Bipolar Disorder Depression, Major Depression
Conditions
Keywords
Bipolar Disorder Depression, Major Depression, Machine Learning, Skin Potential, Micro-Facial Expression, Electroencephalogram
Brief summary
At present, diagnosis and recognition of depression and bipolar disorder are mainly based on subjective evidence such as clinical interview and scale evaluation. The corresponding diagnosis basis has some shortcomings, such as poor diagnostic reliability and failure in early identification of bipolar disorder. Therefore, it is of great significance to explore objective diagnostic indicators to remedy the deficiencies. Therefore，the investigators collect psychological and physiological information data of patients with bipolar disorder and depression.Then the investigators aim to construct and verify the multidimensional emotion recognition model to analyze the personality characteristics, negative emotions and cognitive reactions of different individuals, and form a systematic accurate recognition and evaluation tool.
Interventions
None listed
Sponsors
First Affiliated Hospital of Zhejiang University
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
15 Years to 55 Years
Healthy volunteers
Yes
Inclusion criteria
1. Age 15-55, regardless of gender; 2. The brief International Neuropsychiatric Interview Chinese version (MINI) was used to meet the diagnostic criteria for DSM-IV-TR depressive disorder or bipolar disorder (type I); 3. Total score of Hamilton Depression Scale (HAMD-17) ≥17, and Young's Manic Scale (YMRS) ≤6; 4. Junior high school or above.
Exclusion criteria
1. The patient conforms to DSM-IV schizophrenia and related spectrum disorders. 2. The patient has a history of severe head trauma (loss of consciousness for more than 5 minutes), current or previous history of epilepsy, intracranial hypertension, or other serious neurological diseases; 3. Had a history of alcohol or psychoactive substance abuse/dependence in the 6 months prior to the test; 4. Those considered unsuitable for inclusion by the researcher.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| facial action unit detection | Baseline | The research recruits subjects to look at pictures and videos and then use cameras to record facial microexpressions. Finally, the research uses machine learning methods to analyze facial micro-expressions. Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. |
| event-related potentials | Baseline | An electroencephalogram (EEG) is a test that measures electrical activity in the brain using small, metal discs (electrodes) attached to the scalp. Brain cells communicate via electrical impulses and are active all the time, even during asleep. This activity shows up as wavy lines on an EEG recording. The research recruits subjects to look at videos and pictures and use electroencephalography to record event-related potentials. Finally, we use time domain analysis and frequency analysis to get the results |
| Galvanic skin response | Baseline | The skin also has electrical activity, which is in constant, slight variation, and can be measured and charted. The skin's electrical conductivity fluctuates based on certain bodily conditions, and this fluctuation is called the galvanic skin response.We recruited subjects to watch videos and pictures and record galvanic skin response. Finally, we use time domain analysis and frequency analysis to get the results |
Countries
China
Contacts
Primary ContactHu ShaoHua, M.D
Backup ContactLv HaiLong, M.D
Outcome results
None listed