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Paper Count: 30069
HRV Analysis Based Arrhythmic Beat Detection Using kNN Classifier
Abstract:Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339067Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1539
 Xue, S., Chen, X., Fang, Z., Xia, S., “An ECG arrhythmia classification and heart rate variability analysis system based on android platform”, 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare, Beijing, China, 2015, pp. 1-5.
 Jambukia, S.H., Dabhi, V.K., Prajapati, H.B., “Classification of ECG signals using machine learning techniques: A survey”, International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, 2015, pp. 714-721.
 Benmalek, E., Elmhamdi, J., “Arrhythmia ECG signal analysis using non parametric time-frequency technique”, International Conference on Electrical and Information Technologies, Marrakech, 2015, pp. 281-285.
 Sherbakova, T.F., Sherbakov, G.I., Sedov, S.S., “Cardiac arrhythmia analysis used in the system of electrocardiosignal transmission to the supervision center”, International Conference on Antenna Theory and Techniques, Kharkiv, 2015, pp. 1-3.
 Timus, O., “Sleep Respiration Disorders Diagnosis and Classification Utilizing Soft Computing Algorithms”, Phd. thesis, Natural and Applied Sciences Eletronics and Computer Education Department, Kocaeli University, Kocaeli, Turkey, 2015.
 Sarma, P., Nirmala, S.R., Sarma, K.K., “ECG classification using wavelet subband energy based features”, International Conference on Signal Processing and Integrated Networks, Noida, 2014, pp. 785-790.
 Zairi, H., Kedir-Talha, M., Benouar, S., Ait-Amer, A., “Intelligent system for detecting cardiac arrhythmia on FPGA”, 5th International Conference on Information and Communication Systems, Irbid, 2014, pp. 1-5.
 Boonperm, P., Supakasemwong, D., Naiyanetr, P., “ECG analyzing program for arrhythmia detection”, 7th Biomedical Engineering International Conference, Fukuoka, 2014, pp. 1-4.
 Balachandran, A., Ganesan, M., Sumesh, E.P., “Daubechies algorithm for highly accurate ECG feature extraction”, International Green Computing Communication and Electrical Engineering, Coimbatore, 2014, pp. 1-5.
 O. Yakut, S. Solak, E. Dogru Bolat, “Measuring ECG Signal Using e-Health Sensor Platform”, International Conference on Chemistry, Biomedical and Environment Engineering, Antalya, 2014, pp. 71-75.
 O. Yakut, S. Solak, E. Dogru Bolat, “Implementation of a Web-Based Wireless ECG Measuring and Recording System”, 17th International Conference on Medical Physics and Medical Sciences, Istanbul, 2015, vol. 9(10), pp. 815-818.
 MIT-BIH Arrhythmia Database. Available: http://physionet.org (accessed October, 2015).
 Wang, H.M., Sheng-Chieh, H., “SDNN/RMSSD as a Surrogate for LF/HF: A Revised Investigation”, Modelling and Simulation in Engineering, vol. 2012, pp. 1-8, June, 2012.
 Kampouraki, A., Manis, G., Nikou, C., “Heartbeat Time Series Classification with Support Vector Machines”, IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 512-518, July, 2009.
 http://gim.unmc.edu/dxtests/roc3.htm (accessed October, 2015).