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.

中文翻译：

---

基于 U-Net 和双向长短期记忆的双通道 QRS 波群自动检测

客观的 ：检测 ECG 信号中 QRS 复合波的变化被认为是评估患者心脏健康的一种直接、无创、廉价且初步的诊断方法。因此，检测 ECG 信号中的 QRS 复合波必须在短时间内准确。然而，自动QRS检测的可靠性受到各种噪声和复杂信号形态的限制。本文的目的是解决 QRS 波群的自动检测问题。方法 ：在本文中，我们提出了一种基于 U-Net 和双向长短期记忆的使用双通道自动检测 QRS 复合波的新算法。首先，提出的具有均值滤波和离散小波变换的预处理器最初用于去除不同类型的噪声。接下来，信号被转换，注释被重新标记。最后，采用U-Net与双通道双向长短期记忆相结合的方法对QRS波群进行自动检测。结果 ：使用来自 MIT-BIH 心律失常数据库和 CPSC2019 数据集的 44 条 ECG 记录对所提出的算法进行了训练和测试，其灵敏度达到 99.06% 和 95.13%，阳性预测率达到 99.22% 和 82.03%，准确率达到 98.29% 和 78.73%。分别是两个数据集。 结论 ：实验结果证明所提出的方法可用于QRS复杂任务的自动检测。 意义 ：所提出的方法不仅在大ECG数据的QRS复合检测中具有应用潜力，而且可以扩展到其他医学信号研究领域。