Deep learning based automatic detection and dipole estimation of epileptic discharges in MEG: a multi-center study

• Published in: Scientific reports Vol. 14; no. 1; pp. 24574 - 11
• Main Authors: Hirano, Ryoji, Asai, Miyako, Nakasato, Nobukazu, Kanno, Akitake, Uda, Takehiro, Tsuyuguchi, Naohiro, Yoshimura, Masaki, Shigihara, Yoshihito, Okada, Toyoji, Hirata, Masayuki
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116/1925; 631/378/116/2396; 639/166/985; 692/617/375/178; Adolescent; Adult; Automation; Classification; Deep Learning; Epilepsy; Epilepsy - diagnosis; Epilepsy - physiopathology; Female; Firing pattern; Humanities and Social Sciences; Humans; Magnetoencephalography; Magnetoencephalography - methods; Male; Middle Aged; Multicenter study; multidisciplinary; Science; Science (multidisciplinary); Segmentation; Time series analysis; Young Adult; Deep learning; Segmentation; Magnetoencephalography; Time series analysis; Multicenter study; Classification
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75370-9

Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy. However, dipole estimation, a commonly used analysis method for MEG, can be time-consuming since it necessitates neurophysiologists to manually identify epileptic spikes. To reduce this burden, we developed the a...