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A portable multi-channel depression tendency evaluation system based on emotional stimulation task

A multi-channel, portable technology, applied in medical science, psychological devices, diagnostic signal processing, etc., can solve single and complex problems

Active Publication Date: 2019-01-08
WONDERLAB ADAI TECH BEIJING CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, some studies have used machine learning algorithms to explore the assessment of major depressive disorder, but most of them are based on single-channel information, relying on supervised learning algorithms to process physiological signals, and the measurement often requires the use of more complex equipment

Method used

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  • A portable multi-channel depression tendency evaluation system based on emotional stimulation task
  • A portable multi-channel depression tendency evaluation system based on emotional stimulation task
  • A portable multi-channel depression tendency evaluation system based on emotional stimulation task

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0029] The system hardware of the present invention includes: a wearable EEG collection device, a heart rate collection device, and a skin electricity collection device. Among them, the EEG sensor is a contact dry electrode; the heart rate uses a heart rate sensor to record changes in ECG, and the sensor is a medical patch electrode; the skin electricity uses a skin electric device, and the two sensors are respectively fixed on the non-dominant index finger of the subject. and the middle finger to collect the changes of skin galvanic.

Embodiment 2

[0031] A portable multi-channel method for assessing depression tendency based on emotional stimulation tasks, its features include:

[0032] (1) Standardized emotional stimulation step: present standardized emotional stimulation to the subjects, including static emotional stimulation and dynamic emotional stimulation;

[0033] (2) Physiological signal collection step: collecting multi-channel bioelectrical signals of subjects when completing emotional stimulation tasks, including brain electricity, skin electricity, and electrocardiogram;

[0034](3) Behavioral data collection step: collecting multi-channel behavioral data of the subject when completing the emotional stimulation task, including eye movement information, voice information, two-dimensional image information and three-dimensional depth image information;

[0035] (4) Machine learning data processing steps: use supervised learning and unsupervised learning algorithms to process physiological signals and behavior ...

Embodiment 3

[0048] The specific operation method of the system described in embodiment 3 is specifically:

[0049] Before data collection, the depressed subjects to be evaluated need to wear EEG, skin electricity, and heart rate physiological collection equipment. After wearing all the equipment, the main tester left the subject alone in the laboratory to complete the next test, and the main tester did not intervene during the process.

[0050] Subjects completed two tasks consecutively as required. Before the first test task begins, there is a baseline measurement in which the subjects are asked to sit calmly and gaze at a computer monitor. After the baseline measurement is over, go to the first task. The first task is a static emotional stimulation task. Four pictures will be randomly presented on the monitor at the same time, and the subjects can freely look at the pictures that appear on the screen. Images contain both positive and negative categories. The second task is a dynamic...

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Abstract

The present invention provides a portable multi-channel depression tendency evaluation system based on emotional stimulation task. In a mood stimulation task, Electroencephalogram (EEG), electrodermatogram (ECG), electrocardiogram (ECG), eye movement (EOM), speech and image information were collected by portable equipment. Supervised learning and unsupervised learning algorithms were used to extract features from multi-channel physiological signals and behavioral data, to screen features, to train and validate models, to integrate the results of multi-channel analysis, to calculate depressionpropensity index (DPI) and to evaluate depression propensity.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, based on comprehensive information such as brain electricity, skin electricity, electrocardiogram, eye movement, voice and video information, and using machine learning algorithms to evaluate depression tendencies, and specifically relates to a portable multi-channel depression tendency based on emotional stimulation tasks evaluation system. Background technique [0002] Major depressive disorder (Major Depression) is a typical disease among depressive disorders. It is characterized by well-defined episodes of at least 2 weeks involving marked changes in affective, cognitive, and autonomic function. Studies have shown that the 12-month prevalence of major depressive disorder is about 7%, and it is one of the most common mental illnesses (DSM-5, 2013). [0003] Major depressive disorder has long been a hot issue in the field of mental health, and a lot of research has been carried out on t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/16
CPCA61B5/165A61B5/72A61B5/7267A61B5/7275
Inventor 李岱丁欣放毕成
Owner WONDERLAB ADAI TECH BEIJING CO LTD
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