The Pan-Tompkins Algorithm is the most widely used QRS complex detector for the monitoring of many cardiac diseases including in arrhythmia detection. This method could provide good detection performance with high-quality clinical ECG signal data. However, the numerous types of noise and artefacts that exist in an ECG signal will produce low-quality ECG signal data. Because of this, the performance of Pan-Tompkins-based QRS detection methods using low-quality ECG signals should be further investigated. In this paper, the performance of the Pan-Tompkins algorithm was analysed in extracting the QRS complex from standard ECG data that includes noise-stressed ECG signals. The algorithm’s QRS detection reliability was tested using MIT-BIH Noise Stress Test data and MIT-BIH Arrhythmia data. The performance of the algorithm was then analysed and presented. This paper shows the capability of the Pan-Tompkins algorithms in handling noisy ECG signals.
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.
a b s t r a c tDriver's workload tends to be increased during driving under complicated traffic environments like lanechanging operation. In such cases, rear collision warning is effective for reduction of cognitive workload. On the other hand, it is pointed out that false alarm or missing alarm caused by sensor errors leads to decrease of driver trust in the warning system and it can result in low efficiency of the system. Suppose that sensor reliability information is provided in real-time. In this paper, we propose a novel warning method to increase driver trust in the system even with low sensor reliability by utilizing sensor reliability information. We investigate the effectiveness of the warning methods in high and low workload situations by driving simulator experiments.
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