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AN EFFICIENT ARRHYTHMIC HEARTBEAT CLASSIFICATION METHOD USING ECG MORPHOLOGY BASED FEATURES


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AN EFFICIENT ARRHYTHMIC HEARTBEAT CLASSIFICATION METHOD USING ECG MORPHOLOGY BASED FEATURES

Kaynakça Chazal D. P., (2013, September). A switching feature extraction system for ECG heartbeat classification. In Computing in Cardiology 2013 (pp. 955-958). IEEE. Chazal D. P. & Reilly, R. B., (2006). A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 53(12), 2535-2543. Chazal D. P., O'Dwyer, M., & Reilly, R. B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 51(7), 1196-1206. EC57, A. A., & Association for the Advancement of Medical Instrumentation. (1998). Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. Association for the Advancement of Medical Instrumentation, Arlington, VA. Google Colaboratory, (15.11.2020), Available: https://colab.research.google.com/. Jiang, W., & Kong, S. G. (2007). Block-based neural networks for personalized ECG signal classification. IEEE Transactions on Neural Networks, 18(6), 1750-1761. Mark R., Moody G., MIT-BIH Arrhythmia Database, http://www. physionet.org/physiobank/database/mitdb/. (Ziyaret Tarihi 30.11.2020). Martis, R. J., Acharya, U. R., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2013). Cardiac decision making using higher order spectra. Biomedical Signal Processing and Control, 8(2), 193-203. Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50. Raj, S. (2020, May). An Efficient Analysis Scheme for Intelligent ECG Monitoring Devices. In 2020 Zooming Innovation in Consumer Technologies Conference (ZINC) (pp. 207-212). IEEE. SelectKBest Sklearn feature selection, (30.11.2020), Available:https://scikitlearn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest. Scikit-learn Machine Learning in Python, (28.11.2020), Available: https://scikitlearn.org/stable/index.html. Yakut, O., Solak, S., & Bolat, E. D. (2018). IIR Based Digital Filter Design for Denoising the ECG Signal. Journal of Polytechnic, 21(1), 173-181. Yakut, Ö., & Bolat, E. D. (2018). An improved QRS complex detection method having low computational load. Biomedical Signal Processing and Control, 42, 230-241. Yakut O., (2018). Classification of Arrhytmias in ECG Signal Using Soft Computing Algorithms. PhD, Kocaeli University, Kocaeli, Turkey. Yakut, O., Timus, O., & Bolat, E. D. (2016). HRV Analysis Based Arrhythmic Beat Detection Using kNN Classifier. International Journal of Biomedical and Biological Engineering, 10(2), 60-63. Yakut, O., Solak, S., & Bolat, E. D. (2014, October). Measuring ECG signal using e-health sensor platform. In International Conference

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