基于睡眠脑电信号的抑郁症诊断算法

Depression Diagnosis Algorithm Based on Sleep EEG Signals

抑郁症诊断是医学领域的重要研究方向.而现有的抑郁症诊断方法存在高成本、低效率、准确率不高以及解释性不强等问题,为解决该问题,本文结合睡眠分期技术,提出一种基于睡眠脑电信号的抑郁症自动诊断算法.该方法首先通过卷积神经网络与双向长短时记忆神经网络结合,能够提取睡眠信号的高级特征,同时结合不同睡眠时期的关联性进行分析,提升了睡眠分期的准确率与可解释性.实验结果表明,这种方法在Sleep-EDF公开数据集中准确率最高能够达到95.82%,超越了大多数现有方法.随后,基于睡眠分期的结果,结合卷积神经网络提出了DepNet2D (depression net 2 dimension)模型,对REM期的脑电数据进行特征提取并分类.该模型能够有效地学习睡眠脑电的时空依赖关系,捕捉抑郁症患者大脑活动的特征模式,提高了识别患者频谱特征的准确率.实验结果表明,在抑郁症诊断任务中,本文提出的抑郁症筛查方法准确率达到了88.82%,与传统抑郁症诊断模型相比,具有更高的准确率.该方法增强了抑郁症诊断的可解释性,对现代抑郁症研究等分析研究具有一定的实用价值,为精神健康领域的研究和临床实践提供了新的思路和方法.

The diagnosis of depression is an important research direction in the medical field.However,existing methods for diagnosing depression face problems such as high cost,low efficiency,low accuracy,and weak interpretability.To solve these problems,this study proposes an automatic algorithm for depression diagnosis based on sleep EEG signals,combined with sleep staging.This method first combines convolutional neural networks with bidirectional long short-term memory neural networks to extract advanced features of sleep signals.At the same time,it analyzes the correlation among different sleep stages,improving the accuracy and interpretability of sleep staging.The experimental results show that this method achieves the highest accuracy of 95.82%on the public dataset Sleep-EDF,surpassing most existing methods.Subsequently,based on the results of sleep staging,the compression net 2 dimension(DepNet2D)model combined with convolutional neural networks is proposed to extract features and classify EEG data during the REM phase.This model can effectively learn the spatiotemporal dependencies of sleep EEG,capture the feature patterns of brain activity in patients with depression,and improve the accuracy of identifying the spectral features of patients.The experimental results show that in the diagnosis of depression,the proposed method in this study reaches accuracy of 88.82%,which is higher than that of traditional models.The proposed method enhances the interpretability of depression diagnosis and has practical value for modern depression research and analysis,providing new ideas and methods for research and clinical practice in the field of mental health.