Key result
Multi-detector GbSQI classifies ECG signal quality with ~95% accuracy.
Why the study?
The performance of the bSQI signal quality index heavily depends on the QRS complex detectors used, and it is unclear if different combinations improve signal quality assessment.
A generalized bSQI index using a combination of four specific QRS detectors (U3, UNSW, DOM, OKB) optimizes signal quality assessment for wearable ECG monitoring.
Answers are grounded in this paper and cite their sources.
Supports ECG quality classifiers in research pipelines; leaves open prospective clinical validation before practice integration.
With the fast development of wearable electrocardiogram (ECG) monitoring, real-time and dynamic signal quality assessment (SQA) become an imperious demand. Thus, many signal quality indices (SQIs) have been developed in the past several years. bSQI is a typical SQI defined from two common QRS complex detectors (‘ep_limited’ and ‘wqrs’). However, in actual application, bSQI heavily relies on the QRS complex detectors used. Therefore, if using different combination of QRS detectors can improve the performance of SQA needs to be explored. In this paper, we utilized up to ten QRS detectors to re-define bSQI from the combination of any two QRS detectors to test which combination outputs the highest performance. Then, we generalized the two QRS detector-based bSQI to multiple QRS detector-based bSQI (i.e., GbSQI), to systematically test the effects of type and number of QRS detectors on SQA performances. The results showed that for the single GbSQI feature-based classifier, the combination of six QRS detectors reported the highest classification accuracy with a mAcc of 94.03%. For the multiple GbSQI feature-based classifier, the combination of four QRS detectors showed the best classification accuracy with a mAcc of 94.76%. As a conclusion, we recommended usingU3,UNSW,DOM, andOKBdetectors for calculating GbSQI for the wearable ECG monitoring application.
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Liu et al. (2018) studied Wearable ECG monitoring. Generalized bSQI (GbSQI) using multiple QRS detectors vs. Different combinations of QRS detectors was evaluated on Classification accuracy for signal quality assessment. A multiple GbSQI feature-based classifier using a combination of four QRS detectors (U3, UNSW, DOM, and OKB) achieved the best classification accuracy of 94.76% for ECG signal quality assessment.
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