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Qince Li

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3 papers

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AIIM Journal 2026 Journal Article

A novel ECG QRS complex detection algorithm based on dynamic Bayesian network

Qince Li
Yang Liu
Na Zhao
Yongfeng Yuan
Runnan He

Accurate detection of the QRS complex, a crucial reference for heartbeat localization in electrocardiogram (ECG) signals, remains inadequate in wearable ECG devices due to complex noise interference. In this study, we propose a novel QRS complex detection method based on dynamic Bayesian network (DBN), integrating the probability distribution of RR intervals. Unlike methods focusing solely on ECG waveforms, our approach explicitly integrates ECG waveform and heart rhythm information into a unified probability model, enhancing noise robustness. Additionally, an unsupervised parameter optimization using expectation maximization (EM) adapts to individual differences of patients. Furthermore, several simplification strategies improve reasoning efficiency, and an online detection mode enables real-time applications. Our method outperforms other state-of-the-art QRS detection methods, including deep learning (DL) methods, on noisy datasets. In conclusion, the proposed DBN-based QRS detection algorithm demonstrates outstanding accuracy, noise robustness, generalization ability, real-time capability, and strong scalability, indicating its potential application in wearable ECG devices.

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JBHI Journal 2026 Journal Article

TKRL: Targeted Knowledge Rectification Learning Against Teacher-Originated Defects in Domain Continual Segmentation

Zhanshi Zhu
Wenjian Gu
Xiangyu Li
Qince Li
Yongfeng Yuan
Wei Wang
Kuanquan Wang
Suyu Dong

Knowledge distillation can mitigate catastrophic forgetting in domain continual segmentation by transferring knowledge from the older model to the newer model. However, existing distillation-based methods primarily emphasize knowledge retention while overlooking inherent defects in the older teacher models. As a result, these teacher-originated defects, such as knowledge gaps or biases, are propagated and exacerbate forgetting. To address this challenge, we propose a Targeted Knowledge Rectification Learning framework (TKRL) to probe and correct teacher-originated defects. TKRL consists of two modules: (1) Probe-augmented Class Distillation, which generates gradient-driven “probes” to uncover underrepresented features in the older model, thereby bridging knowledge gaps by distilling hidden information into the new model; (2) Variance-guided Masked Autoencoder, which selectively masks and reconstructs critical high-uncertainty patches across multi-level semantic regions, thereby correcting biases inherited from the older model. Our experimental results show that TKRL effectively rectifies knowledge gaps and biases, thereby mitigating catastrophic forgetting and enhancing performance in domain continual segmentation. The implementation code is publicly available at: https://github.com/PerceptionComputingLab/TKRL_DCMIS.

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JBHI Journal 2021 Journal Article

Automatic Detection of QRS Complexes Using Dual Channels Based on U-Net and Bidirectional Long Short-Term Memory

Runnan He
Yang Liu
Kuanquan Wang
Na Zhao
Yongfeng Yuan
Qince Li
Henggui Zhang

Objective: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. Methods: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. Results: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99. 06% and 95. 13% for sensitivity, 99. 22% and 82. 03% for positive predictivity, and 98. 29% and 78. 73% accuracy on the two datasets respectively. Conclusion: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. Significance: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

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