A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

2022 ◽  
Vol 72 ◽  
pp. 103370
Author(s):  
Yu Lei ◽  
Abdelkader Nasreddine Belkacem ◽  
Xiaotian Wang ◽  
Sha Sha ◽  
Changming Wang ◽  
...  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chi Qin Lai ◽  
Haidi Ibrahim ◽  
Mohd Zaid Abdullah ◽  
Jafri Malin Abdullah ◽  
Shahrel Azmin Suandi ◽  
...  

Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.


IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S425
Author(s):  
Shin-Young Kang ◽  
Youngwoon Choi ◽  
Seung-Ho Paik ◽  
V. Zephaniah Phillips ◽  
Beop-Min Kim

2020 ◽  
Vol 10 (3) ◽  
pp. 681-687
Author(s):  
Danyang Ma ◽  
Genke Yang ◽  
Zeya Li ◽  
Haichun Liu ◽  
Changchun Pan ◽  
...  

Schizophrenia is a severe mental disorder that can result in hallucinations, delusions, and extremely disordered thinking and behavior. While electroencephalography (EEG) has been used as an auxiliary tool for diagnostic purposes in several recent studies, all EEG channels are treated homogeneously without addressing the dominance of certain channels. The main purpose of this study is to obtain the weight value of each channel as the quantitative representation of influence of each scalp area on the classification of schizophrenia phases, and then to apply the weight values to improve the accuracy of classification. We propose a new convolutional neural network (CNN) structure based on AlexNet to derive weight values as weight layer and classify the samples better. Our results show that the modified CNN structure achieves better performance in terms of time consumption and classification accuracy compared with the original classifier. Also, the visualization of the weight layer in our model indicates possible correlations between scalp areas and schizophrenia conditions, which may benefit future pathological study.


2021 ◽  
Vol 9 ◽  
Author(s):  
Wuxiang Shi ◽  
Yurong Li ◽  
Dujian Xu ◽  
Chen Lin ◽  
Junlin Lan ◽  
...  

Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.


2020 ◽  
Vol 52 (1) ◽  
pp. 38-51
Author(s):  
Caglar Uyulan ◽  
Türker Tekin Ergüzel ◽  
Huseyin Unubol ◽  
Merve Cebi ◽  
Gokben Hizli Sayar ◽  
...  

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.


Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 893
Author(s):  
Tao Zhang ◽  
Cunbo Li ◽  
Peiyang Li ◽  
Yueheng Peng ◽  
Xiaodong Kang ◽  
...  

The accurate identification of an attention deficit hyperactivity disorder (ADHD) subject has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, the traditional methods concerning the classification model and feature extraction usually depend on the single-channel model and static measurements (i.e., functional connectivity, FC) in the small, homogenous single-site dataset, which is limited and may cause the loss of intrinsic information in functional MRI (fMRI). In this study, we proposed a new two-stage network structure by combing a separated channel convolutional neural network (SC-CNN) with an attention-based network (SC-CNN-attention) to discriminate ADHD and healthy controls on a large-scale multi-site database (5 sites and n = 1019). To utilize both intrinsic temporal feature and the interactions of temporal dependent in whole-brain resting-state fMRI, in the first stage of our proposed network structure, a SC- CNN is used to learn the temporal feature of each brain region, and an attention network in the second stage is adopted to capture temporal dependent features among regions and extract fusion features. Using a “leave-one-site-out” cross-validation framework, our proposed method obtained a mean classification accuracy of 68.6% on five different sites, which is higher than those reported in previous studies. The classification results demonstrate that our proposed network is robust to data variants and is also replicated across sites. The combination of the SC-CNN with the attention network is powerful to capture the intrinsic fMRI information to discriminate ADHD across multi-site resting-state fMRI data.


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