EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems
(2023) 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023- Abstract
- Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately detect epileptic seizures. However, training deep learning networks requires a large amount of data and computing resources, which is a major challenge for resource-constrained wearable systems. In this paper, we propose EpilepsyNet, the first interpretable self-supervised network tailored to resource-constrained devices without using any seizure... (More)
- Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately detect epileptic seizures. However, training deep learning networks requires a large amount of data and computing resources, which is a major challenge for resource-constrained wearable systems. In this paper, we propose EpilepsyNet, the first interpretable self-supervised network tailored to resource-constrained devices without using any seizure data in its initial offline training. At runtime, however, once a seizure is detected, it can be incorporated into our self-supervised technique to improve seizure detection performance, without the need to retrain our learning model, hence incurring no energy overheads. Our self-supervised approach can reach a detection performance of 79.2%, which is on par with the state-of-the-art fully-supervised deep neural networks trained on seizure data. At the same time, our proposed approach can be deployed in resource-constrained wearable devices, reaching up to 1.3 days of battery life on a single charge. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e4a4cd5d-8d49-4b6c-b662-91d6f36e7178
- author
- Huang, Baichuan LU ; Zanetti, Renato ; Abtahi Fahliani, Azra LU ; Atienza, David and Aminifar, Amir LU
- organization
- publishing date
- 2023-04-03
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023
- conference location
- Hangzhou, China
- conference dates
- 2023-06-11 - 2023-06-13
- external identifiers
-
- scopus:85164127952
- ISBN
- 979-8-3503-3267-4
- DOI
- 10.1109/AICAS57966.2023.10168560
- language
- English
- LU publication?
- yes
- id
- e4a4cd5d-8d49-4b6c-b662-91d6f36e7178
- date added to LUP
- 2023-04-06 10:38:44
- date last changed
- 2024-07-04 04:01:53
@inproceedings{e4a4cd5d-8d49-4b6c-b662-91d6f36e7178, abstract = {{Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately detect epileptic seizures. However, training deep learning networks requires a large amount of data and computing resources, which is a major challenge for resource-constrained wearable systems. In this paper, we propose EpilepsyNet, the first interpretable self-supervised network tailored to resource-constrained devices without using any seizure data in its initial offline training. At runtime, however, once a seizure is detected, it can be incorporated into our self-supervised technique to improve seizure detection performance, without the need to retrain our learning model, hence incurring no energy overheads. Our self-supervised approach can reach a detection performance of 79.2%, which is on par with the state-of-the-art fully-supervised deep neural networks trained on seizure data. At the same time, our proposed approach can be deployed in resource-constrained wearable devices, reaching up to 1.3 days of battery life on a single charge.}}, author = {{Huang, Baichuan and Zanetti, Renato and Abtahi Fahliani, Azra and Atienza, David and Aminifar, Amir}}, booktitle = {{IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)}}, isbn = {{979-8-3503-3267-4}}, language = {{eng}}, month = {{04}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems}}, url = {{https://lup.lub.lu.se/search/files/143062423/2023086402.pdf}}, doi = {{10.1109/AICAS57966.2023.10168560}}, year = {{2023}}, }