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Detection of Ventricular Fibrillation Using Random Forest Classifier

DOI: 10.4236/jbise.2016.95019, PP. 259-268

Keywords: Machine Learning, Random Forests (RF), Ventricular Fibrillation (VF) Detection

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Abstract:

Early warning and detection of ventricular fibrillation is crucial to the successful treatment of this life-threatening condition. In this paper, a ventricular fibrillation classification algorithm using a machine learning method, random forest, is proposed. A total of 17 previously defined ECG feature metrics were extracted from fixed length segments of the echocardiogram (ECG). Three annotated public domain ECG databases (Creighton University Ventricular Tachycardia database, MIT-BIH Arrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database) were used for evaluation of the proposed method. Window sizes 3 s, 5 s and 8 s for overlapping and non-overlapping segmentation methodologies were tested. An accuracy (Acc) of 97.17%, sensitivity (Se) of 95.17% and specificity (Sp) of 97.32% were obtained with 8 s window size for overlapping segments. The results were benchmarked against recent reported results and were found to outper-form them with lower complexity.

References

[1]  Pantelopoulos, A. and Bourbakis, N.G. (2010) A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40, 1-12.
http://dx.doi.org/10.1109/TSMCC.2009.2032660
[2]  Koplan, B.A. and Stevenson, W.G. (2009) Ventricular Tachy-cardia and Sudden Cardiac Death. Mayo Clinic Proceedings, 84, 289-297.
http://dx.doi.org/10.4065/84.3.289
[3]  Weaver, W.D. (1986) Considerations for Improving Survival from Out-of-Hospital Cardiac Arrest. Annals of Emergency Medicine, 15, 1181-1186.
http://dx.doi.org/10.1016/S0196-0644(86)80862-9
[4]  Amann, A., Tratnig, R. and Unterkofler, K. (2005) Reliability of Old and New Ventricular Fibrillation Detection Algorithms for Automated External Defibrillators. Biomedical Engineering Online, 4, 60.
http://dx.doi.org/10.1186/1475-925X-4-60
[5]  Jekova, I. (2007) Shock Advisory Tool: Detection of Life-Threatening Cardiac Arrhythmias and Shock Success Prediction by Means of a Common Parameter Set. Biomedical Signal Processing and Control, 2, 25-33.
http://dx.doi.org/10.1016/j.bspc.2007.01.002
[6]  Clayton, R.H. (1994) Recognition of Ventricular Fibrillation Using Neural Networks. Medical & Biological Engineering & Computing, 32, 217-220.
http://dx.doi.org/10.1007/BF02518922
[7]  Moraes, J. (2002) Ventricular Fibrillation Detection Using Leakage/Complexity Measure Method. Computers in Cardiology, 29, 213-216.
http://dx.doi.org/10.1109/CIC.2002.1166745
[8]  Jekova, I. and Mitev, P. (2002) Detection of Ventricular Fibrillation and Tachycardia from the Surface ECG by a Set of Parameters Acquired from Four Methods. Physiological Measurement, 23, 629-634.
http://dx.doi.org/10.1088/0967-3334/23/4/303
[9]  Goldberger, A.L., et al. (2000) Physiobank, Physiotoolkit, and Physionet Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, e215-e220.
http://dx.doi.org/10.1161/01.cir.101.23.e215
[10]  Nolle, F.M. (1986) CREI-GARD: A New Concept in Computerized Arrhythmia Monitoring Systems. Computers in Cardiology, 13, 515-518.
[11]  Greenwald, S.D. (1986) Development and Analysis of a Ventricular Fibrillation Detector. M.S. Thesis, MIT Dept. of Electrical Engineering and Computer Science, Cambridge, MA.
[12]  Moody, G.B. and Mark, R.G. (2001) The Impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine, 20, 45-50.
http://dx.doi.org/10.1109/51.932724
[13]  Li, Q. (2014) Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach. IEEE Transactions on Biomedical Engineering, 61, 1607-1613.
http://dx.doi.org/10.1109/TBME.2013.2275000
[14]  Amann, A. (2007) Detecting Ventricular Fibrillation by Time-Delay Methods. IEEE Transactions on Biomedical Engineering, 54, 174-177.
http://dx.doi.org/10.1109/TBME.2006.880909
[15]  Zhang, X.S. (1999) Detecting Ventricular Tachycardia and Fibrillation by Complexity Measure. IEEE Transactions on Biomedical Engineering, 46, 548-555.
http://dx.doi.org/10.1109/10.759055
[16]  Kuo, S. and Dillman, R. (1978) Computer Detection of Ventricular Fibrillation. IEEE Computers in Cardiology, 347- 349.
[17]  Barro, S. (1989) Algorithmic Sequential Decision Making in a Frequency Domain for Life Threatening Ventricular Arrhythmias and Imitative Artifacts: A Diagnostic System. Journal of Biomedical Engineering, 11, 320-328.
http://dx.doi.org/10.1016/0141-5425(89)90067-8
[18]  Kim, J. and Chu, C.H. (2014) ETD: An Extended Time Delay Algorithm for Ventricular Fibrillation Detection. Proceedings of the Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, 26-30 August 2014, 6479-6482.
[19]  Jekova, I. and Krasteva, V. (2004) Real Time Detection of Ventricular Fibrillation and Tachycardia. Physiological Measurement, 25, 1167-1178.
http://dx.doi.org/10.1088/0967-3334/25/5/007
[20]  Li, Q. (2008) Robust Heart Rate Estimation from Multiple Asynchronous Noisy Sources Using Signal Quality Indices and a Kalman Filter. Physiological Measurement, 29, 15-32.
http://dx.doi.org/10.1088/0967-3334/29/1/002
[21]  Arafat, M.A., Chowdhury, A.W. and Hasan, M.K. (2011) A Simple Time Domain Algorithm for the Detection of Ventricular Fibrillation in Electrocardiogram. Signal, Image and Video Processing, 5, 1-10.
http://dx.doi.org/10.1007/s11760-009-0136-1
[22]  Li, H. (2009) Detecting Ventricular Fibrillation by Fast Algorithm of Dynamic Sample Entropy. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Guilin, 19-23 December 2009, 1105-1110.
http://dx.doi.org/10.1109/robio.2009.5420764
[23]  Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.
http://dx.doi.org/10.1023/A:1010933404324
[24]  Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009) The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter, 11, 10-18.
http://dx.doi.org/10.1145/1656274.1656278
[25]  Guyon, I., Gunn, S., Nikravesh, M. and Zadeh, L.A. (Eds.) (2008) Feature Extraction: Foundations and Applications. Springer, Vol. 207.
[26]  Alonso-Atienza, F., Morgado, E., Fernandez-Martinez, L., Garcia-Alberola, A. and Rojo-Alvarez, J.L. (2014) Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines. IEEE Transactions on Bio-medical Engineering, 61, 832-840.
http://dx.doi.org/10.1109/TBME.2013.2290800
[27]  Jekova, I. (2000) Comparison of Five Algorithms for the Detection of Ventricular Fibrillation from the Surface ECG. Physiological Measurement, 21, 429-439.
http://dx.doi.org/10.1088/0967-3334/21/4/301
[28]  Thakor, N.V. (1990) Ventricular Tachycardia and Fibrillation Detection by a Sequential Hypothesis Testing Algorithm. IEEE Transactions on Biomedical Engineering, 37, 837-843.
http://dx.doi.org/10.1109/10.58594
[29]  Kim, J.Y. and Chu, C.H. (2014) Analysis of Energy Consumption for Wearable ECG Devices. IEEE SENSORS 2014 Proceedings, Valencia, 2-5 November 2014, 962-965.
http://dx.doi.org/10.1109/ICSENS.2014.6985162

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