Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Ann Biomed Eng. 2010 Apr;38(4):1497-510. doi: 10.1007/s10439-010-9919-3. Epub 2010 Jan 20.
In this study, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s). In the MHOM measure, important geometric characteristics such as maximum value to minimum value ratio, area, extent of smoothness or being impulsive and distribution skewness degree (asymmetry), occult. In the proposed method, first three leads of high resolution 24-h holter data are extracted and preprocessed using Discrete Wavelet Transform (DWT). Next, a sample to sample sliding window is applied to preprocessed sequence and in each slid, mean value, variance, skewness, and kurtosis of the excerpted segment are superimposed called MHOM. The MHOM metric is then used as decision statistic to detect and delineate ECG events. To show advantages of the presented method, it is applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.95% and P+ = 99.94% are obtained for the detection of QRS complexes, with the average maximum delineation error of 6.1, 4.1, and 6.5 ms for P-wave, QRS complex, and T-wave, respectively showing marginal improvement of detection-delineation performance. In the next step, the proposed method is applied to DAY hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks--BBB, Premature Ventricular Complex--PVC, and Premature Atrial Complex-PAC) and average values of Se = 99.97% and P+ = 99.95% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process, reliable robustness against strong noise, artifacts, and probable severe arrhythmia(s) of high resolution holter data can be mentioned as important merits and capabilities of the proposed algorithm.
在这项研究中，引入了一种简单的基于数学统计的度量标准，称为多重高阶矩（MHOM），使心电图（ECG）检测-描绘算法能够在包括强噪声、运动伪影和严重心律失常在内的动态 Holter ECG 中产生可接受的结果。在 MHOM 测量中，重要的几何特征，如最大值与最小值之比、面积、平滑度或冲动程度以及分布偏度（不对称）、隐匿性。在提出的方法中，首先提取高分辨率 24 小时 Holter 数据的前三导，并使用离散小波变换（DWT）进行预处理。接下来，在预处理序列上应用逐点滑动窗口，在每个滑动窗口中，对提取的片段的平均值、方差、偏度和峰度进行叠加，称为 MHOM。然后将 MHOM 度量用作决策统计量来检测和描绘 ECG 事件。为了展示所提出方法的优势，将其应用于 MIT-BIH 心律失常数据库、QT 数据库和 T 波交替数据库，结果表明，用于检测 QRS 复合体的灵敏度和阳性预测值的平均值 Se=99.95%和 P+=99.94%，对于 P 波、QRS 复合体和 T 波的最大描绘误差的平均值分别为 6.1、4.1 和 6.5ms，表明检测-描绘性能略有提高。下一步，将所提出的方法应用于 DAY 医院高分辨率 Holter 数据（包括束支传导阻滞-BBB、室性早搏-PVC 和房性早搏-PAC 在内的超过 150 万次心跳），用于 QRS 检测的灵敏度和阳性预测值的平均值分别为 Se=99.97%和 P+=99.95%。总的来说，ECG 事件检测-描绘过程的性能略有提高，对强噪声、伪影和高分辨率 Holter 数据中可能的严重心律失常的可靠鲁棒性是该算法的重要优点和能力。
