Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

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.