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The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships influencing this work., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Automatic diagnosis of depression based on speech can complement mental health treatment methods in the future. Previous studies have reported that acoustic properties can be used to identify depression. However, few studies have attempted a large-scale differential diagnosis of patients with depressive disorders using acoustic characteristics of non-English speakers., Objective: This study proposes a framework for automatic depression detection using large-scale acoustic characteristics based on the Korean language., Methods: We recruited 153 patients who met the criteria for major depressive disorder and 165 healthy controls without current or past mental illness. Participants' voices were recorded on a smartphone while performing the task of reading predefined text-based sentences. Three approaches were evaluated and compared to detect depression using data sets with text-dependent read speech tasks: conventional machine learning models based on acoustic features, a proposed model that trains and classifies log-Mel spectrograms by applying a deep convolutional neural network (CNN) with a relatively small number of parameters, and models that train and classify log-Mel spectrograms by applying well-known pretrained networks., Results: The acoustic characteristics of the predefined text-based sentence reading automatically detected depression using the proposed CNN model. The highest accuracy achieved with the proposed CNN on the speech data was 78.14%. Our results show that the deep-learned acoustic characteristics lead to better performance than those obtained using the conventional approach and pretrained models., Conclusions: Checking the mood of patients with major depressive disorder and detecting the consistency of objective descriptions are very important research topics. This study suggests that the analysis of speech data recorded while reading text-dependent sentences could help predict depression status automatically by capturing the characteristics of depression. Our method is smartphone based, is easily accessible, and can contribute to the automatic identification of depressive states., (©Ah Young Kim, Eun Hye Jang, Seung-Hwan Lee, Kwang-Yeon Choi, Jeon Gue Park, Hyun-Chool Shin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.01.2023.)

Previous studies have investigated the role of inflammatory markers in suicidality of patients with major depressive disorder (MDD) or panic disorder (PD). However, few studies have investigated associations between serum inflammatory cytokine levels and suicidality. We hypothesized that MDD and PD status might be significantly associated with serum inflammatory cytokines and that we could predict levels of improvement in suicide ideation intensity using serum inflammatory biomarkers in patients with MDD and PD. For this study, 41 patients with MDD, 52 patients with PD, and 59 healthy control (HC) subjects were enrolled. Psychological measurements and serum inflammatory markers such as interleukin (IL) -6, -10, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, and C reactive protein (CRP) were examined. A total of five visits were completed during 12 weeks. After controlling for confounding factors, log-transformed IL-6 (ln&#95;IL-6) at baseline (MDD: 0.297 ± 0.626; PD: 0.342 ± 0.723; HC: -0.121 ± 0.858; p = 0.007, >0.0017, 0.05/30) and mean ln&#95;IL-6 (MDD: 0.395 ± 0.550, PD: 0.249 ± 0.544, HC: -0.139 ± 0.622, p = 0.002, >0.0017, 0.05/30) levels were trends towards significantly higher in patients with MDD and PD than in HC. In MDD patients, a higher level of basal ln&#95;TNF-α was a significant predictor of ΔSSI (changes in SSI scores between baseline and week 12) even after controlling for changes of depression symptoms and baseline SSI scores (standardized β = 0.541, p = 0.002 < 0.0028, 0.05/18). In conclusion, we could predict ΔSSI using baseline inflammatory biomarkers for patients with MDD., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: A link between brain-derived neurotrophic factor (BDNF) expression and the mood regulatory effect of leptin has been suggested in the pathophysiology of major depressive disorder (MDD). We investigated treatment response and pre-treatment leptin and BDNF in patients with MDD and with panic disorder (PD)., Methods: We recruited 41 patients with MDD, 52 patients with PD, and 59 matched healthy controls. All subjects completed five visits (at baseline, 2, 4, 8, and 12 weeks), and both MDD and PD patients were treated with standard pharmacotherapy for 12 weeks. Plasma BDNF (pBDNF) and blood leptin levels were obtained along with a 17-item Hamilton Depression Scale rating (HDRS-17) score at every visit., Results: The ratio of pre-treatment pBDNF to leptin was significantly lower in patients with MDD and PD compared to healthy controls (p = 0.024), but was not associated with severity of depressive or anxiety symptoms. Pre-treatment pBDNF:leptin ratio was significantly higher in treatment responders than in non-responders (p = 0.012) in MDD but not in PD. This difference was larger in MDD patients with appetite loss (p = 0.034). In multivariate analysis, pre-treatment pBDNF:leptin ratio was significantly associated with treatment responsiveness (Adjusted Odds Ration [AOR] = 2.50, 95% CI 1.02-6.14) in MDD., Limitation: small sample size; limited information on detailed pharmacological effects., Conclusions: A relatively higher ratio of pre-treatment pBDNF to leptin was associated with greater treatment response in MDD but not in PD. Further research should focus on exploration of a link between BDNF and leptin underlying neuronal plasticity in depression., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objective: 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., Methods: 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)., Results: 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., Conclusion: 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., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from healthy controls was developed based on linear and nonlinear heart rate variability (HRV), which reflects the autonomic cardiovascular regulation., Methods: HRV data were collected from 37 MDD patients and 41 healthy controls during five 5-min experimental phases: the baseline, a mental stress task, stress recovery, a relaxation task, and relaxation task recovery. The experimental protocol was designed to assess the autonomic responses to stress and recovery. Twenty HRV indices were extracted from each phase, and a total of 100 features were used for classification using a support vector machine (SVM). SVM-recursive feature elimination (RFE) and statistical filter were employed to perform feature selection., Results: We achieved 74.4% accuracy, 73% sensitivity, and 75.6% specificity with two optimal features selected by SVM-RFE, which were extracted from the stress task recovery and mental stress phases. Classification performance worsened when individual phases were used separately as input data, compared to when all phases were included. The SVM-RFE using nonlinear and Poincaré plot HRV features performed better than that using the linear indices and matched the best performance achieved by using all features., Conclusions: We demonstrated the machine learning-based diagnosis of MDD using HRV analysis. Monitoring the changes in linear and nonlinear HRV features for various autonomic nervous system states can facilitate the more objective identification of MDD patients., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states., Competing Interests: The authors have declared that no competing interests exist.

Background: Heart Rate Variability (HRV) parameters have been used to evaluate the autonomic nervous system. We hypothesized that patients with major depressive disorder (MDD) and panic disorder (PD) showed different HRV profiles compared to healthy controls. We also hypothesized that we could predict the responder groups in the MDD and PD patients, using differences in HRV indices between the stress and rest phases., Methods: 28 MDD patients and 29 PD patients were followed for 12 weeks, and we also followed 39 healthy control subjects. We measured HRV parameters at the rest, stress, and recovery phases., Results: Patients with MDD and PD demonstrated lower pNN50 than controls during the stress (F = 7.49, p = 0.001), and recovery phases (F = 9.43, p = 0.0001). Patients with MDD and PD also showed higher LF/HF ratio than controls during the stress phase (F = 6.15, p = 0.002). Responders in the PD group presented a lower level of LF/HF ratio during the stress phase compared to non-responders (F = 10.14, p = 0.002), while responders in the MDD group showed a lower level of heart rate during all three phases, compared to non-responders. Additionally, we could predict treatment response in patients with MDD using ΔLF/HF ratio (OR: 1.33, 95% CI = 1.07-1.65, p = 0.011) and ΔpNN50 (OR: 1.49, 95% CI 1.09-1.77, p = 0.014)., Conclusion: The changes of HRV parameters of pNN50 and LF/HF ratio between the stress and recovery phase may be clinical markers of predictors of treatment responsiveness in MDD and PD patients., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires., Objective: Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated., Methods: The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification., Results: The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2., Conclusion: Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.