Joint detection of ECG fiducial points based on multi-band and multi-task encoding and decoding model
Fiducial point detection is the basis of the electrocardiogram(ECG)diagnostic analysis.However,the ECG has waveform variability and is often disturbed by various artifacts and noises,limiting the detection accuracies of fiducial points.This paper first builds a probability graph model to analyze the inference relationships between different band ECG components and fiducial point detection tasks.Then,we propose a multi-band multi-task encoding-decoding network inspired by this probability graph model.The network first performs 1-D convolutions on each ECG component to extract features,then learns the attention masks to resist noise through temporal convolutional modules,and finally adopts the dependent multi-branch structure to realize the joint detection of ECG fiducial points.The experimental results with five-fold cross-validation on the MIT-BIH QT and LUDB databases show that the proposed method can effectively improve the detection accuracy of ECG fiducial points,comparable to the state-of-the-art level.
ECG fiducial point detectionencoding-decoding modeltemporal convolutional network
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