ESeiz: An Edge-Device for Accurate Seizure Detection for Smart Healthcare

Md Abu Sayeed, Saraju P. Mohanty, Elias Kougianos, Hitten P. Zaveri

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

Epilepsy is one of the most common neurological disorders affecting a significant portion of the world's population and approximately 2.5 million people in the United States. Important biomedical research effort is focused on the development of energy efficient devices for the real-time detection of seizures. In this paper, we propose an Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA). The proposed system analyzes neural signals continuously and extracts the hyper-synchronous pulses for the detection of seizure onset. Within a time frame, if the number of pulses exceeds a predefined threshold value, a seizure is declared. The SRA reduces false detections, which in turn enhances the accuracy of the seizure detector. The design was validated using system-level simulations and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry can lead to reduction of power consumption compared to many contemporary approaches. The proposed approach can be generalized to other sensor modalities and the use of wearable or implantable solutions, or a combination of the two.

Original languageEnglish
Article number8726143
Pages (from-to)379-387
Number of pages9
JournalIEEE Transactions on Consumer Electronics
Volume65
Issue number3
DOIs
StatePublished - Aug 2019

Keywords

  • IoMT
  • Smart healthcare
  • automated detection
  • electroencephalography
  • energy efficient systems
  • epilepsy
  • low latency systems
  • seizure
  • wearables

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