Abstract
This paper describes a Ventricular Tachycardia/Fibrillation (VT/VF) detection algorithm that is specifically designed for a 24/7 personal wireless heart monitoring system. This monitoring system uses Bluetooth enabled bio-sensors and smart phones to monitor continuously cardiac patients’ vital signs. Our VT/VF algorithm is optimized for continuous real-time monitoring on smart phones with a high sensitivity and specificity. We studied and compared existing VT/VF algorithms and selected the one which suited best our requirements. However, we modified and improved the existing algorithm for the smart phone to achieve better performance results. We tested the algorithm on full-length signals from the physionet CU, MIT-db and MIT-vfdb databases [16] without any pre-selection of VT/VF or normal QRS-complex signals. We achieved 97% sensitivity, 98% accuracy and 98% specificity for our implementation which is excellent compared to existing algorithms.
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References
AHA. Cardiovascular Disease Cost. Last accessed (April 3, 2007) [cited, Available from http://www.americanheart.org/ ]
Leijdekkers, P., Gay, V.: Personal Heart Monitoring and Rehabilitation System using Smart Phones (2006)
Throne, R.D., Janice, M., Jenkins, L.A., Dicarlo, A.: Comparison of Four New Time Domain Techniques for Discriminating Monomorphic Ventricular Tachycardia from Sinus Rhythm using Ventricular Waveform Morphology. IEEE Transactions on Biomedical Engineering 38, 561–570 (1991)
Amann, A.R.T., Unterkofler, K.: Reliability of Old and New Ventricular Fibrillation Detection Algorithms for Automated External Defibrillators. BioMedical Engineering Online 4, 1–23 (2005)
Ayesta, U.L.S., Romero, I.: Complexity Measure Revisited: A New Algorithm for Classifying Cardiac Arrhythmias. IEEE Explorer 2, 1589–1591 (2001)
Chen, S., Thakor, N.V., Mower, M.M.: Ventricular Fibrillation Detection by a Regression Test on the Autocorrelation Function. Medical and Biological Engineering and Computing 25, 241–249 (1987)
Thakor, N., Yi-Zheng Shu, V., Kong-Yan, P.: Ventricular Tachycardia and Fibrillation Detection by a Sequential Hypothesis Testing. IEEE Transactions on Biomedical Engineering 37, 837–843 (1990)
Zhang, X.-S., Yi-Sheng, Z., Nitish, V.: Thakor and Zhi-Zhong Wang, Detecting Ventricular Tachycardia and Fibrillation by Complexity Measure. IEEE Transactions on Biomedical Engineering 46, 548–555 (1999)
Jekova, I., Krasteva, V.: Real Time Detection of Ventricular Fibrillation and Tachycardia. Physiological Measurements 25, 1167–1178 (2004)
Barro, S., Ruiz, R., Cabello, D., Mira, J.: Algorithmic Sequential Decision-making in the Frequency Domain for Life Threatening Ventricular Arrhythmias and Imitative Artefacts: a Diagnostic System. Journal of Biomedical Engineering 11, 320–328 (1989)
Kuo, S.D.R.: Computer Detection of Ventricular Fibrillation. In: Computers in Cardiology, pp. 347–349. IEEE Computer Society, Washington, DC (1978)
Fernandez, A.R., Folgueras, J., Colorado, O.: Validation of a Set of Algorithms for Ventricular Fibrillation Detection: Experimental Results. In: Proceedings of the 2nd Annual International Conference of the IEEE EMBS, Mexico (2003)
Hamilton, P.S., Tompkins, W.J.: Evaluation of QRS Detection Algorithms Using the IBM PC. Engineering Medical Biological Society, Annual Conference of the IEEE, pp. 830– 833 (1985)
Nemec, J., Hammill, S.C., Shen, W.K.: Increase in Heart Rate Precedes Episodes of Ventricular Tachycardia and Ventricular Fibrillation in Patients with Implantable Cardioverter Defibrillators: Analysis of Spontaneous Ventricular Tachycardia Database. Scientific Congress of NASPE 22, 1729–1738 (1999)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M, Ivanov, P.C., 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 101, e215–e220 (2000)
MIT-BIH. CU Ventricular Tachyarrhythmia Database (1992) [cited, Available from: http://www.physionet.org/ ]
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Fokkenrood, S., Leijdekkers, P., Gay, V. (2007). Ventricular Tachycardia/Fibrillation Detection Algorithm for 24/7 Personal Wireless Heart Monitoring. In: Okadome, T., Yamazaki, T., Makhtari, M. (eds) Pervasive Computing for Quality of Life Enhancement. ICOST 2007. Lecture Notes in Computer Science, vol 4541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73035-4_12
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DOI: https://doi.org/10.1007/978-3-540-73035-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73034-7
Online ISBN: 978-3-540-73035-4
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