Hardware-Efficient 1D CNN for Patient-Specific Early Seizure Detection.
Annu Int Conf IEEE Eng Med Biol Soc
; 2023: 1-4, 2023 07.
Article
em En
| MEDLINE
| ID: mdl-38083071
ABSTRACT
Closed-loop brain-implantable neuromodulation devices are a new treatment option for patients with refractory epilepsy. Seizure detection algorithms implemented on such devices are subject to strict power and area constraints. Deep learning methods, though very powerful, tend to have high computational complexity and thus are typically impractical for resource-constrained neuromodulation devices. In this paper, we propose a compact and hardware-efficient one-dimensional convolutional neural network (1D CNN) structure for patient-specific early seizure detection. Feature extraction techniques and a novel initialization method based on the forward-chaining training and testing scheme are used to improve model performance. Our compact model achieves similar accuracy to that of support vector machines, the state-of-the-art method for seizure detection, while consuming over 20x less power.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Convulsões
/
Eletroencefalografia
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article