A machine learning approach to multi-level ECG signal quality classification

Computer Methods and Programs in Biomedicine

Volumn 117, Issue 3, 2014, Pages 435-447

  Li, Qiao ^a,b   Rajagopalan, Cadathur ^c   Clifford, Gari D ^b  

^a SHANDONG UNIVERSITY SCHOOL OF MEDICINE   (China)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c Mindray DS USA   (United States)

Author keywords

ECG; Machine learning; Multi level classification; Signal quality; Support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; ELECTROCARDIOGRAPHY; LEARNING SYSTEMS; PHOSPHORUS COMPOUNDS; PLATES (STRUCTURAL COMPONENTS); SIGNAL TO NOISE RATIO; SUPPORT VECTOR MACHINES; URANIUM COMPOUNDS;

CLASSIFICATION ACCURACY; ELECTROCARDIOGRAM SIGNAL; MACHINE LEARNING APPROACHES; MULTI-LEVEL CLASSIFICATIONS; NOISE STRESS TEST; SIGNAL QUALITY; SIMULATED DATASETS; SIMULATED TRAININGS;

BIOMEDICAL SIGNAL PROCESSING;

ACOUSTIC STRESS; ARTICLE; ARTIFACT; ATTENUATION; CLASSIFICATION ALGORITHM; DIAGNOSTIC ACCURACY; ELECTRIC ACTIVITY; ELECTROCARDIOGRAM; ELECTROCARDIOGRAPH ELECTRODE; FIDUCIAL MARKER; HEART ATRIUM FIBRILLATION; HEART ATRIUM FLUTTER; HEART RATE; HEART VENTRICLE FLUTTER; MORPHOLOGY; PERIODICITY; QRS COMPLEX; QUALITY CONTROL; SIGNAL NOISE RATIO; SINUS RHYTHM; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; WAVEFORM; ARRHYTHMIAS, CARDIAC; ELECTROCARDIOGRAPHY; HUMAN; MEDICAL INFORMATICS; OBSERVER VARIATION; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SIGNAL PROCESSING; THEORETICAL MODEL;

ARRHYTHMIAS, CARDIAC; ELECTROCARDIOGRAPHY; HUMANS; MEDICAL INFORMATICS; MODELS, THEORETICAL; OBSERVER VARIATION; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO; SUPPORT VECTOR MACHINES;

EID: 84909581069     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2014.09.002     Document Type: Article

References (33)

1. Clifford G.D., Azuaje F., McSharry P.E. Advanced Methods and Tools for ECG Data Analysis 2006, 55-70. Artech House, Norwood, MA.
2. Li Q., Clifford G.D. Signal quality and data fusion for false alarm reduction in the intensive care unit. J. Electrocardiol. 2012, 45:596-603.
3. Moody G.B., Mark R.G. QRS morphology representation and noise estimation using the Karhunen-Loève transform. Comput. Cardiol. 1989, 16:269-272.
4. Li Q., Mark R.G., Clifford G.D. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol. Measure. 2008, 29:15-32.
5. Redmond S.J., Lovell N.H., Basilakis J., Celler B.G. ECG quality measures in telecare monitoring. Proceeding of 30th Annual International IEEE EMBS Conference 2008, 2869-2872.
6. Silva I., Moody G.B., Celi L. Improving the quality of ECGs collected using mobile phones: the PhysioNet/Computing in Cardiology Challenge 2011. Comput. Cardiol. 2011, 38:273-276.
7. Clifford G.D., Moody G.B. Signal quality in cardiorespiratory monitoring. Physiol. Measure. 2012, 33:E01. 10.1088/0967-3334/33/9/E01.
8. Clifford G.D., Behar J., Li Q., Rezek I. Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments. Physiol. Measure. 2012, 33:1419-1433.
9. Hayn D., Jammerbund B., Schreier G. QRS detection based ECG quality assessment. Physiol. Measure. 2012, 33:1449-1461.
10. Xia H., Garcia G., Bains J., Wortham D., Zhao X. Matrix of regularity for improving the quality of ECGs. Physiol. Measure. 2012, 33:1535-1548.
11. Chen Y., Yang H. Self-organized neural network for the quality control of 12-lead ECG signals. Physiol. Measure. 2012, 33:1399-1418.
12. Di Marco L.Y., Duan W., Bojarnejad M., Zheng D., King S., Murray A., Langley P. Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality. Physiol. Measure. 2012, 33:1435-1448.
13. Jekova I., Krasteva V., Christov I., Abacherli R. Threshold-based system for noise detection in multilead ECG recordings. Physiol. Measure. 2012, 33:1463-1477.
14. Johannesen J., Galeotti L. Automatic ECG quality scoring methodology: mimicking human annotators. Physiol. Measure. 2012, 33:1479-1489.
15. Redmond S.J., Xie Y., Chang D., Basilakis J., Lovell N.H. Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiol. Measure. 2012, 33:1517-1533.
16. Behar J., Oster J., Li Q., Clifford G.D. ECG signal quality during arrhythmia and its application to false alarm reduction. IEEE Trans. Biomed. Eng. 2013, 60:1660-1666.
17. Vaglio M., Isola L., Gates G., Badilini F. Use of ECG quality metrics in clinical trials. Comput. Cardiol. 2010, 37:505-508.
18. Quesnel P.X., Chan A.D.C., Yang H. Real-time biosignal quality analysis of ambulatory ECG for detection of myocardial ischemia. Proceedings of IEEE International Symposium on Medical Measurements and Applications 2013, 10.1109/MeMeA.2013.6549694.
19. Moody G.B., Mark R.G. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng. Med. Biol. 2001, 20:45-50.
20. Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.Ch., Mark R.G., Mietus J.E., Moody G.B., Peng C.-K., Stanley H.E. PhysioBank, PhysioToolkit, and PhysioNet. Components of a new research resource for complex physiologic signals. Circulation 2000, 101:e215-e220.
21. Moody G.B., Muldrow W.E., Mark R.G. A noise stress test for arrhythmia detectors. Comput. Cardiol. 1984, 11:381-384.
22. http://www.physionet.org/physiotools/wag/wave-1.htm.
23. Rabiner L.R., McClellan J.H., Parks T.W. FIR digital filter design techniques using weighted Chebyshev approximation. Proc. IEEE 1975, 63:595-610.
24. Hamilton P.S., Tompkins W.J. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 1986, 33:1157-1165.
25. Zong W., Moody G.B., Jiang D. A robust open-source algorithm to detect onset and duration of QRS complexes. Comput. Cardiol. 2003, 30:737-740.
26. Nemati S., Malhotra A., Clifford G.D. Data fusion for improved respiration rate estimation. EURASIP J. Adv. Signal Process. 2010, 2010:926305.
27. Richman J.S., Moorman J.R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. - Heart Circ. Physiol. 2000, 278:H2039-H2049.
28. Sameni R., Jutten C., Shamsollahi M.B. Multichannel electrocardiogram decomposition using periodic component analysis. IEEE Trans. Biomed. Eng. 2008, 55:1935-1940.
29. Chang C.C., Lin C.J. LIBSVM. A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2(27):27.
30. C.C. Chang, C.J. Lin, LibSVM - a library for support vector machines, . http://www.csie.ntu.edu.tw/cjlin/libsvm/.
31. Gwet K.L. Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement Among Multiple Raters 2012, March, Advanced Analytics, LLC, P.O. Box 2696, Gaithersburg, MD 20886-2696, USA. 3rd ed.
32. Afonso V.X., Tompkins W.J., Nguyen T.Q., Michler K., Luo S. Comparing stress ECG enhancement algorithms. IEEE Eng. Med. Biol. 1996, 15:37-44.
33. Li Q., Rajagopalan C., Clifford G.D. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans. Biomed. Eng. 2014, 61:1607-1613.