Key result
Removing destroyed segments in ECG signals using a novel deep learning network significantly promoted the sensitivity and positive prediction of R-peak detection algorithms.
Why the study?
Electrode motion and muscle artifacts distort wearable ECG signals and decrease diagnostic accuracy, necessitating methods to detect corrupted signal segments.
Does a novel deep learning method for detecting noise segments in ECG signals improve the performance of R-peak detection algorithms?
Does a novel deep learning method for detecting noise segments in ECG signals improve the performance of R-peak detection algorithms?
A novel deep learning method for detecting and removing noise segments in ECG signals significantly improves the performance of R-peak detection algorithms.
Answers are grounded in this paper and cite their sources.
May improve automated ECG analysis in noisy signals; leaves open prospective clinical validation.
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.
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Zhu et al. (2021) studied Electrocardiogram noise and artifacts. Deep learning network for detecting destroyed segments of ECG signals was evaluated on Sensitivity and positive prediction of R-peak detection algorithms. Removing destroyed segments in ECG signals using a novel deep learning network significantly promoted the sensitivity and positive prediction of R-peak detection algorithms.
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