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

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.

Figure 1

Brief architecture of the MNet. Features extracted by the convolutional layers and the relative powers of the six frequency bands are concatenated before fully connected layer 13. Output size depends on classification patterns: two for binary classification and three for classification of two diseases and healthy subjects. Conv: convolutional layer; Fc: fully connected layer; HS: healthy subjects; EP: patients with epilepsy; SCI: patients with spinal cord injury.

Figure 2

MEG signals labelled with high probability by MNet. The figure shows representative 800-ms MEG signals that were correctly classified by the MNet with high probability for a (a) patient with epilepsy, (b) healthy subject, and (c) patient with spinal cord injury. The probabilities of their labels were 99.9%, 99.1%, and 83.2%, respectively. The descriptions located at the left of waves (LF11 to RP43) indicate the MEG channel positions.

Figure 3

Power spectrums of MEG signals labelled with high probability by MNet. Panels (a–c) show the log power spectrums of the whole MEG signals of the same subjects as Fig. 2. Color represents the logarithm of power; (d) shows the log power spectrum averaged over all channels shown in (a–c). In all cases, the logarithm of power was calculated by applying Welch’s power spectral density estimate using a Hamming window of length 800 ms for each channel, and by taking logarithms.