Why the study?
ECG delineation detects multiple characteristic points that contain critical diagnostic information about cardiac diseases, motivating improved methods to segment ECG signals.
Does a deep learning method using 1D CNN and CRF improve ECG delineation performance compared to existing state-of-the-art methods?
Does a deep learning method using 1D CNN and CRF improve ECG delineation performance compared to existing state-of-the-art methods?
A novel deep learning approach using 1D CNN and CRF provides competitive ECG delineation performance, particularly improving the detection of P wave onset and T wave offset.
Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac diseases. We treat the ECG delineation task as an one-dimensional segmentation problem, and propose a novel end-to-end deep learning method to segment sections of ECG signal. Our neural network consists of two parts: a segmentation network composed of multiple 1D Convolutional Neural Networks (CNN) and a postprocessing network composed of a sequential Conditional Random Field (CRF). Our method is trained and validated on QT database. The experimental results show that our method yields competitive overall performance compared with other state-of-the-art works and outperform them on onset of the P wave and offset of the T wave.
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Jia et al. (2019) studied Cardiac diseases (ECG delineation). Deep learning method (1D CNN and sequential CRF) vs. Other state-of-the-art works was evaluated on ECG delineation performance.
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