ObjectiveThis study was aimed to compare the accuracy of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) in the detection of manic state of bipolar disorders (BD) of single patients and multiple patients. Methods21 hospitalized BD patients (14 females, average age 34.5±15.3) were recruited after admission. Spontaneous speech was collected through a preloaded smartphone. Firstly, speech features [pitch, formants, mel-frequency cepstrum coefficients (MFCC), linear prediction cepstral coefficient (LPCC), gamma-tone frequency cepstral coefficients (GFCC) etc.] were preprocessed and extracted. Then, speech features were selected using the features of between-class variance and within-class variance. The manic state of patients was then detected by SVM and GMM methods. ResultsLPCC demonstrated the best discrimination efficiency. The accuracy of manic state detection for single patients was much better using SVM method than GMM method. The detection accuracy for multiple patients was higher using GMM method than SVM method. ConclusionSVM provided an appropriate tool for detecting manic state for single patients, whereas GMM worked better for multiple patients’ manic state detection. Both of them could help doctors and patients for better diagnosis and mood state monitoring in different situations.

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Purpose The study aims to clarify the negative psychological state and resilience impairments of schizophrenia (SCZ) with metabolic syndrome (MetS) while evaluating their potential as risk factors. Patients and Methods We recruited 143 individuals and divided them into three groups. Participants were evaluated using the Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD)-24, Hamilton Anxiety Rating Scale (HAMA)-14, Automatic Thoughts Questionnaire (ATQ), Stigma of Mental Illness scale and Connor–Davidson Resilience Scale (CD-RISC). Serum biochemical parameters were measured by automatic biochemistry analyzer. Results The score of ATQ was highest in the MetS group (F = 14.5, p < 0.001), and the total score of CD-RISC, subscale tenacity score and subscale strength score of CD-RISC were lowest in the MetS group (F = 8.54, p < 0.001; F = 5.79, p = 0.004; F = 10.9, p < 0.001). A stepwise regression analysis demonstrated that a negative correlation was observed among the ATQ with employment status, high-density lipoprotein (HDL-C), and CD-RISC (β=−0.190, t=−2.297, p = 0.023; β=−0.278, t=−3.437, p = 0.001; β=−0.238, t=−2.904, p = 0.004). A positive correlation was observed among the ATQ with waist, TG, WBC, and stigma (β=0.271, t = 3.340, p = 0.001; β=0.283, t = 3.509, p = 0.001; β=0.231, t = 2.815, p = 0.006; β=0.251, t=−2.504, p = 0.014). The area under the receiver-operating characteristic curve analysis showed that among all independent predictors of ATQ, the TG, waist, HDL-C, CD-RISC, and stigma presented excellent specificity at 0.918, 0.852, 0.759, 0.633, and 0.605, respectively. Conclusion Results suggested that the non-MetS and MetS groups had grievous sense of stigma, particularly, high degree of ATQ and resilience impairment was shown by the MetS group. The TG, waist, HDL-C of metabolic parameters, CD-RISC, and stigma presented excellent specificity to predict ATQ, and the waist showed excellent specificity to predict low resilience level.