Seizure detection from EEG signal plays important role in diagnosing and treating the Epilepsy disease. Development of Low complexity detection algorithms is needed in order to design efficient automatic epilepsy detection devices. In this paper, an automatic seizure detection algorithm proposed using Discrete Wavelet Transform and Cluster-based Nearest Neighborhood machine learning algorithm. The Electroencephalogram signals decomposed by Daubechies Wavelet transform. Temporal features extracted from decomposed Wavelet sub-bands. A new distance-based feature selection method introduced for an optimal feature selection. The proposed Cluster-based KNN algorithm reduces the number of computations required for conventional KNN method. The performance of proposed algorithm is validated by publically available benchmark EEG database. This proposed Classification method obtained 100% accuracy between seizure and normal EEG signals; 98% of accuracy between Inter-ictal and seizure signals, 91% of accuracy between Normal and Inter-ictal signals. This proposed cluster nearest neighborhood classifier requires less number of training samples and less number of calculation steps to detect seizure events. The analysis on classification performance between the various frequency bands confirms that, the EEG signal frequency band of 2.6–5.5 Hz reveals better classification results in adults. Due to less complexity of algorithm, the proposed algorithm is well suited for hardware implementation of automatic seizure detection systems.

脑电信号的癫痫发作检测在癫痫病的诊断和治疗中具有重要作用。需要开发低复杂度检测算法以设计高效的自动癫痫检测设备。在本文中，提出了一种使用离散小波变换和基于聚类的最近邻机器学习算法的自动癫痫检测算法。Daubechies小波变换分解的脑电信号。从分解的小波子带中提取的时间特征。为优化特征选择引入了一种新的基于距离的特征选择方法。提出的基于集群的 KNN 算法减少了传统 KNN 方法所需的计算次数。所提出算法的性能由公开可用的基准 EEG 数据库验证。这种提出的分类方法在癫痫发作和正常脑电信号之间获得了 100% 的准确率；发作间期和癫痫发作信号之间的准确度为 98%，正常和发作间期信号之间的准确度为 91%。这种建议的聚类最近邻域分类器需要较少数量的训练样本和较少数量的计算步骤来检测癫痫发作事件。对各频段间分类性能的分析证实，2.6-5.5Hz的脑电信号频段在成人中表现出更好的分类效果。由于算法的复杂性较低，所提出的算法非常适合自动扣押检测系统的硬件实现。正常和发作间期信号之间的准确度为 91%。这种建议的聚类最近邻域分类器需要较少数量的训练样本和较少数量的计算步骤来检测癫痫发作事件。对各频段间分类性能的分析证实，2.6-5.5Hz的脑电信号频段在成人中表现出更好的分类效果。由于算法的复杂性较低，所提出的算法非常适合自动扣押检测系统的硬件实现。正常和发作间期信号之间的准确度为 91%。这种建议的聚类最近邻域分类器需要较少数量的训练样本和较少数量的计算步骤来检测癫痫发作事件。对各频段间分类性能的分析证实，2.6-5.5Hz的脑电信号频段在成人中表现出更好的分类效果。由于算法的复杂性较低，所提出的算法非常适合自动扣押检测系统的硬件实现。2.6-5.5 Hz 的 EEG 信号频带在成人中显示出更好的分类结果。由于算法的复杂性较低，所提出的算法非常适合自动扣押检测系统的硬件实现。2.6-5.5 Hz 的 EEG 信号频带在成人中显示出更好的分类结果。由于算法的复杂性较低，所提出的算法非常适合自动扣押检测系统的硬件实现。