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Absence seizure detection based on embedded machine learning
Name: Pimchanok Chatchavalvong
Organization :
Chulalongkorn University. Faculty of Engineering. Department of Electrical Engineering
Email :
6470239821@student.chula.ac.th
Name: Apiwat Lek-uthai
Organization :
Chulalongkorn University. Faculty of Engineering. Department of Electrical Engineering
Email :
apiwat.l@chula.ac.th
Name: Krisnachai Chomtho
Organization :
Chulalongkorn University. Faculty of Medicine. Department of Pediatrics
Email :
kchomtho@yahoo.com
Name: Pakpum Somboon
Organization :
Chulalongkorn University. Faculty of Engineering. Department of Biomedical Engineering
Email :
pakpum.s@chula.ac.th
Name: Suree Pumrin
Organization :
Chulalongkorn University. Faculty of Engineering. Department of Electrical Engineering
Email :
suree.p@chula.ac.th
LCSH:
Absence seizures
-- Diagnosis
LCSH:
Nervous system
-- Diseases
LCSH:
Biomedical engineering
LCSH:
Artificial intelligence
Abstract:
Epilepsy affects about 50 million people worldwide, making it one of the most common neurological diseases. One type of seizure is absence seizures, which are typically short, sudden lapses of consciousness, often going unnoticed and delaying diagnosis. Most absence seizure detection methods involve machine learning and deep learning techniques; however, their algorithms are not implemented on an ultra-low-power microcontroller as an edge device. In this paper, we present the implementation of absence seizure detection algorithms on an ultra-low-power STM32L432KC microcontroller based on the Long Short-Term Memory network using the absence seizure dataset. The algorithm reaches a sensitivity of 84.6%, specificity of 95%, and F1-score of 79.4% for time-based evaluation and a sensitivity of 100% and F1-score of 85.7% for episode-based evaluation. Our proposed network implemented on STM32L432KC microcontroller performs 720,021 Multiply-Accumulate (MACC) operations and requires 32.89 KB RAM and 35.04 KB Flash, suitable for low-power microcontrollers due to their low computational and memory use.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2026-06-05
Issued:
2026-06-05
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI) Association, Thailand. 2024 21th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2024) (pp.374-379). Piscataway, NJ : IEEE
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.041535 วินาที
Pimchanok Chatchavalvong
| Title | Contributor | Type |
|---|
Apiwat Lek-uthai
| Title | Contributor | Type |
|---|---|---|
| A comparison of cuff-less blood pressure estimation between pulse arrival time and pulse transit time using photoplethysmography
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Tipnirin Vajanarat;Apiwat Lek-uthai | บทความ/Article | |
| Absence seizure detection based on embedded machine learning
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pimchanok Chatchavalvong;Apiwat Lek-uthai;Krisnachai Chomtho;Pakpum Somboon;Suree Pumrin | บทความ/Article |
Krisnachai Chomtho
| Title | Contributor | Type |
|---|---|---|
| Absence seizure detection based on embedded machine learning
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pimchanok Chatchavalvong;Apiwat Lek-uthai;Krisnachai Chomtho;Pakpum Somboon;Suree Pumrin | บทความ/Article |
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