Key result
Electrode motion noise influenced heartbeat detection performance the most compared to muscle artefact and baseline wander, resulting in the highest number of misdetections and false detections.
Why the study?
Heartbeat detection during ambulatory cardiac monitoring is challenged by daily-life noise and artefacts, making it valuable to understand the relationship between ECG noise characteristics and beat detection performance.
How do different types of ECG noise (baseline wander, muscle artefact, electrode motion) affect the performance of heartbeat detection algorithms?
How do different types of ECG noise (baseline wander, muscle artefact, electrode motion) affect the performance of heartbeat detection algorithms?
Electrode motion artefacts are the most detrimental noise source for heartbeat detection algorithms in ambulatory ECG monitoring.
May compromise ambulatory ECG reliability; leaves open targeted denoising validation for robust algorithms.
Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections.
No takes yet. Share an insight, caveat, or question.
Apandi et al. (2020) studied Ambulatory cardiac monitoring. Three well-known algorithms for beat detection was evaluated on Beat detection performance (misdetections and false detections). Electrode motion noise influenced heartbeat detection performance the most compared to muscle artefact and baseline wander, resulting in the highest number of misdetections and false detections.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: