Deep Learning for Interictal Epileptiform Discharge Detection from Scalp EEG Recordings

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Catarina Lourenço (Autor) Outra; Marleen C. Tjepkema-Cloostermans (Autor) Outra; Luís F. Teixeira (Autor) FEUP; Michel J. A. M. van Putten (Autor) Outra

Ata de Conferência Internacional

Título: XV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING - MEDICON 2019

Páginas: 1984-1997

15th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON)

UNESCO World Heritage Univ, Coimbra, PORTUGAL, SEP 26-28, 2019

Indexação

ISI Web of Knowledge - 20 Citações

Scopus - 23 Citações

Outras Informações

ID Authenticus: P-00R-F4M

DOI: 10.1007/978-3-030-31635-8_237

Abstract (EN): Interictal Epileptiform Discharge (IED) detection in EEG signals is widely used in the diagnosis of epilepsy. Visual analysis of EEGs by experts remains the gold standard, outperforming current computer algorithms. Deep learning methods can be an automated way to perform this task. We trained a VGG network using 2-s EEG epochs from patients with focal and generalized epilepsy (39 and 40 patients, respectively, 1977 epochs total) and 53 normal controls (110770 epochs). Five-fold cross-validation was performed on the training set. Model performance was assessed on an independent set (734 IEDs from 20 patients with focal and generalized epilepsy and 23040 normal epochs from 14 controls). Network visualization techniques (filter visualization and occlusion) were applied. The VGG yielded an Area Under the ROC Curve (AUC) of 0.96 (95% Confidence Interval (CI) = 0.95 - 0.97). At 99% specificity, the sensitivity was 79% and only one sample was misclassified per two minutes of analyzed EEG. Filter visualization showed that filters from higher level layers display patches of activity indicative of IED detection. Occlusion showed that the model correctly identified IED shapes. We show that deep neural networks can reliably identify IEDs, which may lead to a fundamental shift in clinical EEG analysis.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14