232 single-lead ECG sensor. The performance of the proposed experimental setup is compared with the existing state-of-the-art approaches.