Lightweight Machine Learning for Seizure Detection on Wearable Devices
(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:
https://lup.lub.lu.se/record/321c7d64-bd20-45f4-890e-0673f19cf057
- author
- Huang, Baichuan LU ; Abtahi Fahliani, Azra LU and Aminifar, Amir LU
- organization
- publishing date
- 2023
- 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
- 2024-03-17 04:02:46
@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}}, }