Electrocardiographic Classification using Deep Learning with Lead Switching.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID:
40039540
Abstract
The classification algorithms of rhythm and morphology abnormalities in electrocardiogram (ECG) signals have been widely studied. However, the existing study uses ECGs with fixed leads. We propose a neural network-based method to improve the ECG classification performance by switching the observing lead, where a single-lead ECG signal is observed at a time. The proposed model takes a sequence of ECGs with different leads as input, and outputs its abnormal probability. Using the trained model, an optimal lead order is determined based on the areas under the receiver-operating characteristic curve (AUC). The experimental results using 6,877 ECG recordings with nine diagnoses show that the proposed lead switching approach achieves significantly better AUCs than the fixed single-lead approaches, and comparable performance with 12-lead ECGs for classification of several diagnoses.