Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm

Abstract

Abstract Background Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm/beat. Method A computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach. Results The empirical results using both beat segmentation approaches, with a total of 14,588 beats were showed that our proposed model performed excellently well. All performance metrics above 95% and 93%, for beat-based and patient-based segmentation, respectively. Conclusions This is a significant step towards the clinical pertinency of automated 12-lead ECG delineation using deep learning.

Keywords

• 12-lead electrocardiogram
• Bidirectional long short-term memory
• Convolutional neural network
• Delineation model
• ECG waveform
• Isoelectric line