Abstract
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.
Highlights
Advancement in the field of microelectronics and the computational systems has indirectly led to the evolvement of health monitoring devices for daily applications [1]
The beat detection is more challenging for ambulatory monitoring as the level of noise and artefacts produced during daily-life activities is greater than the monitoring process in the hospital setting
EExxaammppllee ooff ssiimmuullaatteedd EECCGG ssiiggnnaallss tthhaatt ccoonnttaaiinn nnooiisse wwiitthh ssiiggnna-to-noise-ratio (SNR) 12, 6, −−6, −−1122 ddBB:: ((aa)) EECCGG ssiiggnnaall wwith baseline wander (BW); (b) ECG signal with muscle artefact (MA); (c) ECG signal with electrode motion (EM) Artefact
Summary
Advancement in the field of microelectronics and the computational systems has indirectly led to the evolvement of health monitoring devices for daily applications [1] This has enhanced the utilization of portable devices that can record ambulatory bio-signals or electrocardiogram (ECG) signals during daily-life activities such as resting, housework, exercise and other physical works. Unlike the standard ECG, the ambulatory ECG records the signal continuously over a long period out-of-hospital environment using the conventional Holter monitor [2] or the trendy wearable devices [3]. The beat detection is more challenging for ambulatory monitoring as the level of noise and artefacts produced during daily-life activities is greater than the monitoring process in the hospital setting. In the ambulatory ECG, various types of noise may occur simultaneously and produced during daily-life activities is greater than the monitoring process in the hospital setting. The effects of noise artefacts in the ECG signals that degraded the beat detection performance were investigated
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