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

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...

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Published inScientific 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
LanguageEnglish
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
Online AccessGet full text
ISSN2045-2322
2045-2322
DOI10.1038/s41598-019-41500-x

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Abstract 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.
AbstractList 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 ). 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.
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.
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.
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.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.
ArticleNumber 5057
Author Fukuma, Ryohei
Aoe, Jo
Kobayashi, Maki
Tanaka, Masataka
Inoue, You
Ohnishi, Yuichiro
Harada, Tatsuya
Kishima, Haruhiko
Yamamoto, Shota
Yanagisawa, Takufumi
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SSID ssj0000529419
Score 2.4521792
Snippet The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep...
SourceID pubmedcentral
proquest
pubmed
crossref
springer
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 5057
SubjectTerms 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
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Title Automatic diagnosis of neurological diseases using MEG signals with a deep neural network
URI https://link.springer.com/article/10.1038/s41598-019-41500-x
https://www.ncbi.nlm.nih.gov/pubmed/30911028
https://www.proquest.com/docview/2197742525
https://www.proquest.com/docview/2197895289
https://pubmed.ncbi.nlm.nih.gov/PMC6433906
Volume 9
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