Classification of Arrhythmia using KNN-Classifier
Keywords:
Cumulants, Discrete Wavelet Transform, K-NN classifierAbstract
In this paper we are introducing a new methodology called discrete wavelet transform along with the higher order statistics (HOS). The feature vectors give the information about the original signal, these feature vectors are a compressed version of the original signal. In this paper we are using the discrete wavelet transform (DWT) for the calculation of feature vectors. In this methodology we have mainly three stages. In the first stage data is collected from the MIT/BIH database, after collecting the data segmentation is done. In the second stage, we perform DWT on the segmented data. In this process the data is mainly divided into approximated coefficients and detailed coefficients. In the third stage, we are calculating HOS (cumulants) for the detailed coefficients. Finally the signal is given to the decision making device, according to the threshold value the beats are classified faithfully. In this process we are getting Precision 96.30%, Recall 96.30% and by using K-NN classifier.
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