Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier
This work addresses early seizure detection for biomedical engineering, but it appears incremental as it combines existing statistical and classification methods.
The paper tackled spike-and-wave detection in epileptic EEG signals using a t-location-scale distribution and K-nearest neighbors classifier, achieving performance evaluated via accuracy, sensitivity, and specificity on a real dataset.
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.