Automatic diagnosis of neurological diseases using MEG signals with a deep neural network

• Published in: Scientific reports Vol. 9; no. 1; p. 5057
• Main Authors: Aoe, Jo, Fukuma, Ryohei, Yanagisawa, Takufumi, Harada, Tatsuya, Tanaka, Masataka, Kobayashi, Maki, Inoue, You, Yamamoto, Shota, Ohnishi, Yuichiro, Kishima, Haruhiko
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.03.2019; Nature Publishing Group
• Subjects: 692/617/375/178; 692/617/375/1824; 9/10; Brain Mapping - methods; Case-Control Studies; Computational Biology - methods; Data processing; Diagnosis; Diagnosis, Differential; Epilepsy; Epilepsy - diagnosis; Female; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Magnetoencephalography; Magnetoencephalography - methods; Male; Medical imaging; multidisciplinary; Nervous System Diseases - diagnosis; Nervous System Diseases - etiology; Neural networks; Neural Networks, Computer; Neuroimaging; Neurological diseases; Pattern recognition; Reproducibility of Results; Science; Science (multidisciplinary); Sensitivity and Specificity; Spinal cord injuries
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-41500-x

The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological diseases, leading to better diagnoses. In this study, we developed MNet, a novel deep neural network to classify multiple neurological diseases using resting-state magnetoencephalography (MEG) signals. We used the MEG signals of 67 healthy subjects, 26 patients with spinal cord injury, and 140 patients with epilepsy to train and test the network using 10-fold cross-validation. The trained MNet succeeded in classifying the healthy subjects and those with the two neurological diseases with an accuracy of 70.7 ± 10.6%, which significantly exceeded the accuracy of 63.4 ± 12.7% calculated from relative powers of six frequency bands (δ: 1–4 Hz; θ: 4–8 Hz; low-α: 8–10 Hz; high-α: 10–13 Hz; β: 13–30 Hz; low-γ: 30–50 Hz) for each channel using a support vector machine as a classifier ( p  = 4.2 × 10 −2 ). The specificity of classification for each disease ranged from 86–94%. Our results suggest that this technique would be useful for developing a classifier that will improve neurological diagnoses and allow high specificity in identifying diseases.