An Edge-computing Platform for Low-Latency and Low-power Wearable Medical Devices for Epilepsy

Md Abu Sayeed, Fatahia Nasrin

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

2 Scopus citations

Abstract

Epilepsy is a neurological disorder that affects 1% of people globally. The development of a portable, low-power, and low-latency wearable sensor is a growing need to address epilepsy. An edge-computing-based wearable sensor has been presented that uses a pulse exclusion mechanism (PEM) and a random forest classifier to identify seizures at a reduced delay and minimal power consumption. Datasets recorded from the scalp electrode are utilized to demonstrate the feasibility of using the method as a wearable medical device. Including the edge-IoT platform in place of cloud IoT offers a considerable reduction in system latency. The optimized edge-computing platform reduces power usage significantly compared to existing methods. The reduced latency and battery usage make the proposed device faster and more energy-efficient, which may be useful for low-power wearable devices.

Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems, WMCS 2023
ISBN (Electronic)9798350338805
DOIs
StatePublished - 2023
Event2023 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems, WMCS 2023 - Waco, United States
Duration: 19 Apr 202320 Apr 2023

Publication series

NameProceedings of the 2023 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems, WMCS 2023

Conference

Conference2023 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems, WMCS 2023
Country/TerritoryUnited States
CityWaco
Period19/04/2320/04/23

Keywords

  • Edge-computing
  • Electroencephalography (EEG)
  • Feature Reduction
  • Internet of Things (IoT)
  • Wearable Device

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