Knowledge of electrocardiogram (ECG) wave signals is one of the essential steps in diagnosing heart abnormalities. Considerable performance with respect to obtaining the critical point of a signal waveform (P-QRS-T) through ECG delineation has been achieved in many studies. However, several deficiencies remain regarding previous methods, including the effects of noise interference on the performance degradation of delineation and the role of medical knowledge in reaching a delineation decision. To address these challenges, this paper proposes a robust delineation model based on a convolutional recurrent network with grid search optimization, aiming to classify the precise P-QRS-T waves. In order to make a delineation decision, the results from the ECG waveform classification model are utilized to interpret morphological abnormalities, based on medical knowledge. We generated 36 models, and the model with the best results achieved 99.97% accuracy, 99.92% sensitivity, and 99.93% precision for ECG waveform classification (P-wave, QRS-complex, T-wave, and isoelectric line class). To ensure the model robustness, we evaluated delineation model performance on seven different types of ECG datasets, namely the Lobachevsky University Electrocardiography Database (LUDB), QT Database (QTDB), the PhysioNet/Computing in Cardiology Challenge 2017, China Physiological Signal Challenge 2018, ECG Arrhythmia of Chapman University, MIT-BIH Arrhythmia Database and General Mohammad Hossein Hospital (Indonesia) databases. To detect the patterns of ECG morphological abnormalities through proposed delineation model, we focus on investigating arrhythmias. This process is based on two inputs examination: the P-wave and the regular/irregular rhythm of the RR interval. As the results, the proposed method has considerable capability to interpret the delineation result in cases with artifact noise, baseline drift and abnormal morphologies for delivering robust ECG delineation.
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