Shang Haixia, Wei Shoushui, Liu Feifei, Wei Dingwen, Chen Lei, Liu Chengyu
School of Control Science and Engineering, Shandong University, Jinan 250061, China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Comput Math Methods Med. 2019 Apr 1;2019:3130527. doi: 10.1155/2019/3130527. eCollection 2019.
The wave represents ECG repolarization, whose detection is required during myocardial ischemia, and the first significant change in the ECG signal is being observed in the ST segment followed by changes in other waves like wave and QRS complex. To offer guidance in clinical diagnosis, decision-making, and daily mobile ECG monitoring, the wave needs to be detected firstly. Recently, the sliding area-based method has received an increasing amount of attention due to its robustness and low computational burden. However, the parameter setting of the search window's boundaries in this method is not adaptive. Therefore, in this study, we proposed an improved sliding window area method with more adaptive parameter setting for wave detection.
Firstly, -means clustering was used in the annotated MIT QT database to generate three piecewise functions for delineating the relationship between the RR interval and the interval from the peak to the wave onset and that between the RR interval and the interval from the peak to the wave offset. Then, the grid search technique combined with 5-fold cross validation was used to select the suitable parameters' combination for the sliding window area method.
With respect to onset detection in the QT database, 1 improved from 54.70% to 70.46% and 54.05% to 72.94% for the first and second electrocardiogram (ECG) channels, respectively. For offset detection, 1 also improved in both channels as it did in the European ST-T database.
1 results from the improved algorithm version were higher than those from the traditional method, indicating a potentially useful application for the proposed method in ECG monitoring.
T波代表心电图复极化，在心肌缺血期间需要对其进行检测，并且在心电图信号中首先观察到的显著变化出现在ST段，随后是其他波如T波和QRS复合波的变化。为了在临床诊断、决策和日常动态心电图监测中提供指导，需要首先检测T波。最近，基于滑动区域的方法因其鲁棒性和低计算负担而受到越来越多的关注。然而，该方法中搜索窗口边界的参数设置缺乏适应性。因此，在本研究中，我们提出了一种改进的滑动窗口区域方法，其参数设置更具适应性，用于T波检测。
首先，在带注释的麻省理工学院QT数据库中使用K均值聚类生成三个分段函数，以描述RR间期与从T波峰值到T波起始点的间期之间的关系以及RR间期与从T波峰值到T波终点的间期之间的关系。然后，将网格搜索技术与五折交叉验证相结合，为滑动窗口区域方法选择合适的参数组合。
关于QT数据库中的起始点检测，对于第一和第二心电图通道，T1分别从54.70%提高到70.46%和从54.05%提高到72.94%。对于终点检测，在两个通道中T1也如在欧洲ST-T数据库中一样有所改善。
改进算法版本的T1结果高于传统方法，表明所提出的方法在心电图监测中具有潜在的有用应用。
