Annals of Computer Science and Information Systems, Volume 14

DOI: http://dx.doi.org/10.15439/2017KM42

Citation: Proceedings of the 2017 International Conference on Information Technology and Knowledge Management, Ajay Jaiswal, Vijender Kumar Solanki, Zhongyu (Joan) Lu, Nikhil Rajput (eds). ACSIS, Vol. 14, pages 97–100 (2017)

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Abstract. There is an increase in cardio logical patients all over the world due to change in modern life style. It forces the medical researchers to search for smart devices that can diagnosis and predict the onset of cardiac problem before it is too late. This motivates the authors to predict Arrhythmia that can help both the patients and the medical practitioners for better healthcare services. The proposed method uses the frequency domain information which can represent the ECG signals of Arrhythmia patients better. Features representing the MIT-BIH Arrhythmia are extracted using the efficient Short Time Fourier Transform and the Wavelet transform. A comparison of these features is made with that of normal human being using Neural Network based classifier. Wavelet based features has shown an improvement of Accuracy over that of STFT features in classifying Arrhythmia as our results reveal. A Mean Square Error (MSE) of with wavelet transform has validated our results.

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