ECG_DEEPNet: A Novel Approach for Delineation and Classification of Electrocardiogram Signal Based on Ensemble Deep-Learning

The advancements in telehealth monitoring technology have enabled the collection of vast quantities of electrophysiological signals, including the electrocardiogram (ECG) which contains critical diagnostic information about cardiac diseases. There are two main key challenges in the automatic classification of cardiac rhythms. First, addressing the specific characteristics of irregular heartbeats is critical for accurate classification. Second, the low frequency of ECG signals combined with noise interference makes it particularly difficult to efficiently detect abnormal electrical activity in the heart. To solve this issue, this paper proposes an Ensemble deep-learning model, ECG_DEEPNet architecture to enhance the delineation of ECG signals with improved accuracy for better diagnosis in telemedicine monitoring systems. The presented technique consists of a feature extraction stage using a convolutional neural network (CNN) and a sequence processing stage using a combination of gated recurrent units (GRU) and bidirectional long short-term memory (BiLSTM) networks. The proposed method is divided into four parts: firstly, the signal preprocessing, secondly waveform segmentation, thirdly classification of ECG signals and lastly results are evaluated on the proposed model. The proposed technique was tested and trained using standard LUDB and QT database (QTDB) containing annotation of a waveform for accurate classification of ECG wave components. The presented technique shows the average accuracy of 99.82%, 98.50%, and 97.42% for QRS, P, and T on the QTDB database, and 99.96%, 98.82%, and 99.47% on LUDB dataset respectively, for classification and delineation of ECG signals. The proposed technique achieves better performance compared to state-of-the-art methods, which results in a better diagnosis of heart-related problems.