Hejč Jakub, Vítek Martin, Ronzhina Marina, Nováková Marie, Kolářová Jana
Department of Biomedical Engineering, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic.
International Clinical Research Center - Center of Biomedical Engineering, St. Anne's University Hospital, Brno, Czech Republic.
Cardiovasc Eng Technol. 2015 Sep;6(3):364-75. doi: 10.1007/s13239-015-0224-z. Epub 2015 Apr 8.
We present a novel wavelet-based ECG delineation method with robust classification of P wave and T wave. The work is aimed on an adaptation of the method to long-term experimental electrograms (EGs) measured on isolated rabbit heart and to evaluate the effect of global ischemia in experimental EGs on delineation performance. The algorithm was tested on a set of 263 rabbit EGs with established reference points and on human signals using standard Common Standards for Quantitative Electrocardiography Standard Database (CSEDB). On CSEDB, standard deviation (SD) of measured errors satisfies given criterions in each point and the results are comparable to other published works. In rabbit signals, our QRS detector reached sensitivity of 99.87% and positive predictivity of 99.89% despite an overlay of spectral components of QRS complex, P wave and power line noise. The algorithm shows great performance in suppressing J-point elevation and reached low overall error in both, QRS onset (SD = 2.8 ms) and QRS offset (SD = 4.3 ms) delineation. T wave offset is detected with acceptable error (SD = 12.9 ms) and sensitivity nearly 99%. Variance of the errors during global ischemia remains relatively stable, however more failures in detection of T wave and P wave occur. Due to differences in spectral and timing characteristics parameters of rabbit based algorithm have to be highly adaptable and set more precisely than in human ECG signals to reach acceptable performance.
我们提出了一种基于小波的新型心电图描绘方法，该方法能够对P波和T波进行稳健分类。这项工作旨在使该方法适用于在离体兔心脏上测量的长期实验心电图（EGs），并评估实验心电图中整体缺血对描绘性能的影响。该算法在一组具有既定参考点的263个兔心电图以及使用标准定量心电图标准数据库（CSEDB）的人体信号上进行了测试。在CSEDB上，测量误差的标准差（SD）在每个点都满足给定标准，并且结果与其他已发表的作品相当。在兔信号中，尽管QRS复合波、P波的频谱成分和电源线噪声相互叠加，但我们的QRS检测器的灵敏度达到了99.87%，阳性预测值达到了99.89%。该算法在抑制J点抬高方面表现出色，在QRS起始（SD = 2.8 ms）和QRS终末（SD = 4.3 ms）描绘中均达到了较低的总体误差。T波终末的检测误差可接受（SD = 12.9 ms），灵敏度接近99%。在整体缺血期间，误差的方差保持相对稳定，然而T波和P波检测中的失败情况更多。由于频谱和时间特征的差异，基于兔的算法的参数必须比人体心电图信号中的参数具有更高的适应性和更精确的设置，才能达到可接受的性能。
