Multimode characteristic information fusing and predicting method of depression suicide behavior

Abstract

The invention discloses a multimode characteristic information fusing and predicting method of depression suicide behavior. The multimode characteristic information fusing and predicting method comprises the steps of collecting EEG signals, during data collection, using one-sided mastoid as a reference electrode, using opposite side mastoid as a recording electrode, besides, performing three-dimensional psychology pain measurement on testers, and performing neuropsychology behavioral indicator judgment on an simulated balloon risk task; preprocessing data, detecting EEG signal synchronicity onthe preprocessed EEG data through calculating PLV value between brain electrodes, and performing brain network construction through the PLV value; and by a pattern recognition method, performing classification on depression patient samples having high-risk suicide behavior and depression patient samples having low-risk suicide behavior. According to the multimode characteristic information fusingand predicting method disclosed by the invention, classification accuracy can be effectively increased, prediction of high suicide risk of major depressive disorder is objective, and the occurrence of a phenomenon of the suicide behavior is effectively reduced.

Description

technical field [0001] The invention relates to the medical field, in particular to a multi-mode feature information fusion prediction method for depression suicide behavior. Background technique [0002] Suicide prevention is the focus of global health services. According to statistics from the World Health Organization in 2015, nearly one million people commit suicide worldwide every year; the number of suicides has been increasing in recent years, and suicide has become the second leading cause of death among people aged 15 to 29 Among them, major depressive disorder (Major Depressive Disorder, MDD) is the mental illness most often associated with suicide, and its suicide risk is about 20 times higher than that of the general population. The suicide rate of patients is 2.2-6.2%, so severe Depressive disorder (Major Depressive Disorder, MDD) is the target population of most suicide research. [0003] Predicting and assessing suicide risk is an important and arduous clinic...

AI Technical Summary

Problems solved by technology

However, traditionally, suicidal behaviors are still mainly qualitative and simple subjective evaluation. Traditional prediction techniques are usually based on single-mode feature samples. However, suicidal patients are a complex mental problem, involving behavior, cognition and neural information abnormalities, therefore, traditional detection methods cannot effectively and objectively make accurate predictions on suicidal behavior

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