eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizure Detection using Pulse Exclusion Mechanism

Md Abu Sayeed, Fatahia Nasrin, Saraju P. Mohanty, Elias Kougianos

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

1 Scopus citations

Abstract

Epilepsy is a neurological disorder marked by recurrent seizures. At least 3 million Americans and 1% of the global population have epilepsy, requiring a low-latency seizure detection system necessary for effective epilepsy treatment. In this paper, a pulse exclusion mechanism (PEM) based novel seizure detection system has been presented in the internet of medical things (IoMT), which uses a PEM to eliminate unnecessary features or channels and allocate desired pulses in a time frame. An optimized deep neural network (DNN) algorithm is used for feature classification. The proposed approach has been evaluated using CHB-MIT Scalp database. The results of the experiments indicate that the proposed eSeiz 2.0 offers a high specificity of 100% and a low latency of 1.05 sec, which can be useful for wearable biomedical applications as well as real-world epilepsy treatment.

Original languageEnglish
Title of host publicationProceedings - 2022 OITS International Conference on Information Technology, OCIT 2022
Pages108-112
Number of pages5
ISBN (Electronic)9781665493482
DOIs
StatePublished - 2022
Event20th OITS International Conference on Information Technology, OCIT 2022 - Bhubaneswar, India
Duration: 14 Dec 202216 Dec 2022

Publication series

NameProceedings - 2022 OITS International Conference on Information Technology, OCIT 2022

Conference

Conference20th OITS International Conference on Information Technology, OCIT 2022
Country/TerritoryIndia
CityBhubaneswar
Period14/12/2216/12/22

Keywords

  • Epilepsy
  • Feature Extraction
  • Internet of Things (IoT)
  • Low Latency System
  • Pulse Exclusion Mechanism (PEM)

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