Cross-modal multiscale multi-instance learning for long-term ECG classification

Abstract

At present, deep learning models have been widely used in electrocardiogram (ECG) classification. However, when processing ECG signals over a long period of time, it is difficult for existing deep learning methods to effectively diagnose and classify ECG signals based on a few abnormal heartbeats in these long-term time series data. In this paper, we present a novel cross-modal multiscale multi-instance learning approach for long-term electrocardiogram (ECG) classification. Our method leverages the foundational paradigm of multi-instance learning (MIL) to tackle the challenge of processing long-term ECG signals, while incorporating cross-modal information to guide the global integration of instances. Furthermore, to enhance model performance and bolster its robustness, we introduce multiscale data into the modeling process. The experimental results show that our model achieves excellent performance in different patterns of different datasets, outperforming a series of other traditional deep learning methods and MIL-based methods. Our model achieves impressive F1 scores of 0.8512 and 0.8293 on the St. Petersburg INCART Arrhythmia dataset and MIT-BIH Arrhythmia dataset, respectively. In the context of intrapatient patterns, our model outperforms the suboptimal models, exhibiting improvements of 1.41% and 0.48% on F1, and 2.07% and 0.93% on Recall. Similarly, for interpatient patterns, our model demonstrates superior performance, surpassing the suboptimal models with substantial gains of 4.21% and 3.07% on F1, and 4.71% and 0.55% on Recall.