Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain's underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional brain-template structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.

Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.

Can Evolutionary-based Brain Map Be Used as a Complementary Diagnostic Tool with fMRI, CT and PET for Schizophrenic Patients?

OBJECTIVE: In this research, a new approach termed as "evolutionary-based brain map" is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (EEG) features. METHODS: Particle swarm optimization (PSO) is employed to find discriminative frequency bands from different EEG channels. By deploying the energy of those selected frequency bands from different channels within each time frame (window) on the scalp geometry, a sort of two dimensional points along with their values are created; by applying Lagrange interpolation, an image can be constructed. Finally, by averaging the images belonging to successive time frames, an evolutionary-based brain map is created. RESULTS: In this study, twenty subjects from each group voluntarily participated and their EEG signals were caught from 20 channels. The energy of selected bands for different channels are arranged in a feature vector for each time frame and applied to Fisher linear discriminant analysis (FLDA) resulting in 83.74% diagnostic accuracy between the two groups. The achieved result by the proposed method was much higher than applying the energy of standard EEG bands (delta, theta, alpha, beta and gamma) to the same classifier which just provided 77.04% accuracy. Applying T-test to the achieved results supports the supremacy of the proposed method as an automatic powerful diagnostic tool. CONCLUSION: The proposed brain map is capable of highlighting the same physiological and anatomical changes which are observed in fMRI, PET and CT as differentiable indicators between controls and schizophrenic patients.

Diagnosis of the OCD Patients using Drawing Features of the Bender Gestalt Shapes.

BACKGROUND: Since psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the psychologists cannot assess their patients while running the tests. One of the famous drawing tests to measure the degree of Obsession Compulsion Disorder (OCD) is the Bender Gestalt, though its reliability is not promising. OBJECTIVE: The main objective of this study is to make the Bender Gestalt test quantitative; therefore, an optical pen along with a digital tablet is utilized to preserve the key drawing features of OCD patients during the test. MATERIALS AND METHODS: Among a large population of patients who referred to a special clinic of OCD, 50 under therapy subjects voluntarily took part in this study. In contrast, 50 subjects with no sign of OCD performed the test as a control group. This test contains 9 shapes and the participants were not constraint to draw the shapes in a certain interval of time; consequently, to classify the stream of feature vectors (samples through drawing) Hidden Markov Model (HMM) is employed and its flexibility increased by incorporating the fuzzy technique into its learning scheme. RESULTS: Applying fuzzy HMM classifier to the data stream of subjects could classify two groups up to 95.2% accuracy, whereas the results by applying the standard HMM resulted in 94.5%. In addition, multi-layer perceptron (MLP), as a strong static classifier, is applied to the features and resulted in 86.6% accuracy. CONCLUSION: Applying the pair of T-test to the results implies a significant supremacy of the fuzzy HMM to the standard HMM and MLP classifiers.

Serum total antioxidant capacity status of HTLV-1 infected patients.

Many aspects of the pathogenesis of Human T-cell lymphotropic virus type 1 (HTLV-1) still need further elucidations. Previous studies have indicated that oxidative stress occurs during infection with the other retrovirus, human immunodeficiency virus 1 (HIV-1). Similar results have been observed in some other chronic viral infections including hepatitis B (HBV) and hepatitis C (HCV). In order to reveal possible oxidative stress in HTLV-1-infected patients, we evaluated serum total antioxidant capacity (TAC) as an indicator of oxidative stress in these patients. Forty-four HTLV-1-seropositive individuals were included in this study, consisting of 12 symptomatic and 32 asymptomatic (carrier) cases. Controls consisted of 36 apparently healthy, HTLV-1-, HIV- and hepatitis-seronegative individuals. All symptomatic patients had HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Serum TAC levels in patients and healthy individuals were measured using a quantitative TAC assay. The antioxidant capacity in HTLV-1-seropositive cases was significantly reduced compared to control group (P = 0.001). In addition, TAC was lower in patients with more than 5 years history of HAM/TSP compared to those with ≤5 years duration of the myelopathy (P = 0.03). Our results show a depletion of TAC during HTLV-1 infection, which intensifies along with the disease progress. This finding indicates a role of the oxidative stress in pathogenesis of HTLV-1. These results may prompt further research to evaluate any possible therapeutic effect of antioxidant dietary supplements for HTLV-1 infected individuals.

A hybrid classifier for characterizing motor unit action potentials in diagnosing neuromuscular disorders.

BACKGROUND: The time and frequency features of motor unit action potentials (MUAPs) extracted from electromyographic (EMG) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. However, the results of conventional automatic diagnosis methods using MUAP features is not convincing yet. OBJECTIVE: The main goal in designing a MUAP characterization system is obtaining high classification accuracy to be used in clinical decision system. For this aim, in this study, a robust classifier is proposed to improve MUAP classification performance in estimating the class label (myopathic, neuropathic and normal) of a given MUAP. METHOD: The proposed scheme employs both time and time-frequency features of a MUAP along with an ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture. Time domain features includes phase, turn, peak to peak amplitude, area, and duration of the MUAP. Time-frequency features are discrete wavelet transform coefficients of the MUAP. RESULTS: Evaluation results of the developed system using EMG signals of 23 subjects (7 with myopathic, 8 with neuropathic and 8 with no diseases)  showed that the system estimated the class label of MUAPs extracted from these signals with average of accuracy of 91% which is at least 5% higher than the accuracy of two previously presented methods. CONCLUSION: Using different optimized subsets of features along with the presented hybrid classifier results in a classification accuracy that is encouraging to be used in clinical applications for MUAP characterization. 

Khorasan Disease: Prevalence of HTLV-I Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP) In West Azarbaijan from 2004 to 2007.

BACKGROUND: Because of the low prevalence of Human T Lymphotropic Virus type I (HTLV-I) in comparison with Khorasan Province, considering HTLV-I as an etiology of spastic paraparesia, it may be neglected in evaluation of spastic paraparesis in the other regions of Iran. Some reports of spastic paraparetic patients due to HTLV-I infection in West Azarbaijan, caused us to reconsider the importance of HTLV-I epidemiology in the other areas of the country. METHODS: All spastic paraparetic patients who referred to Motahari and Imam Khomeini educational hospitals of Urmia from September 2004 to September 2007 were assessed for HTLV-I infection antibodies. RESULTS: In our 3 years study, 11 cases were diagnosed as Human T Lymphotropic Virus type I Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP, 2 males and 9 females).The mean age of patients at the time of diagnosis was 45.8 years. Dorsal and cervical MRI of all patients was normal. Serum Enzyme-Linked Immuno-Sorbent Assay (ELISA) and Western blot (WB) for anti HTLV-I antibody in all patients was positive. Four patients underwent for lumbar puncture in which were normal in respect of cells and biochemistry, but positive for anti-HLTLV-I antibodies. CONCLUSION: HAM/TSP detection in West Azarbaijan in spite of its long distance from Khorasan Province shows the importance of anti-HTLV-I Ab assay in the blood and CSF of every spastic paraparetic patient all over the country.

A comparison approach toward finding the best feature and classifier in cue-based BCI.

In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain-Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.