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EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

Huang, Baichuan LU orcid ; Zanetti, Renato ; Abtahi Fahliani, Azra LU ; Atienza, David and Aminifar, Amir LU orcid (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)
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organization
publishing date
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
language
English
LU publication?
yes
id
e4a4cd5d-8d49-4b6c-b662-91d6f36e7178
date added to LUP
2023-04-06 10:38:44
date last changed
2023-09-14 14:32:11
@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)}},
  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}},
  year         = {{2023}},
}