Merino Manuel, Gómez Isabel María, Molina Alberto J
Department of Electronic Technology, University of Seville, Avd. Reina Mercedes s/n, 41012 Seville, Spain .
Department of Electronic Technology, University of Seville, Avd. Reina Mercedes s/n, 41012 Seville, Spain.
Med Eng Phys. 2015 Jun;37(6):605-9. doi: 10.1016/j.medengphy.2015.03.019. Epub 2015 Apr 23.
The electrocardiogram (ECG) is a well-established technique for determining the electrical activity of the heart and studying its diseases. One of the most common pieces of information that can be read from the ECG is the heart rate (HR) through the detection of its most prominent feature: the QRS complex. This paper describes an offline version and a real-time implementation of a new algorithm to determine QRS localization in the ECG signal based on its envelopment and K-means clustering algorithm. The envelopment is used to obtain a signal with only QRS complexes, deleting P, T, and U waves and baseline wander. Two moving average filters are applied to smooth data. The K-means algorithm classifies data into QRS and non-QRS. The technique is validated using 22 h of ECG data from five Physionet databases. These databases were arbitrarily selected to analyze different morphologies of QRS complexes: three stored data with cardiac pathologies, and two had data with normal heartbeats. The algorithm has a low computational load, with no decision thresholds. Furthermore, it does not require any additional parameter. Sensitivity, positive prediction and accuracy from results are over 99.7%.
心电图（ECG）是一种用于确定心脏电活动并研究其疾病的成熟技术。可以从心电图中读取的最常见信息之一是心率（HR），通过检测其最突出的特征：QRS波群来实现。本文描述了一种基于包络和K均值聚类算法来确定心电图信号中QRS波群定位的新算法的离线版本和实时实现。包络用于获取仅包含QRS波群信号，去除P波、T波、U波及基线漂移。应用两个移动平均滤波器对数据进行平滑处理。K均值算法将数据分类为QRS波群和非QRS波群。使用来自五个Physionet数据库的22小时心电图数据对该技术进行了验证。这些数据库是随机选择的，以分析QRS波群的不同形态：三个存储了患有心脏疾病的数据，另外两个存储了正常心跳的数据。该算法计算量低，无需决策阈值。此外，它不需要任何额外参数。结果的灵敏度、阳性预测值和准确率均超过99.7%。
