Preliminary abnormal electrocardiogram segment screening method for Holter data based on long short-term memory networks

Holter usually monitors electrocardiogram (ECG) signals for more than 24 hours to capture short-lived cardiac abnormalities. In view of the large amount of Holter data and the fact that the normal part accounts for the majority, it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments, and then take the left segments to the doctors or the computer programs for further diagnosis. In this paper, we propose a preliminary abnormal segment screening method for Holter data. Based on long short-term memory (LSTM) networks, the prediction model is established and trained with the normal data of a monitored object. Then, on the basis of kernel density estimation, we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data. Based on these, the preliminary abnormal ECG segment screening analysis is carried out without R wave detection. Experiments on the MIT-BIH arrhythmia database show that, under the condition of ensuring that no abnormal point is missed, 53.89% of normal segments can be effectively obviated. This work can greatly reduce the workload of subsequent further processing.