YUE-LOONG HSIN2026-10-162026-10-162009-01-01Pure ID: 15159205Pure UUID: 536e505b-cc78-4888-9770-ce35bfce86d310.1109/ICNC.2009.639https://www.scopus.com/pages/publications/77950604556https://scholars.csmu.edu.tw/handle/123456789/33225Sudden unexpected death in epilepsy (SUDEP) is the top of death rate of epilepsy population. To develop an accurate, realizable, personalized automatic epilepsy spike detection method is valuable for understanding epilepsy and preventing the possible loss. In this research, a novel spike detection method based on ant κ-means (AK) clustering is proposed. By compare with other intelligent computing methods, our results show that AK worked successfully well in our epilepsy patient data with 100% sensitivity, 96% specificity, and 97.9% accuracy. Although the EEG analysis system still has room for improving, the preliminary results are encouraging for future developments.5enjournal article