Affiliation:
1. School of Information Technology, Deakin University, Geelong 3225, Australia
2. Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia
Abstract
Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQI
p
), skewness (SQI
skew
), signal-to-noise ratio (SQI
snr
), higher order statistics SQI (SQI
hos
) and peakedness of kurtosis (SQI
kur
). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.
Subject
Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology
Cited by
18 articles.
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