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

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Abstract

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.

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Abbreviations

MHOM:

Multiple higher order moments

ECG:

Electrocardiogram

PVC:

Premature ventricular contraction

PAC:

Premature atrial contraction

RCA:

Retrograde conduction into atrium

BBB:

Bundle branch block

DWT:

Discrete wavelet transform

QTDB:

QT database

MITDB:

MIT-BIH Arrhythmia database

TWADB:

T-wave alternans database

EDB:

European ST-T database

FP:

False positive

FN:

False negative

TP:

True positive

P+:

Positive predictivity (%)

Se:

Sensitivity (%)

SMF:

Smoothing function

FIR:

Finite-duration impulse response

LE:

Location error

CHECK#0:

Procedure of evaluating obtained results using MIT-BIH annotation files

CHECK#1:

Procedure of evaluating obtained results consulting with a control cardiologist

CHECK#2:

Procedure of evaluating obtained results consulting with a control cardiologist and also at least with three residents

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Acknowledgments

The authors wish to dedicate sincere thanks to Professor Jami G. Shakibi (director of DAY general Hospital NICEL) and Professor Reza Rahmani (director of Imam Hospital Catheter Lab.) for their lively discussions during evolution of this study. Finally, authors wish to dedicate many sincere thanks to the Editor-in-Chief, experts and staffs of “Annals of Biomedical Engineering” for the valuable time and patience they kindly devoted during the review process of this manuscript.

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Correspondence to M. R. Homaeinezhad.

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Associate Editor Kyriacos A. Athanasiou oversaw the review of this article.

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Ghaffari, A., Homaeinezhad, M.R., Khazraee, M. et al. Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric. Ann Biomed Eng 38, 1497–1510 (2010). https://doi.org/10.1007/s10439-010-9919-3

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  • DOI: https://doi.org/10.1007/s10439-010-9919-3

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