Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, NE1 8ST, UK.
Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, NE1 8ST, UK.
Comput Methods Programs Biomed. 2017 Jun;144:61-75. doi: 10.1016/j.cmpb.2017.02.028. Epub 2017 Mar 18.
Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals.
A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals.
The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation.
In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal.
检测心电图信号的 QRS 复合波中的 R 波对于诊断患者的心脏状况起着重要作用。为了从采集的原始心电图信号中准确地识别 QRS 位置，我们需要处理许多挑战，包括噪声、基线漂移、峰值幅度变化和信号异常。本研究旨在通过开发一种用于从原始心电图信号中检测 QRS（即 R 波）的高效轻量级算法来解决这些挑战。
提出了一种基于轻量级实时滑动窗口的最大-最小差（MMD）算法，用于从导联 II 心电图信号中检测 QRS。为了在计算效率和检测精度之间达到最佳折衷，所提出的算法包括 QRS 检测的五个关键步骤，即基线校正、MMD 曲线生成、动态阈值计算、R 波检测和误差校正。使用来自 Physionet 的五个标注数据库来评估所提出的算法在 R 波检测中的性能。通过与特征提取技术和神经网络分类器集成，所提出的 QRS 检测算法也扩展到从心电图信号中进行正常和异常心跳检测。
所提出的算法在 QRS 检测中表现出高度的稳健性，平均灵敏度为 99.62%，平均阳性预测值为 99.67%。其性能优于文献中报道的现有最先进模型的性能。在正常和异常心跳检测方面，基于 MIT-BIH 心律失常数据集的 10 倍交叉验证，所提出的 QRS 检测算法与特征提取技术和神经网络分类器相结合，在经验评估中达到了 93.44%的总体准确率。
与其他相关研究相比，所提出的算法为 R 波检测提供了一种轻量级自适应替代方案，具有良好的计算效率。实验结果表明，它不仅在 QRS 检测中具有高准确率，而且在计算复杂度方面也具有高效性，其阶数为 O(n)，其中 n 是心电图信号的长度。
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