Comparison of patient specific and general classification of epileptic seizure prediction

Yasmin M. Massoud, Levin Kuhlmann, Mohamed A.Abd El Ghany

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Epilepsy is a neurological disorder in which abnormal brain activity occurs, causing seizures. Recent studies have used machine learning techniques to produce a seizure classification system. In this work, two aspects of seizure classification are discussed and compared in terms of accuracy and efficacy. Seizure classification can follow a patient specific or general approach. For a patient specific approach, feature extraction and classification are performed for each patient independently. However, a general approach means data is trained and classified for all patients at once. Results show that AUC of general approach is 0.74 which is higher than that of patient-specific 0.71. Computational time is decreased when using patient-specific approach to 8 hours, while general approach requires 10 hours for training and prediction.

Original languageEnglish
Title of host publication2021 International Conference on Microelectronics, ICM 2021
EditorsThang Manh Hoang, Ta Son Xuat
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781665408394
ISBN (Print)9781665408400
Publication statusPublished - 2021
Externally publishedYes
EventInternational Conference on Microelectronics 2021 - New Cairo City, Egypt
Duration: 19 Dec 202122 Dec 2021 (Proceedings)


ConferenceInternational Conference on Microelectronics 2021
Abbreviated titleICM 2021
CityNew Cairo City
Internet address


  • classification
  • Epilepsy
  • prediction
  • seizure

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