| As a mental disorder,depression shows main clinical symptoms such as significant and lasting black mood,loss of interest,and slow thinking.In serious cases,it will lead to self-mutilation and even suicide.In recent years,with a dramatic increase in the number of people suffering from depression and frequent suicides reported around the world,depression has become gradually well known,and its harm is increasing.According to the report of the World Health Organization from 2017,depression affects about 322 million people worldwide and is the leading cause of disability in today's society.In addition,depression brings a huge economic burden to families and societies,and it is a significant contributor to the global burden of disease.The prevalence and severity of depression are not matched by its relatively backward diagnosis and treatment.As a mental disease,the pathogenesis of depression is still unclear.At present,clinical diagnosis mainly relies on the subjective diagnosis of psychiatrists and qualitative methods such as depression rating scales.The clinical application of quantitative detection and accurate diagnosis is rarely reported.At the same time,it is also faced with difficulty in early depression diagnosis as well as a lack of medical resources.Therefore,it has very important clinical significance and social value to explore the physiological and psychological mechanism of depression.On the basis of a full understanding of the physiological and pathological state,accurate and effective depressive biomarkers are desired to realize the quantitative diagnosis and treatment of depression,which is helpful to improve the diagnostic accuracy and reduce the medical burden caused by depression.Based on the dynamic analysis of multiple physiological signals,this paper explored the status detection of patients with depression.The multi-physiological signal database of patients with depression was constructed firstly,and then the biomarkers that could accurately depict the characteristics of depression were proposed.By exploring the changes of physiological signals in patients with different severity depression,an in-depth understanding of pathogenesis has been speculated.Furthermore,the physiological and psychological model of patients with depression was established,which verified the effectiveness of depression detection research based on multiple physiological signals.The model expands the index library in the field of depression recognition and lays a theoretical foundation for the accurate diagnosis and treatment of clinical depression.The main research achievements and innovations of this paper are as follows:(1)Due to the lack of a public multi-physiological signal database in the field of depression research at present,this paper established a database of depression patients and healthy controls with multi-physiological signals collected simultaneously.Firstly,the EEG acquisition scheme was optimized based on the pathological characteristics of depression,and a three-channel EEG signal acquisition method based on the prefrontal lobe was proposed.Secondly,in order to meet the requirements of multi-physiological signal fusion for depression detection and analysis,an experimental scheme employing the eight-channel physiological signal acquisition system was designed,and six physiological signals including ECG,heart sound,pulse,respiration,EEG,and skin conductance were collected simultaneously.According to the literature,few researchers pay attention to the effect of depression severity on physiological signals.In this paper,patients were divided into three groups according to depression levels,which laid a data basis for the relevant research on the effect of depressive status on human physiological signals.Finally,a database of depression and healthy controls was constructed based on the multi-physiological signals collected synchronously.It will be helpful to promote the rapid development of depression research after opening the standardized database to the public.(2)The effects of different depression severities on the cardiorespiratory coupling state of patients were studied.Compared with traditional methods based on heart rate variability,respiration signal was imported in this paper to extract the nonlinear cardiorespiratory coupling features by employing entropy and cross entropy methods.The differences of features among different depression groups and the correlation between each feature and depression degree were analyzed.On the one hand,the validity of proposed features is proved in the evaluation of cardiorespiratory status changes in depressive patients.On the other hand,the results show that the irregularity of heart rate variability and respiratory variation sequence is increased as well as the synchronization of heart rate and breathing is decreased,which reveals that the imbalance of sympathetic and parasympathetic activity is increasing as the depression deepens.(3)The effects of different depression severities on the prefrontal EEG complexity of patients were explored based on the nonlinear complexity analysis method.Sample entropy and Lemple-Ziv complexity methods were employed to explore the difference of EEG complexity between depressive patients and healthy controls,as well as the difference between different depression levels.The results showed that the complexity of the prefrontal alpha wave in patients with depression was higher than that in the healthy control group,and the increase of prefrontal alpha wave complexity showed a trend of further increasing with the depression deepening The results reveal that the prefrontal unpredictability of patients with depression is increased,suggesting that the randomness of related brain dynamic activities is increased,which explains the decrease in the cardiorespiratory coupling of depressive patients.Meanwhile,the sample entropy and LZC were proved to be useful as quantitative indicators for the accurate diagnosis and treatment of clinical depression.(4)The changes of prefrontal EEG asymmetry in patients with depression were studied based on the nonlinear complexity method.Two new proposed indicators Asy_LZC and Asy_SEn,and the cross entropy algorithm were employed to evaluate the change of prefrontal asymmetry related to the depression severities.It was found that the prefrontal alpha asymmetry in the depressive patients was significantly higher than that in the healthy control group,and this asymmetry showed a trend of further increasing with the deepening of depression.The results reveal that as the degree of depression patients deepens,the right frontal brain activity is relatively increased compared with the left frontal lobe,which causes the imbalance of sympathetic and vagus nerve activity inclining to the sympathetic dominant direction.Therefore,the mechanism of neurophysiological changes in clinical depression is illustrated,and the effectiveness of the two new indicators in the depressive state evaluation is proved at the same time.(5)The proposed physiological indexes of the cardiorespiratory system and each EEG band were tested in depression recognition,and a depressive state detection model based on multiple physiological signal fusion was established which acquired a depression identification accuracy of 92%.The results prove the validity of the database built in this paper and the feasibility of the research methods and lay the theoretical foundation for the realization of depressive status recognition based on multiple physiological signals. |
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