A Novel Method for Detecting Noise Segments in ECG Signals

Wearable electrocardiogram (ECG) monitoring systems were effective ways to diagnose intermittent cardio diseases. However, the Electrode Motion Artifact (EMA) and the Muscle Artifact (MA) destroy the incipient shape of ECG signals, decrease the accuracy of diagnostic results. Here, we proposed a novel method for detecting destroyed segments of ECG signals. The method was based on a deep learning network, and its performance was evaluated on a synthetic dataset of the MIT-BIH arrhythmia database. Its practicability was tested with three R-peak detection algorithms. By removing the destroyed segments in ECG signals, the sensitive and positive prediction of these R-peak detection algorithms were promoted significantly.