Usually, the frequency of MA noise ranges from 20 to 1000 Hz which can cause challenges in eliminating MA without interfering with the clinical features of the ECG signal. The MA or electromyogram as shown in Figure 1b was produced during a sudden body movement by the electrical activity of muscles. In general, the frequency of the BW is below 1 Hz but through exercise activity, the frequency of the BW in ECG recording may increase with the increasing rate of breathing. The BW amplitudes depend on several factors such as the subject movements, properties of electrode and skin impedance. The BW and abrupt drift as shown in Figure 1a could be due to the subject’s respiration movements besides being contributed by a loose or dry electrode-skin contact. The amplitude and frequency of ECG signals as affected by the artefacts in comparison to clean ECG are presented in Figure 1. Among them, baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact which have frequency range within the frequency limit of ECG signal can manifest similar morphology as the ECG signal and can distort the clinical features of the signal which is important in recognition of various ECG arrhythmias. In the ambulatory ECG, various types of noise may occur simultaneously and unpredictably that originate from stationary and non-stationary sources. When a subject performs various high-intensity physical activities, a poor ECG signal-to-noise-ratio (SNR) may result. 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. The most important process in the monitoring system for the detection of arrhythmia is the identification of the QRS wave also recognized as the QRS complex or beat detection in ECG. This allows the analysis of ambulatory cardiac signals that can assist in various medical applications including the diagnosis of cardiac arrhythmias that can lead to sudden death or heart failure among patients. Unlike the standard ECG, the ambulatory ECG records the signal continuously over a long period out-of-hospital environment using the conventional Holter monitor or the trendy wearable devices. This has also 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. 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.Īdvancement in the field of microelectronics and the computational systems has indirectly led to the evolvement of health monitoring devices for daily applications. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. 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. 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. For this purpose, three well-known algorithms for the beat detection process were re-implemented. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. 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.
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