Towards Automatic Identification of Epileptic Recordings in Long-term EEG Monitoring

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 273 - 276
• Main Authors: Kok, Xuen Hoong, Imtiaz, Syed Anas, Rodriguez-Villegas, Esther
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Algorithms; Electroencephalography; Epilepsy; Epilepsy - diagnosis; Feature extraction; Humans; Reliability; Seizures - diagnosis; Sensitivity; Time-frequency analysis
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630782

Electroencephalogram (EEG) is a crucial tool in the diagnosis and management of epilepsy. The process of analyzing EEG is time consuming leading to the development of seizure detection algorithms to aid its analysis. This approach is limited since it requires seizures to occur during monitoring periods and can often lead to misdiagnosis in cases where seizure occurrence is rare. For such cases, it has been shown that the interictal periods in EEG signals, which is the predominant state in long-term monitoring, can be useful for the diagnosis of epilepsy. This paper presents an algorithm, using the information in interictal periods, to discriminate between long-term EEG recordings of epilepsy patients and healthy subjects. It extracts several time and frequency-time domain features from the signals and classifies them using an ensemble classifier, achieving 100% sensitivity and 98.7% specificity in classifying 267 recordings from 105 subjects. The results demonstrate the feasibility of this approach to reliably identify EEG recordings of epilepsy subjects automatically which can be highly useful to facilitate screening and diagnosis of epilepsy, especially in those parts of the world where there is a lack of trained personnel for interpreting EEG signals.