Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

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The prevalence of anxiety was high, whereas for depression and stress were considerably low. Gender was the only factor significantly associated with anxiety. Other factors were not associated with depression, anxiety and stress. Future research should aim to gain better understanding on unique factors that affect female and male medical officers' anxiety level in emergency setting, thus guide authorities to chart strategic plans to remedy this condition.

Background Stress and burnout commonly threaten the mental health of medical students in Malaysia and elsewhere. This study aimed to explore the interrelations of psychological distress, emotional intelligence, personality traits, academic stress, and burnout among medical students. Methods A cross-sectional study was conducted with 241 medical students. Validated questionnaires were administered to measure burnout, psychological distress, emotional intelligence, personality traits, and academic stress, respectively. A structural equation modelling analysis was performed by AMOS. Results The results suggested a structural model with good fit indices, in which psychological distress and academic stress were noted to have direct and indirect effects on burnout. The burnout levels significantly increased with the rise of psychological distress and academic stress. Neuroticism was only found to have significant indirect effects on burnout, whereby burnout increased when neuroticism increased. Emotional intelligence had a significant direct effect on lowering burnout with the incremental increase of emotional intelligence, but it was significantly reduced by psychological distress and neuroticism. Conclusion This study showed significant effects that psychological distress, emotional intelligence, academic stress, and neuroticism have on burnout. Academic stress and neuroticism significantly increased psychological distress, leading to an increased burnout level, while emotional intelligence had a significant direct effect on reducing burnout; however, this relationship was compromised by psychological distress and neuroticism, leading to increased burnout. Several practical recommendations for medical educators, medical students, and medical schools are discussed.

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

Introduction Methadone is a full agonist of the opioid receptor mu 1 which is encoded by the OPRM1 gene. Sleep disorders were frequently reported by opioid-dependent patients during methadone maintenance therapy (MMT). It is possible, therefore, that genetic polymorphisms in OPRM1 influence sleep quality among patients on MMT. This study investigated the association of OPRM1 polymorphisms with sleep quality among opioid-dependent patients on MMT. Methods The sleep quality of 165 male opioid-dependent patients receiving MMT was evaluated using the Pittsburgh Sleep Quality Index (PSQI). DNA was extracted from whole blood and subjected to polymerase chain reaction (PCR) genotyping. Results Patients with IVS2 + 691 CC genotype had higher PSQI scores [mean (SD) = 5.73 (2.89)] compared to those without the IVS2 + 691 CC genotype (IVS2 + 691 GG/GC genotype) [4.92 (2.31)], but the difference did not reach statistical significance ( p = 0.081). Patients with combined 118 AA genotype and IVS2 + 691 GC genotype (AC/AG diplotype) had significantly lower PSQI scores [mean (SD) = 4.25 (2.27)] compared to those without the diplotype [5.68 (2.77)] ( p = 0.018). Conclusion Our study indicates that the AC/AG diplotype for the 118A>G and IVS2 + 691G>C polymorphisms of OPRM1 gene is associated with better sleep quality among males with opioid dependence on MMT.