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首页> 外文会议>IEEE International Conference on Wireless Communications, Signal Processing and Networking >Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals
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Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals

机译：从脑电信号对癫痫进行稳健分类的KNN分类器和K-means聚类性能分析

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Epilepsy is a neurological disorder which affects persons of all age. The brain waves are studied for epilepsy detection. The Electroencephalogram (EEG) is the simplest diagnostic technique available for brain wave analysis. In this paper, we investigate the performance of KNN classifier and K-means clustering for the classification of epilepsy risk level from EEG signals. To identify the non linearity present in the data, detrend analysis is done. An EEG record of twenty patients is analyzed. The power spectral density is determined which is further used for dimensionality reduction. The performance index achieved by KNN classifier and K-means clustering are 78.31% and 93.02% respectively. A high Quality value of 22.37 with K-means clustering and a low value of 18.02 are obtained with KNN classifier. The results show that K-means outperforms KNN classifier in epilepsy risk level classification.
机译：癫痫病是一种影响所有年龄段人群的神经系统疾病。研究了脑电波用于癫痫检测。脑电图（EEG）是可用于脑电波分析的最简单的诊断技术。在本文中，我们研究了KNN分类器和K-means聚类在从EEG信号分类癫痫风险水平中的性能。为了确定数据中存在的非线性，进行了趋势下降分析。分析了二十名患者的脑电图记录。确定功率谱密度，其进一步用于降维。通过KNN分类器和K-means聚类获得的性能指标分别为78.31％和93.02％。使用KNN分类器可获得22.37的高质量值和K-means聚类的低值18.02。结果表明，在癫痫风险等级分类中，K-means优于KNN分类器。

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