Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images

• Published in: NeuroImage clinical Vol. 22; p. 101684
• Main Authors: Emami, Ali, Kunii, Naoto, Matsuo, Takeshi, Shinozaki, Takashi, Kawai, Kensuke, Takahashi, Hirokazu
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2019; Elsevier
• Subjects: Adolescent; Adult; Child; Convolutional neural networks; Deep Learning; Electroencephalography - methods; Electroencephalography - standards; Epilepsies, Partial - diagnosis; Epileptic seizure; Female; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Male; Middle Aged; Radiology; Regular; Scalp; Scalp electroencephalogram; Seizure detection; Seizures - diagnosis; Sensitivity and Specificity; Young Adult; CNN; STD; Sz; PLE; OLE; PFA; TLE; Epileptic seizure; Deep learning; rec; Seizure detection; FIAS; MF; DMF; PWE; RSA; ROC; Sup; Sb; RED; FBTCS; FAS; Convolutional neural networks; Scalp electroencephalogram; FLE; TS; seizure; subject; rhythmic slow activity; recording; occipital lobe epilepsy; multifocal; suppression; parietal lobe epilepsy; receiver operating characteristic; repetitive epileptiform discharge; temporal lobe epilepsy; patients with epilepsy; paroxysmal fast activity; focal aware seizure; focal to bilateral tonic-clonic seizure; frontal lobe epilepsy; focal impaired awareness seizure; diffuse multifocal; tonic seizure; standard deviation

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural network...