RSeiz: A channel selection based approach for rapid seizure detection in the IoMT

Md Abu Sayeed, Saraju Mohanty, Elias Kougianos, Laavanya Rachakonda

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

3 Scopus citations

Abstract

Epilepsy affects 1% of the world population, which necessitates a fast seizure detection system for practical epilepsy solutions. The reduction of seizure detection delay is a critical problem which needs to be addressed as rapid detection provides effective treatment. In this paper an electroencephalogram (EEG) based, patient-specific seizure detection system is proposed in the Internet of Medical Things (IoMT) framework which can detect seizures at a minimum delay. The proposed system uses neighborhood component analysis (NCA) for channel selection, statistical features for optimal feature extraction, and a ReliefFbased optimization (RBO) in conjunction with a k-nearest neighbor classifier for feature classification. A publicly available database (CHB-MIT EEG) has been used for evaluation of the proposed algorithm. The simulation results show that the proposed algorithm provides a sensitivity of 100% while maintaining a low average latency of 1.49 sec, which may be useful for practical epilepsy treatment and biomedical applications.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Symposium on Smart Electronic Systems, iSES 2019
Pages105-110
Number of pages6
ISBN (Electronic)9781728146553
DOIs
StatePublished - Dec 2019
Event5th IEEE International Symposium on Smart Electronic Systems, iSES 2019 - Rourkela, India
Duration: 16 Dec 201918 Dec 2019

Publication series

NameProceedings - 2019 IEEE International Symposium on Smart Electronic Systems, iSES 2019

Conference

Conference5th IEEE International Symposium on Smart Electronic Systems, iSES 2019
Country/TerritoryIndia
CityRourkela
Period16/12/1918/12/19

Keywords

  • Electroencephalogram (EEG)
  • Epilepsy
  • Internet of Medical Things (IoMT)
  • Seizure Detection
  • Seizure Early Detection
  • Smart Healthcare

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