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Depression: HELP
Articles by Han Young Yu
Based on 8 articles published since 2010
(Why 8 articles?)
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Between 2010 and 2020, Han Young Yu wrote the following 8 articles about Depression.
 
+ Citations + Abstracts
1 Clinical Trial Skin conductance responses in Major Depressive Disorder (MDD) under mental arithmetic stress. 2019

Kim, Ah Young / Jang, Eun Hye / Choi, Kwan Woo / Jeon, Hong Jin / Byun, Sangwon / Sim, Joo Yong / Choi, Jae Hun / Yu, Han Young. ·Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute(ETRI), Gajeong-Ro, Yoseong-Gu, Daejeon, Rep. of Korea. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro, Gangnam-gu, Seoul, Rep. of Korea. · Department of Electronics Engineering, Incheon National University, Incheon, Korea. ·PLoS One · Pubmed #30943195.

ABSTRACT: 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.

2 Clinical Trial Heart rate variability for treatment response between patients with major depressive disorder versus panic disorder: A 12-week follow-up study. 2019

Choi, Kwan Woo / Jang, Eun Hye / Kim, Ah Young / Fava, Maurizio / Mischoulon, David / Papakostas, George I / Kim, Dong Jun / Kim, Kiwon / Yu, Han Young / Jeon, Hong Jin. ·Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea. · Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), 218 Gajeong-ro, Daejeon, Korea. · Depression Clinical and Research Program, Massachusetts General Hospital, Harvard Medical School, Boston, USA. · Department of Health Sciences & Technology, Department of Medical Device Management & Research, and Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea; Department of Health Sciences & Technology, Department of Medical Device Management & Research, and Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea. Electronic address: jeonhj@skku.edu. ·J Affect Disord · Pubmed #30583140.

ABSTRACT: 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.

3 Article Pre-treatment peripheral biomarkers associated with treatment response in panic symptoms in patients with major depressive disorder and panic disorder: A 12-week follow-up study. 2019

Kim, Kiwon / Jang, Eun Hye / Kim, Ah Young / Fava, Maurizio / Mischoulon, David / Papakostas, George I / Kim, Hyewon / Na, Eun Jin / Yu, Han Young / Jeon, Hong Jin. ·Department of Psychiatry, Veteran Health Service Medical Center, Seoul, South Korea; Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. · Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Republic of Korea. · Depression Clinical and Research Program, Massachusetts General Hospital, Harvard Medical School, Boston, USA. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Health Sciences & Technology, Department of Medical Device Management & Research, and Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea. Electronic address: jeonhj@skku.edu. ·Compr Psychiatry · Pubmed #31669792.

ABSTRACT: 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.

4 Article Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol. 2019

Byun, Sangwon / Kim, Ah Young / Jang, Eun Hye / Kim, Seunghwan / Choi, Kwan Woo / Yu, Han Young / Jeon, Hong Jin. ·Department of Electronics Engineering, Incheon National University, 22012, Incheon, South Korea. Electronic address: swbyun@inu.ac.kr. · Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), 34129, Daejeon, South Korea. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06351, Seoul, South Korea; Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, 02841, South Korea. · Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), 34129, Daejeon, South Korea. Electronic address: uhan0@etri.re.kr. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06351, Seoul, South Korea. Electronic address: jeonhj@skku.edu. ·Comput Biol Med · Pubmed #31404718.

ABSTRACT: 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.

5 Article Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study. 2019

Byun, Sangwon / Kim, Ah Young / Jang, Eun Hye / Kim, Seunghwan / Choi, Kwan Woo / Yu, Han Young / Jeon, Hong Jin. ·Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea. · Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea. · Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea. ·Technol Health Care · Pubmed #31045557.

ABSTRACT: 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.

6 Article Automatic detection of major depressive disorder using electrodermal activity. 2018

Kim, Ah Young / Jang, Eun Hye / Kim, Seunghwan / Choi, Kwan Woo / Jeon, Hong Jin / Yu, Han Young / Byun, Sangwon. ·Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea. · Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. · Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea. uhan0@etri.re.kr. · Department of Electronics Engineering, Incheon National University, Incheon, Korea. swbyun@inu.ac.kr. ·Sci Rep · Pubmed #30451895.

ABSTRACT: 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.

7 Article Leptin is associated with mood status and metabolic homeostasis in patients with bipolar disorder. 2014

Lee, Hyun Jeong / Kim, Se Hyun / Kim, Eun Young / Lee, Nam Young / Yu, Han Young / Kim, Yong Sik / Ahn, Yong Min. ·Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea. ·Neuropsychobiology · Pubmed #25471890.

ABSTRACT: BACKGROUND: Patients with bipolar disorder are at a high risk for becoming obese. Adipokines are associated with depression and obesity via the inflammatory process. However, few studies have investigated the associations between depression and leptin, adiponectin and resistin levels in patients with bipolar disorder. We explored the associations between serum levels of leptin, adiponectin and resistin and mood and metabolic status in patients with bipolar disorder. METHODS: Body mass index (BMI) and serum leptin, adiponectin and resistin levels were assessed in 94 Korean patients with bipolar disorder. The Hamilton Rating Scale for Depression-17 and the Young Mania Rating Scale were used to assess mood state. RESULTS: Leptin (17.19 ± 13.08 vs. 10.47 ± 10.05 ng/ml; p = 0.008) and adiponectin (10.51 ± 8.37 vs. 5.91 ± 2.82 μg/ml; p = 0.001) levels were higher in female than in male patients. After adjusting for mood state, age, smoking, alcohol habit, and BMI in a multivariate analysis of covariance (MANCOVA), leptin (17.86 ± 1.22 vs. 10.05 ± 1.48 ng/ml; p < 0.001) and adiponectin (10.18 ± 0.98 vs. 6.40 ± 1.19 μg/ml; p = 0.027) levels were still higher in female than in male patients. Compared to euthymic patients, depressed patients had higher levels of leptin (17.37 ± 14.69 vs. 11.65 ± 9.04 ng/ml; p = 0.024), but there was no significant difference in adiponectin and resistin levels between the two groups. After adjusting for age, gender and BMI in the MANCOVA, leptin levels were also significantly higher in depressed (16.78 ± 1.34 ng/ml) than in euthymic patients (10.73 ± 1.22 ng/ml; p = 0.001). CONCLUSION: Leptin is closely associated with the regulation of mood and metabolic homeostasis in patients with bipolar disorder.

8 Article Assessment of risk factors related to suicide attempts in patients with bipolar disorder. 2012

Song, Joo Yun / Yu, Han Young / Kim, Se Hyun / Hwang, Samuel S-H / Cho, Hyun-Sang / Kim, Yong Sik / Ha, Kyooseob / Ahn, Yong Min. ·Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Republic of Korea. ·J Nerv Ment Dis · Pubmed #23124183.

ABSTRACT: We compared the characteristics of patients with bipolar disorder with and without a history of suicide attempts to identify the risk factors of suicide in this disorder. Among 212 patients with bipolar disorder, 44 (21.2%) patients had histories of suicide attempts. Suicide attempters were younger and more likely to be diagnosed with bipolar II. The variables that differentiated those who did from those who did not attempt suicide included age at first contact, lifetime history of antidepressant use, major depressive episode, mixed episode, auditory hallucinations, rapid cycling, the number of previous mood episodes, age of first depressive episode, and age of first psychotic symptoms. Strong predictors of suicide attempts were younger age at onset, lifetime history of auditory hallucinations, and history of antidepressant use. Antecedent depressive episodes and psychotic symptoms predicted the first suicide attempt in patients with bipolar disorder. This study could help clinicians to understand the major risk factors of suicidal behavior in bipolar disorder.