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.
This is a preview of subscription content, log in via an institution to check access.
Access this article Subscribe and saveSpringer+ Basic
€34.99 /Month
Price includes VAT (Germany)
Instant access to the full article PDF.
Similar content being viewed by others Explore related subjectsDiscover the latest articles and news from researchers in related subjects, suggested using machine learning. AbbreviationsMultiple higher order moments
Electrocardiogram
Premature ventricular contraction
Premature atrial contraction
Retrograde conduction into atrium
Bundle branch block
Discrete wavelet transform
QT database
MIT-BIH Arrhythmia database
T-wave alternans database
European ST-T database
False positive
False negative
True positive
Positive predictivity (%)
Sensitivity (%)
Smoothing function
Finite-duration impulse response
Location error
Procedure of evaluating obtained results using MIT-BIH annotation files
Procedure of evaluating obtained results consulting with a control cardiologist
Procedure of evaluating obtained results consulting with a control cardiologist and also at least with three residents
Arzeno Natalia, M., Z.-D. Deng, and C.-S. Poon. Analysis of first-derivative based QRS detection algorithms. IEEE Trans. Biomed. Eng. 55(2):478–484, 2008.
Bishop, C. M. Pattern Recognition and Machine Learning. New York: Springer Publishing, 2006.
Chiarugi, F., M. Varanini, F. Cantini, F. Conforti, and G. Vrouchos. Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation. IEEE Trans. Biomed. Eng. 54(8):1399–1406, 2007.
Christov, I., and I. Simova. Q-onset and T-end delineation: assessment of the performance of an automated method with the use of a reference database. Physiol. Meas. 28:213–221, 2007.
de Lannoy, G., B. Frenay, M. Verleysen, and J. Delbeke. Supervised ECG delineation using the wavelet transform and hidden Markov models. Proc. IFMBE 22:22–25, 2008.
Ghaffari, A., and M. R. Homaeinezhad. Fading parameters of sodium, potassium and leakage ionic channels the best linear unbiased sequentially estimation (BLUE) via voltage clamp technique noisy measurement. In: 16th Annual (International) Conference on Mechanical Engineering-ISME 2008 May 14–16, 2008, Shahid Bahonar University of Kerman, Iran.
Ghaffari, A., M. R. Homaeinezhad, M. Akraminia, M. Atarod, and M. Daevaeiha. Detecting and discriminating premature atrial and ventricular contractions: application to prediction of paroxysmal atrial fibrillation. In: 35th Annual Conference of Computers in Cardiology (CinC), September 13–16 2009, Lake City, UT, USA.
Ghaffari, A., M. R. Homaeinezhad, M. Akraminia, M. Atarod, and M. Daevaeiha. Detecting and quantifying T-wave alternans in patients with heart failure and non-ischemic cardiomyopathy via modified spectral method. In: 35th Annual Conference of Computers in Cardiology (CinC), September 13–16 2009, Lake City, UT, USA.
Ghaffari, A., M. R. Homaeinezhad, M. Akraminia, M. Atarod, and M. Daevaeiha. A robust wavelet-based multi-lead electrocardiogram delineation algorithm. Med. Eng. Phys. 31(10):1219–1227, 2009.
Ghaffari, A., M. R. Homaeinezhad, M. Akraminia, and M. Davaeeha. Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales. IMECHE Proc. Part H: Eng. Med. 224(1):27–42, 2010.
Ghaffari, A., M. R. Homaeinezhad, M. Atarod, Y. Ahmady, and R. Rahmani. Detecting and quantifying T-wave alternans using the correlation method and comparison with the FFT-based method. In: 34th Annual Conference of Computers in Cardiology (CinC), September 14–17 2008, Bologna, Italy.
Ghaffari, A., M. R. Homaeinezhad, M. Atarod, and M. Akraminia. Parallel processing of ECG and blood pressure waveforms for detection of acute hypotensive episodes: a simulation study using a risk scoring model. Comput Methods Biomech Biomed Eng., 2010. doi:10.1080/10255840903099711.
Hamilton, P. S., and W. Tompkins. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 33:1157–1165, 1986.
Kannathal, N., C. M. Lim, U. R. Acharya, and P. K. Sadasivan. Cardiac state diagnosis using adaptive neuro fuzzy technique. Med. Eng. Phys. 28:809–815, 2006.
Laguna, P., R. Jane, and P. Caminal. Automatic detection of wave boundaries in multi-lead ECG signals: validation with the CSE database. Comput. Biomed. Res. 27(1):45–60, 1994.
Laguna, P., R. Mark, A. Goldenberger, and G. B. Moody. A database for evaluation of algorithms for measurement of QT and other waveform intervals in ECG. In: The Proceeding of Computers in Cardiology, pp. 673–676, 1997.
Li, C., C. Zheng, and C. Tai. Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42:21–28, 1995.
Lin, C. H., Y. C. Du, and T. Chen. Adaptive wavelet network for multiple cardiac arrhythmias recognition. Expert Syst. Appl. 34:2601–2611, 2008.
Martinez, J. P., R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4):570–581, 2004.
Minhas, F. A. A., and M. Arif. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiol. Meas. 29:555–570, 2008.
Mitra, M., and S. Mitra. A software based approach for detection of QRS vector of ECG signal. IFMBE Proc. 15:348–351, 2007.
Montgomery, D. C., and G. C. Runger. Applied Statistics and Probability for Engineers (3rd ed.). New York: Wiley, 2003.
Moody, G. B. WFDB Applications Guide (10th ed.). Harvard: MIT Division of Health Sciences and Technology, 2006. http://www.physionet.org/physiotools/wag/.
Moody, G. B. The PhysioNet/computers in cardiology challenge 2008: T-wave alternans. In: The Proceeding of Computers in Cardiology, vol. 35, 505–508, 2008.
Moody, G. B., and R. G. Mark. Development and evaluation of a 2-lead ECG analysis program. In: The Proceeding of Computers in Cardiology, pp. 39–44, 1982.
Moody, G. B., and R. G. Mark. The MIT-BIH arrhythmia databaseon CD-Rom and software for it. In: The Proceeding of Computers in Cardiology, pp. 185–188, 1990.
Pan, J., and W. J. Tompkins. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32:230–236, 1985.
Sayadi, O., and M. B. Shamsollahi. A model-based Bayesian framework for ECG beat segmentation. Physiol. Meas. 30:335–352, 2009.
Vila, J., Y. Gang, J. Presedo, M. Fernandez-Delgado, and M. Malik. A new approach for TU complex characterization. IEEE Trans. Biomed. Eng. 47:764–772, 2000.
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.
Author information Authors and AffiliationsDepartment of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
A. Ghaffari, M. R. Homaeinezhad & M. Khazraee
CardioVascular Research Group (CVRG), Tehran, Iran
A. Ghaffari, M. R. Homaeinezhad & M. Khazraee
Non-invasive Cardiac Electrophysiology Laboratory (NICEL), DAY Hospital, Tehran, Iran
M. M. Daevaeiha
Correspondence to M. R. Homaeinezhad.
Additional informationAssociate Editor Kyriacos A. Athanasiou oversaw the review of this article.
About this article Cite this articleGhaffari, 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
Received: 28 October 2009
Accepted: 07 January 2010
Published: 20 January 2010
Issue Date: April 2010
DOI: https://doi.org/10.1007/s10439-010-9919-3
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4