IDDS: An edge-device in IoMT for automatic seizure control using on-time drug delivery

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

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

10 Scopus citations

Abstract

Epilepsy affects around 1% of the world population, necessitating wearable or implantable solutions for seizure control. In this paper, an Internet of medical things (IoMT) based unified drug delivery system (iDDS) is proposed for automatic seizure detection and control. iDDS consists of two units: a seizure detection unit and a drug delivery unit. Seizure detection is performed in real time using statistical feature extraction and a deep neural network (DNN) classifier. Once a detection is complete, the drug is injected into the target area using a piezoelectric actuated valveless double reservoir micropump. iDDS presents a unique piezoelectric actuated double-reservoir based drug delivery system for fault-tolerance as well as better drug control. The proposed system was implemented in Simulink® and Thinkspeak, which reported a sensitivity of 100% and an average latency of 1.8 sec for the selected dataset.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics, ICCE 2020
ISBN (Electronic)9781728151861
DOIs
StatePublished - Jan 2020
Event2020 IEEE International Conference on Consumer Electronics, ICCE 2020 - Las Vegas, United States
Duration: 4 Jan 20206 Jan 2020

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2020-January
ISSN (Print)0747-668X

Conference

Conference2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Country/TerritoryUnited States
CityLas Vegas
Period4/01/206/01/20

Keywords

  • Deep neural network (DNN)
  • Double Reservoir Micropump
  • Drug Delivery System (DDS)
  • Epilepsy
  • Implantable
  • Internet of Medical Things
  • Seizure
  • Smart Healthcare

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