A multi-label classification system for anomaly classification in electrocardiogram

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Abstract

Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.

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Li, C., Sun, L., Peng, D., Subramani, S., & Nicolas, S. C. (2022). A multi-label classification system for anomaly classification in electrocardiogram. Health Information Science and Systems, 10(1). https://doi.org/10.1007/s13755-022-00192-w

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