Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis.
Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis.
复制标题
使用程度中心性对伴有认知障碍的严重阻塞性睡眠呼吸暂停进行分类：机器学习分析
DOI：
10.3389/fneur.2022.1005650
复制
发表时间：
2022
影响因子：
3.4
通讯作者：
Peng, Dechang
中科院分区：
文献类型：
--
作者：
Liu, Xiang;Shu, Yongqiang;Yu, Pengfei;Li, Haijun;Duan, Wenfeng;Wei, Zhipeng;Li, Kunyao;Xie, Wei;Zeng, Yaping;Peng, Dechang
关键词：
在这项研究中，我们旨在使用体素水平的程度中心性(DC)特征结合机器学习方法来区分伴有和不伴有轻度认知损害(MCI)的阻塞性睡眠呼吸暂停(OSA)患者。99例阻塞性睡眠呼吸暂停综合征患者接受了RS-MRI扫描，其中51例为MCI患者，48例为无轻度认知损害的患者。根据自动解剖标记(AAL)脑图谱，计算并提取所有受试者的DC特征。通过剔除具有高度管脚相关性和最小绝对收缩的变量并进行选择性算子套索回归，筛选出10个DC特征。最后，采用三种机器学习方法建立分类模型。支持向量机分类效率最高(AUC=0.78)，其次是随机森林(AUC=0.71)和Logistic回归(AUC=0.77)。这些发现证明了一种有效的机器学习方法来区分患有和不患有MCI的OSA患者，并为OSA引起的认知损害提供了潜在的神经影像证据。
In this study, we aimed to use voxel-level degree centrality (DC) features in combination with machine learning methods to distinguish obstructive sleep apnea (OSA) patients with and without mild cognitive impairment (MCI). Ninety-nine OSA patients were recruited for rs-MRI scanning, including 51 MCI patients and 48 participants with no mild cognitive impairment. Based on the Automated Anatomical Labeling (AAL) brain atlas, the DC features of all participants were calculated and extracted. Ten DC features were screened out by deleting variables with high pin-correlation and minimum absolute contraction and performing selective operator lasso regression. Finally, three machine learning methods were used to establish classification models. The support vector machine method had the best classification efficiency (AUC = 0.78), followed by random forest (AUC = 0.71) and logistic regression (AUC = 0.77). These findings demonstrate an effective machine learning approach for differentiating OSA patients with and without MCI and provide potential neuroimaging evidence for cognitive impairment caused by OSA.
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DOI：
10.4103/jrms.jrms_357_17
发表时间：
2018
期刊：
Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences
影响因子：
--
作者：
Manoochehri Z;Salari N;Rezaei M;Khazaie H;Manoochehri S;Pavah BK
通讯作者：
Pavah BK
DOI：
10.1176/appi.ajp.2009.08121894
发表时间：
2009-08
期刊：
The American journal of psychiatry
影响因子：
--
作者：
Di Martino A;Shehzad Z;Kelly C;Roy AK;Gee DG;Uddin LQ;Gotimer K;Klein DF;Castellanos FX;Milham MP
通讯作者：
Milham MP
影响因子：
3
作者：
Lv, Jian;Shi, Lin;Wang, Defeng
通讯作者：
Wang, Defeng
影响因子：
5.6
作者：
Huang, Wen-Chi;Lee, Pei-Lin;Lai, Feipei
通讯作者：
Lai, Feipei
影响因子：
3.3
作者：
Gao, Yujun;Xiong, Zhenying;Li, Dongbin
通讯作者：
Li, Dongbin