Automated Rest EEG-Based Diagnosis of Depression and Schizophrenia Using a Deep Convolutional Neural Network

Abstract

Depression (DP) and schizophrenia (SCZ) are both highly prevalent psychiatric disorders, and their diagnosis depends on the examination of symptoms and clinical tests, which can be subjective. As a measure of real-time neural activity, Electroencephalographic (EEG) has shown its usability to classify people either as normal or as having DP or SCZ, but automatic classification between the three categories (DP, SCZ and the normal) was rarely reported. Here, we propose an automatic diagnostic framework based on a convolutional neural network called the Multi-Channel Frequency Network (MUCHf-Net), which automatically learns feature representations of EEGs that characterize them as normal, DP, or SCZ. Two EEG databases were used in this study, the first one contains EEGs from 300 individuals (DP: 100, SCZ: 100, normal: 100) collecting from our hospital, and the second contains EEGs from 30 individuals (DP: 10, SCZ: 10, normal: 10) from public available datasets, and the spectrum matrices from these multi-channel EEGs were feed into MUCHf-Net. The results showed that: (1) MUCHf-Net accurately distinguished normal EEGs from DP or SCZ EEGs (accuracy: 91.12%; F1 score: 0.8947); (2) low-frequency bands (delta, theta, alpha) contributed the most important information to the classification model; (3) features located in the frontal and parietal lobes contributed more than other regions did; (4) MUCHf-Net fine-tuned on public datasets also had high classification accuracy: 87.71% (triple: normal, SCZ or DP) and 79.27% (binary: psychiatric disorders (DP or SCZ) or normal). Our study shows that deep leaning has the potential to become an important tool for assisting in the diagnosis of psychiatric disorders.

DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network

“…The physiological function of the brain is captured by the EEG signal [5], which can be recorded using various feature extraction techniques. The EEG signal can be used to detect numerous neurological diseases, such as Seizures [6], Schizophrenia [7], [8], [9], [10], Parkinson's [11], Autism [12], Depression [13], [14], Sleep disorders, Anxiety [15], and Alzheimer [16]. Linear approaches may not be suitable for detecting complex dynamic variations in EEG signals.…”

Preclinical Diagnosis of Schizophrenia using EEGTime-Frequency Analysis During Motor Function and verbal Fluency Tests

“…Zhiming Wang et al suggested Multi-Channel Frequency (MUCHFs)-Nets method, which is a deep learning model used for extracting the features from the EEG pattern spectrum and classifying it as schizophrenia, depression, and normal people. The results show that the EEG signal interacts with the deep neural network, it can be utilized to classify individuals as either normal or having depression and schizophrenia 18 .…”

“…Schizophrenia patients are at high risk of ending their life by suicide, due to hallucinations. Further, the existing related works [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] predict schizophrenia with less accuracy, and older age people are not subjected to the detection. These research gaps motivate the research in detecting both younger and older persons having schizophrenia with early diagnosis of delusion and hallucination with high accuracy.…”