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Lightweight Machine Learning for Seizure Detection on Wearable Devices

Huang, Baichuan LU orcid ; Abtahi Fahliani, Azra LU and Aminifar, Amir LU orcid (2023) IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)
Abstract
For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wear- able SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
ICASSP, the International Conference on Acoustics, Speech, and Signal Processing 2023
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)
conference location
Rhodes Island, Greece
conference dates
2023-06-04 - 2023-06-10
external identifiers
  • scopus:85168885694
language
English
LU publication?
yes
id
321c7d64-bd20-45f4-890e-0673f19cf057
date added to LUP
2023-03-26 17:24:59
date last changed
2025-10-14 11:48:24
@inproceedings{321c7d64-bd20-45f4-890e-0673f19cf057,
  abstract     = {{For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wear- able SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection.}},
  author       = {{Huang, Baichuan and Abtahi Fahliani, Azra and Aminifar, Amir}},
  booktitle    = {{ICASSP, the International Conference on Acoustics, Speech, and Signal Processing 2023}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Lightweight Machine Learning for Seizure Detection on Wearable Devices}},
  url          = {{https://lup.lub.lu.se/search/files/141522442/2023051653.pdf}},
  year         = {{2023}},
}