CN113855021B - Depression tendency evaluation method and device - Google Patents
Depression tendency evaluation method and device Download PDFInfo
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- CN113855021B CN113855021B CN202111179593.9A CN202111179593A CN113855021B CN 113855021 B CN113855021 B CN 113855021B CN 202111179593 A CN202111179593 A CN 202111179593A CN 113855021 B CN113855021 B CN 113855021B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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Abstract
The present disclosure provides a depression tendency assessment method and device, the method comprising: obtaining the reaction time of the testee for making emotion decisions; extracting the characteristics of the reaction time to obtain original characteristic parameters; performing dimension reduction processing on the original characteristic parameters to obtain evaluation characteristic parameters; inputting the evaluation characteristic parameters into a pre-trained overrun learning machine model to obtain depression trend indexes. The method can increase the attention degree of emotion and response speed by carrying out depression tendency assessment through the response time of the testee to make emotion decisions. Compared with the manual evaluation in the related art, the method reduces the influence of subjective factors, reduces the noise of environment and equipment compared with the evaluation by physiological signals in the related art, and is beneficial to improving the accuracy of depression tendency evaluation.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to a depression tendency evaluation method and device.
Background
Depression is a mental disorder characterized by affective disorders, mainly manifested by depressed mood, dysthymia and cognitive dysfunction. According to the 2018 report of the world health organization, more than 3 hundred million people in all ages of the world have depression, and the depression is expected to rise to the 2 nd position next to heart disease in 2020, and the depression can increase the risks of cardiovascular diseases, diabetes and the like, seriously affect daily work, study and life of people, and even cause suicide in serious cases.
Currently, clinical diagnosis of depression is performed by psychiatric physicians by interview mainly because there are no biological markers associated with the symptoms of depression.
With the development and mature application of Artificial Intelligence (AI) technology taking machine learning as a core, a brand new revolution is brought to the field of medical health, and the research of AI+medical treatment is greatly focused in academia, industry and medical field.
The depression recognition method constructed by adopting the machine learning technology uses physiological and behavioral signals, and comprises two modes:
one is classification data obtained in a natural state, including depression classification based on resting brain waves or resting functional magnetic resonance and the like, and using virtual agents to interview acquired multi-modal data such as expression/speech/language in a natural state. The resting brain wave data is difficult to obtain, and the cost for obtaining resting functional magnetic resonance data is high; the information such as expression/voice/language obtained in the natural state cannot be data directly related to emotion, and the mood of a depression patient cannot be accurately represented, so that the depression recognition rate is low.
The other is to use two-mode or multi-mode data such as emotion attention deviation/skin electricity/heart rate/memory/voice/expression/brain wave/eye movement information and the like obtained under emotion stimulation to identify the depression, and the data obtained by the method is directly related to the depressed mood of a patient, but the multi-mode data acquisition time is long, and an acquisition device and an acquisition system are complex and have high cost.
In addition, depression is a mental disorder caused by mood disorder, and is mainly represented by signal anomalies such as mood attention deviation, hypomnesis (mainly represented by working memory), abnormal cognition style, expression/language/voice/brain wave/skin electricity/fMRI/heart rate/eye movement behavior (eye movement track, fixation position, fixation time) and the like, and the higher the severity of depression is, the more obvious the indexes are. However, because the depressed early-stage population does not possess the fundamental features of depression, depression identification methods based on these behavioral and physiological signals are not suitable for early-stage depressed patients.
Disclosure of Invention
The embodiment of the disclosure provides a depression tendency evaluation method and device, which can improve the accuracy of depression tendency evaluation.
For this reason, the embodiment of the present disclosure provides the following technical solutions:
in a first aspect, embodiments of the present disclosure provide a depression trend assessment method, comprising:
obtaining the reaction time of the testee for making emotion decisions;
extracting the characteristics of the reaction time to obtain original characteristic parameters;
performing dimension reduction processing on the original characteristic parameters to obtain evaluation characteristic parameters;
inputting the evaluation characteristic parameters into a pre-trained overrun learning machine model to obtain depression tendency indexes.
Optionally, the raw characteristic parameters include a minimum, a maximum, an upper quartile, a median, a lower quartile, a mean, a standard deviation, kurtosis, and skewness of the reaction time.
Optionally, performing the dimension reduction processing on the original feature parameters to obtain estimated feature parameters includes:
calculating the characteristic value of the original characteristic parameter through principal component analysis;
sorting the characteristic values from big to small;
and acquiring original characteristic parameters corresponding to the first N characteristic values as evaluation characteristic parameters.
Optionally, obtaining the reaction time of the testee to make the emotional decision includes at least one of the following steps:
obtaining the response time of a tested person under calm emotion to make an emotion decision under conflict emotion stimulation;
acquiring the response time of a tested person under calm emotion for making an emotion decision under non-conflict emotion stimulation;
acquiring the response time of a tested person under conflict emotion to make an emotion decision under non-conflict emotion stimulation;
and acquiring the response time of the testee under the non-conflicted emotion to make an emotion decision under the conflicted emotion stimulus.
Optionally, the pre-trained overrun learning model includes a heavy model and a light model;
inputting the estimated characteristic parameters into a pre-trained overrun learning model comprises:
obtaining subjective depression tendency scores of the tested person through a self-evaluation scale;
inputting the evaluation characteristic parameters into a severe model when the subjective depression tendency score is larger than a set value;
and when the subjective depression tendency score is smaller than a set value, inputting the evaluation characteristic parameters into a mild model.
In a second aspect, embodiments of the present disclosure provide a mental state estimation apparatus, including:
the acquisition module is used for acquiring the response time of the testee for making emotion decisions;
the feature extraction module is used for extracting features of the reaction time to obtain original feature parameters;
the dimension reduction module is used for carrying out dimension reduction processing on the original characteristic parameters to obtain evaluation characteristic parameters;
and the data processing module is used for inputting the evaluation characteristic parameters into a pre-trained overrun learning machine model to obtain a depression tendency index.
Optionally, the raw characteristic parameters include a minimum, a maximum, an upper quartile, a median, a lower quartile, a mean, a standard deviation, kurtosis, and skewness of the reaction time.
Optionally, the dimension reduction module includes:
a principal component analysis unit for calculating a feature value of the original feature parameter by principal component analysis;
the sorting unit is used for sorting the characteristic values from big to small;
and the data processing unit is used for acquiring original characteristic parameters corresponding to the first N characteristic values as evaluation characteristic parameters.
Optionally, the acquisition module comprises at least one of the following units:
the first acquisition unit is used for acquiring the response time of the testee under calm emotion to make emotion decisions under the conflicted emotion stimulus;
the second acquisition unit is used for acquiring the response time of the testee under calm emotion for making emotion decisions under non-conflicted emotion stimulation;
the third acquisition unit is used for acquiring the response time of the testee under the conflicted emotion for making emotion decision under the non-conflicted emotion stimulation;
and the fourth acquisition unit is used for acquiring the response time of the testee under the non-conflicted emotion for making the emotion decision under the conflicted emotion stimulus.
Optionally, the pre-trained overrun learning model includes a heavy model and a light model;
the data processing module comprises:
the subjective scoring module is used for acquiring subjective depression tendency scores of the tested person through the self-scoring table;
the first evaluation module is used for inputting the evaluation characteristic parameters into the severe model when the subjective depression tendency score is larger than a set value;
and the second evaluation module is used for inputting the evaluation characteristic parameters into the mild model when the subjective depression tendency score is smaller than a set value.
One or more technical solutions provided in the embodiments of the present disclosure have the following advantages:
the depression tendency evaluation method provided by the embodiment of the disclosure can increase the attention degree to emotion and response speed by performing depression tendency evaluation through the response time of the testee to make emotion decisions. Compared with the manual evaluation in the related art, the method reduces the influence of subjective factors, reduces the noise of environment and equipment compared with the evaluation by physiological signals in the related art, and is beneficial to improving the accuracy of depression tendency evaluation.
Drawings
FIG. 1 is a flow chart of a method of assessing depression trend in accordance with an embodiment of the present disclosure;
fig. 2 is a block diagram of a configuration of a depression tendency evaluation apparatus according to an embodiment of the present disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the drawings and specific language will be used to describe the same. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The described embodiments of the present disclosure are some, but not all embodiments of the present disclosure. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
In the description of the present disclosure, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, technical features related to different embodiments of the present disclosure described below may be combined with each other as long as they do not make a conflict with each other.
Summary of related art physiological and behavioral signals used in the depression trend assessment method are constructed by machine learning techniques, including two technical routes:
one is classification data obtained in a natural state, including data based on resting brain waves or resting functional magnetic resonance, etc., and depression trend assessment using multimodal data such as expression/speech/language in a natural state obtained by interviews with a virtual agent.
The acquisition difficulty of resting brain wave data, especially multi-lead brain waves, is high. The acquisition of resting state functional magnetic resonance data is costly and is generally suitable for identification of major depressive patients.
The information such as expression/voice/language obtained in the natural state is not data directly related to emotion, and the depression mood of a patient suffering from depression cannot be accurately represented, so that the identification rate of the depression is not high.
The other is depression recognition using multimodal data based on emotional attention bias/galvanic skin/heart rate/memory/voice/expression/brain waves/eye movement information and the like obtained under emotional stimulus. Because the obtained single-mode data can only represent one side face of the depression mood of the patient, two or more modes of data are often used for identifying depression, and the multiple modes of data are long in acquisition time, and the data acquisition of each mode needs a corresponding acquisition device, so that the whole acquisition system is complex in structure, long in acquisition time and low in efficiency.
In addition, the acquisition of data such as brain waves, functional magnetic resonance, skin electricity, eye movement, expression, voice and the like has higher requirements on acquisition devices or equipment, and the data acquisition process has constraint on a tested person, so that the tested person needs to keep a certain state between the tested person and the equipment and cannot change, for example, head movement is not allowed, otherwise, larger noise is generated, and even the data cannot be used and discarded.
Fig. 1 is a flowchart of a depression tendency assessment method according to an embodiment of the present disclosure. As shown in fig. 1, an embodiment of the present disclosure provides a depression tendency evaluation method, including the steps of:
s101: and obtaining the response time of the testee for making emotion decisions.
S102: and extracting the characteristics of the reaction time to obtain original characteristic parameters. The raw characteristic parameters may optionally include at least one of the following: minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the reaction time.
S103: and performing dimension reduction processing on the original characteristic parameters to obtain evaluation characteristic parameters.
S104: inputting the evaluation characteristic parameters into a pre-trained overrun learning machine model to obtain depression trend indexes.
The depression tendency evaluation method provided by the embodiment of the disclosure can increase the attention degree to emotion and response speed by performing depression tendency evaluation through the response time of the testee to make emotion decisions. Compared with the manual evaluation in the related art, the method reduces the influence of subjective factors, reduces the noise of environment and equipment compared with the evaluation by physiological signals in the related art, and is beneficial to improving the accuracy of depression tendency evaluation.
In some embodiments, the raw characteristic parameters include: minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the reaction time. According to the embodiment of the disclosure, the characteristics of the reaction time can be comprehensively extracted through the original characteristic parameters, so that the accuracy of subsequent evaluation is improved.
The reaction time comprises a first reaction time, a second reaction time, a third reaction time and a fourth reaction time, and the reaction time for the testee to make emotion decision comprises the following steps:
acquiring the response time of a tested person under calm emotion for making an emotion decision under conflicting emotion stimulation as a first response time; acquiring the response time of a tested person under calm emotion for making an emotion decision under non-conflict emotion stimulation as second response time; acquiring the reaction time of the testee under the conflicted emotion for making an emotion decision under the non-conflicted emotion stimulus as a third reaction time; and acquiring the response time of the testee under the non-conflicted emotion for making an emotion decision under the conflicted emotion stimulus as a fourth response and a driver.
The conflicting emotional stimuli and the non-conflicting emotional stimuli may be optionally implemented by showing a picture, video, or virtual reality scene to the subject. Conflicting emotions may be selected as happy and sad emotions. The non-conflicting emotion may be selected as a happy emotion or a sad emotion.
The original characteristic parameters are 36, including the minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the first reaction time, the minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the second reaction time, the minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the third reaction time, and the minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the fourth reaction time.
Performing dimension reduction processing on the original characteristic parameters to obtain estimated characteristic parameters comprises the following steps:
calculating the characteristic values of 36 original characteristic parameters through principal component analysis; sorting the characteristic values from big to small; and acquiring original characteristic parameters corresponding to the first N characteristic values as evaluation characteristic parameters. N is an integer greater than or equal to 1. N is optionally 10.
In some embodiments, the pre-trained overrun learning model includes a heavy model and a light model.
Inputting the estimated characteristic parameters into the pre-trained overrun learning model comprises:
obtaining subjective depression tendency scores of the tested person through a self-evaluation scale; when the subjective depression tendency score is larger than a set value, inputting the evaluation characteristic parameters into a serious model; and when the subjective depression tendency score is smaller than the set value, inputting the evaluation characteristic parameters into the mild model.
The depression tendency evaluation method provided by the embodiment of the disclosure evaluates the person with higher depression tendency and the person with lower depression tendency through the respectively trained major model and minor model, thereby being beneficial to further enhancing the accuracy of depression tendency evaluation.
When the method is used, the depression index Di can be calculated according to the depression trend index lambda output by the overrun learning machine model.
Alternatively, when Di > 0.5, the subject is judged to be a depression patient, and when Di < 0.5, the subject is judged not to be a depression patient.
The comparison of the manual diagnosis results of 68 subjects who seek medical attention with the depression tendency evaluation results of the above method is shown in the following table:
and (3) tag: 1 is artificially diagnosed as depression, 2 is artificially diagnosed as non-depression
From the above table, the depression trend evaluating device provided by the embodiment of the disclosure has important reference value for clinical use.
Fig. 2 is a block diagram of a configuration of a depression tendency evaluation apparatus according to an embodiment of the present disclosure. As shown in fig. 2, an embodiment of the present disclosure provides a depression tendency evaluation apparatus including:
an obtaining module 21, configured to obtain a reaction time of the testee making an emotion decision;
a feature extraction module 22, configured to perform feature extraction on the reaction time to obtain an original feature parameter;
the dimension reduction module 23 is configured to perform dimension reduction processing on the original feature parameters to obtain estimated feature parameters;
the data processing module 24 is used for inputting the evaluation characteristic parameters into a pre-trained overrun learning machine model to obtain the depression tendency index.
The raw characteristic parameters include minimum, maximum, upper quartile, median, lower quartile, mean, standard deviation, kurtosis and skewness of the reaction time.
The dimension reduction module comprises:
a principal component analysis unit for calculating a feature value of the original feature parameter by principal component analysis;
the sorting unit is used for sorting the characteristic values from large to small;
and the data processing unit is used for acquiring original characteristic parameters corresponding to the first N characteristic values as evaluation characteristic parameters.
The acquisition module comprises at least one of the following units:
the first acquisition unit is used for acquiring the response time of the testee under calm emotion to make emotion decisions under the conflicted emotion stimulus;
the second acquisition unit is used for acquiring the response time of the testee under calm emotion for making emotion decisions under non-conflicted emotion stimulation;
the third acquisition unit is used for acquiring the response time of the testee under the conflicted emotion for making emotion decision under the non-conflicted emotion stimulation;
and the fourth acquisition unit is used for acquiring the response time of the testee under the non-conflicted emotion for making the emotion decision under the conflicted emotion stimulus.
The pre-trained overrun learning machine model comprises a heavy model and a light model;
the data processing module comprises:
the subjective scoring module is used for acquiring subjective depression tendency scores of the tested person through the self-scoring table;
the first evaluation module is used for inputting evaluation characteristic parameters into the severe model when the subjective depression tendency score is larger than a set value;
and the second evaluation module is used for inputting the evaluation characteristic parameters into the mild model when the subjective depression tendency score is smaller than a set value.
The depression tendency evaluation device provided by the embodiment of the disclosure can increase the attention degree to emotion and response speed by performing depression tendency evaluation through the response time of the testee to make emotion decisions. Compared with the manual evaluation in the related art, the method reduces the influence of subjective factors, reduces the noise of environment and equipment compared with the evaluation by physiological signals in the related art, and is beneficial to improving the accuracy of depression tendency evaluation.
It is to be understood that the above-described embodiments of the present disclosure are merely illustrative or explanatory of the principles of the disclosure and are not restrictive of the disclosure. Accordingly, any modifications, equivalent substitutions, improvements, or the like, which do not depart from the spirit and scope of the present disclosure, are intended to be included within the scope of the present disclosure. Furthermore, the appended claims of this disclosure are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or the equivalents of such scope and boundary.
Claims (2)
1. A depression tendency evaluation device, characterized by comprising:
the acquisition module is used for acquiring the response time of the testee for making emotion decisions;
the feature extraction module is used for extracting features of the reaction time to obtain original feature parameters;
the dimension reduction module is used for carrying out dimension reduction processing on the original characteristic parameters to obtain evaluation characteristic parameters;
the data processing module is used for inputting the evaluation characteristic parameters into a pre-trained overrun learning machine model to obtain a depression tendency index;
wherein the original characteristic parameters comprise the minimum value, the maximum value, the upper quartile, the median, the lower quartile, the mean, the standard deviation, the kurtosis and the skewness of the reaction time;
wherein the acquisition module comprises at least one of the following units:
the first acquisition unit is used for acquiring the response time of the testee under calm emotion to make emotion decisions under the conflicted emotion stimulus;
the second acquisition unit is used for acquiring the response time of the testee under calm emotion for making emotion decisions under non-conflicted emotion stimulation;
the third acquisition unit is used for acquiring the response time of the testee under the conflicted emotion for making emotion decision under the non-conflicted emotion stimulation;
a fourth obtaining unit, configured to obtain a response time of the testee under the non-conflicting emotion to make an emotion decision under the conflicting emotion stimulus;
wherein, the dimension reduction module includes:
the principal component analysis unit is used for calculating the characteristic values of the original characteristic parameters through principal component analysis, wherein the number of the original characteristic parameters is 36;
the sorting unit is used for sorting the characteristic values from big to small;
the data processing unit is used for acquiring original characteristic parameters corresponding to the first N characteristic values as evaluation characteristic parameters, wherein N is a natural number greater than or equal to 1;
when the depression trend index is lambda, the depression index Di is calculated as follows:
alternatively, when Di > 0.5, the subject is judged to be a depression patient, and when Di < 0.5, the subject is judged not to be a depression patient.
2. The depression trend assessment apparatus of claim 1, wherein the pre-trained overrun learning model comprises a heavy model and a light model;
the data processing module comprises:
the subjective scoring module is used for acquiring subjective depression tendency scores of the tested person through the self-scoring table;
the first evaluation module is used for inputting the evaluation characteristic parameters into the severe model when the subjective depression tendency score is larger than a set value;
and the second evaluation module is used for inputting the evaluation characteristic parameters into the mild model when the subjective depression tendency score is smaller than a set value.
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