论文标题
PAN-TOMPKINS ++：在ECG信号中检测R-Peaks的强大方法
Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals
论文作者
论文摘要
R-peak检测对于心电图（ECG）信号处理至关重要，因为它是心率变异性分析的基础。泛汤匙算法是使用最广泛使用的QRS复合物检测器，用于监测包括心律不齐检测在内的许多心脏疾病。但是，泛汤匙算法在检测QRS复合物中的性能下降，在低质量和嘈杂的信号中降解。本文介绍了PAN-TOMPKINS ++，这是一种改进的Pan-Tompkins算法。通行带为5--18 Hz的带通滤波器，然后使用N点移动平均过滤器，以消除噪声，而无需丢弃显着的信号组件。 PAN-TOMPKINS ++使用三个阈值来区分R峰和噪声峰。与其使用通用方程式，不如使用不同的规则来根据信号模式来调整阈值，以精确检测信号模式的重大变化。所提出的算法减少了假阳性和假阴性检测，因此改善了Pan-Tompkins算法的鲁棒性和性能。 PAN-TOMPKINS ++已在四个开源数据集上进行了测试。实验结果显示R-peak检测和执行时间都有明显的改善。我们分别降低了FP和FN的2.8％和1.8％，在四个数据集中，F-评分平均增加了2.2％，执行时间降低了33％。我们展示了具体的例子，以证明在泛汤匙算法无法识别r峰的情况下，发现所提出的算法是有效的。结果也与其他众所周知的R-PEAK检测算法形成鲜明对比。可用的代码，网址为：https：//github.com/niaz-imtiaz/pan-tompkins-plus-plus
R-peak detection is crucial in electrocardiogram (ECG) signal processing as it is the basis of heart rate variability analysis. The Pan-Tompkins algorithm is the most widely used QRS complex detector for the monitoring of many cardiac diseases including arrhythmia detection. However, the performance of the Pan-Tompkins algorithm in detecting the QRS complexes degrades in low-quality and noisy signals. This article introduces Pan-Tompkins++, an improved Pan-Tompkins algorithm. A bandpass filter with a passband of 5--18 Hz followed by an N-point moving average filter has been applied to remove the noise without discarding the significant signal components. Pan-Tompkins++ uses three thresholds to distinguish between R-peaks and noise peaks. Rather than using a generalized equation, different rules are applied to adjust the thresholds based on the pattern of the signal for the accurate detection of R-peaks under significant changes in signal pattern. The proposed algorithm reduces the False Positive and False Negative detections, and hence improves the robustness and performance of Pan-Tompkins algorithm. Pan-Tompkins++ has been tested on four open source datasets. The experimental results show noticeable improvement for both R-peak detection and execution time. We achieve 2.8% and 1.8% reduction in FP and FN, respectively, and 2.2% increase in F-score on average across four datasets, with 33% reduction in execution time. We show specific examples to demonstrate that in situations where the Pan-Tompkins algorithm fails to identify R-peaks, the proposed algorithm is found to be effective. The results have also been contrasted with other well-known R-peak detection algorithms. Code available at: https://github.com/Niaz-Imtiaz/Pan-Tompkins-Plus-Plus
