In this study, the international affective picture system was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency, and entropy were used to identify normal, moderately, and markedly ill schizophrenic patients. The signals were collected and preprocessed. Then, the signals were separated according to three types of emotions and five frequency bands. Finally, the features were calculated using three different methods of entropy. For classification, the features were divided into different sections and classification using support vector machine (principal components analysis on 95%). Finally, simple regression and correlation analysis between the total scores of positive and negative syndrome scale and features were used. At first, we observed that to classify normal and markedly ill schizophrenic patients, the identification result was as high as 81.5%, and therefore, we further explored moderately and markedly ill schizophrenic patients. Second, the identification rate in both moderately and markedly ill schizophrenic patient was as high as 79.5%, which at the Fz point signal in high valence low arousal fragments was calculated using the ApEn methods. Finally, the total scores of positive and negative syndrome scale were used to analyze the correlation with the features that were the five frequency bands at the Fz point signal. The results show that the p value was less than .001 at the beta wave in the 15–18 Hz frequency range.

We aimed to explore relations between symptomatic remission and functionality evaluation in schizophrenia patients treated with paliperidone extended-release (ER), as seen in a normal day-to-day practice, using flexible dosing regimens of paliperidone ER. We explored symptomatic remission rate in patients treated with flexibly dosed paliperidone ER by 8 items of Positive and Negative Syndrome Scale (PANSS) and change of Personal and Social Performance (PSP) scale.

Late-life depression is common among elderly patients. Ignorance of the health problem, either because of under-diagnosis or under-treatment, causes additional medical cost and comorbidity. For a better health and quality of life (QoL), evaluation, prevention and treatment of late-life depression in elderly patients is essential.

Despite an increasing number of reports on the associations between chronic occupational stress and structural and functional changes of the brain, the underlying neural correlates of perceived occupational stress is still not clear. Perceived stress reflects the extents to which situations are appraised as stressful at a given point in one’s life. Using near-infrared spectroscopy, we investigated the associations between perceived occupational stress and cortical activity over the bilateral frontotemporal regions during a verbal fluency test. Sixty-eight participants (17 men, 51 women), 20–62 years of age were recruited. Perceived occupational stress was measured using the Chinese version of Job Content Questionnaire, and the Chinese version of the Copenhagen Burnout Inventory. We found statistically significant negative associations between occupational burnout and brain cortical activity over the fronto-polar and dorsolateral prefrontal cortex during the VFT (r = −0.343 to −0.464). In conclusion, our research demonstrated a possible neural basis of perceived occupational stress that are distributed across the prefrontal cortex.

The main purpose of this research is to evaluate the differences in the reaction time and accuracy rate of three categories of subjects using our serious games. Thirty-seven subjects were divided into three groups: normal (n₁ = 16), MCI (Mild Cognitive Impairment) (n₂ = 10), and dementia—moderate-to-severe (n₃ = 11) groups based on the MMSE (Mini Mental State Examination). Two serious games were designed: (1) whack-a-mole and (2) hit-the-ball. Two dependent variables, reaction time and accuracy rate, were statistically analyzed to compare elders’ performances in the games among the three groups for three levels of speed: slow, medium, and fast. There were significance differences between the normal group, the MCI group, and the moderate-to-severe dementia group in both the reaction-time and accuracy-rate analyses. We determined that the reaction times of the MCI and dementia groups were shorter compared to those of the normal group, with poorer results also observed in accuracy rate. Therefore, we conclude that our serious games have the feasibility to evaluate reaction performance and could be used in the daily lives of elders followed by clinical treatment in the future.

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some related studies have considered feature selection to remove irrelevant features from the datasets for further performance improvement. However, since the order of combining feature selection and over-sampling can result in different training sets to construct the prediction model, it is unknown which order performs better. In this paper, the information gain (IG) and genetic algorithm (GA) feature selection methods and the synthetic minority over-sampling technique (SMOTE) are used for different combinations. The experimental results based on two breast cancer datasets show that the combination of feature selection and over-sampling outperform the single usage of either feature selection and over-sampling for the highly class imbalanced datasets. In particular, performing IG first and SMOTE second is the better choice. For other datasets with a small class imbalance ratio and a smaller number of features, performing SMOTE is enough to construct an effective prediction model.