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PN-QRS: An Uncertainty-aware QRS-complex Detection Method for Wearable ECGs
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  • Xingyao Wang ,
  • Hongxiang Gao ,
  • Caiyun Ma ,
  • Chang Cui ,
  • Tingting Zhu ,
  • Xianghong Cheng ,
  • Jianqing Li ,
  • Chengyu Liu
Xingyao Wang
Southeast University

Corresponding Author:[email protected]

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Hongxiang Gao
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Caiyun Ma
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Chang Cui
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Tingting Zhu
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Xianghong Cheng
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Jianqing Li
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Chengyu Liu
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Abstract

Long-term wearable ECG monitoring has increased the influence of ambiguous factors on QRS-complex detection. Reliable ECG information must be extracted from abundant noises and confusing artifacts. To address this issue, we suggest a positive-negative QRS-complex detector (PN-QRS), a QRS-complex detection model that accounts for uncertainty. Specifically, we relate non-ECG screening and artifact recognition to epistemic and aleatoric uncertainty, respectively. To disentangle the two uncertainties, we introduce the class-biased transformation based on the uniqueness of this problem. The performance of artifact and non-ECG detection and multi-lead QRS-complex localization is then validated. The results indicate that PN-QRS reduces ECG error detection rate by 22.24% and improves non-ECG screening accuracy by 5.9% compared to conventional signal quality assessment methods. And for identification of ambiguous beats, PN-QRS achieves a F1 score of 82.41% in a manually annotated ambiguous ECG dataset, while maintaining high precision for QRS-complex detection (99.38% F1 score). Above all, for multi-lead QRS-complex location, PN-QRS is approaching the performance upper bound through integrating the frame-level results on the lead with the minimum uncertainty. Our work suggests that the proposed PN-QRS has the potential to be employed as a QRS-complex detector in wearable ECG monitoring, with the capacity to remove invalid non-ECG episodes and identify QRS-like artifacts simultaneously.