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We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k‐nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross‐validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs. [ABSTRACT FROM AUTHOR]

Background: Although emotion-specific autonomic responses based on the discrete theory of emotion have been widely studied, studies on the reliability of physiological responses to emotional stimuli are limited. In this study, we aimed to assess the reliability of physiological changes induced by the six basic emotions (happiness, sadness, anger, fear, disgust, and surprise) that were measured during 10 weekly repeated experiments. Methods: Twelve college students participated, and in each experiment, physiological signals were collected before and while participants were watching emotion-provoking film clips. Additionally, the participants self-evaluated the emotions that they experienced during the film presentation at the end of each emotional stimulus. To avoid adaptation of participants to identical stimuli during repeated measurements, we used 10 different film clips for each emotion, and thus a total of 60 film clips over 10 weeks were used. Physiological features, such as skin conductance level (SCL), fingertip temperature (FT), heart rate (HR), and blood volume pulse (BVP), were extracted from the physiological signals. Two reliability indices, Cronbach's alpha and intraclass correlation coefficient, were calculated from the physiological features to assess internal consistency and interrater reliability, respectively. Results: We found that SCL, HR, and BVP measured during the emotion-provoking phase over the 10 weekly sessions were more reliable than those assessed at baseline. Furthermore, SCL, HR, and BVP from the emotion-provoking phase exhibited excellent internal consistency and interrater reliability. Conclusions: Our findings suggest that these features can be used as reliable physiological indices in emotion studies. The results also support the significance of physiological signals as meaningful indicators for emotion recognition in HCI (human computer interface) area. [ABSTRACT FROM AUTHOR]

• Higher pre-treatment leptin is associated with better treatment response on panic symptoms in MDD patients. • Higher pre-treatment IL-6 is associated with worse treatment response on panic symptoms in PD patients. • Individualized predictive biomarker research is neccessary for clinical practice. Peripheral biomarkers have been studied to predict treatment response of panic symptoms. We hypothesized that depressive disorder (MDD) vs. panic disorder (PD) would exhibit different peripheral biomarkers, and their correlation with severity of panic attacks (PA) would also differ. Forty-one MDD patients, 52 PD patients, and 59 healthy controls were followed for 12 weeks. We measured peripheral biomarkers along with the Panic Disorder Severity Scale (PDSS) at each visit—pre-treatment, 2, 4, 8, and 12 weeks on a regular schedule. Peripheral biomarkers including serum cytokines, plasma and serum brain-derived neurotrophic factor (BDNF), leptin, adiponectin, and C-reactive protein (CRP) were quantified using enzyme-linked immunosorbent assay (ELISA). Patients with MDD and PD demonstrated significantly higher levels of pre-treatment IL-6 compared to controls, but no differences were seen in plasma and serum BDNF, leptin, adiponectin, and CRP. Pre-treatment leptin showed a significant clinical correlation with reduction of panic symptoms in MDD patients at visit 5 (p = 0.011), whereas pre-treatment IL-6 showed a negative correlation with panic symptom reduction in PD patients (p = 0.022). An improvement in three panic-related items was observed to be positively correlated with pre-treatment leptin in MDD patients: distress during PA, anticipatory anxiety, and occupational interference. Higher pre-treatment leptin was associated with better response to treatment regarding panic symptoms in patients with MDD, while higher IL-6 was associated with worse response regarding panic symptoms in PD patients. Different predictive peripheral biomarkers observed in MDD and PD suggest the need for establishing individualized predictive biomarkers, even in cases of similar symptoms observed in different disorders. [ABSTRACT FROM AUTHOR]