Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity, and specificity.

翻译：脑电图（EEG）信号中的模式分类是生物医学工程中的一个重要问题，因为它能够检测大脑活动，特别是癫痫发作的早期检测。本文提出了一种基于t位置尺度统计表示的癫痫EEG信号K近邻分类方法，用于检测棘慢波。所提出的方法在一个包含棘慢波事件和正常脑功能信号的真实数据集上得到了验证，并通过分类准确率、灵敏度和特异性评估了其性能。