[1]王晨,毋琳,常英娟,等.基于机器学习的睡眠剥夺注意力易损性分类研究[J].中国医学物理学杂志,2022,39(6):713-718.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.010]
　WANG Chen,WU Lin,CHANG Yingjuan,et al.Classification of attention vulnerability to sleep deprivation based on machine learning[J].Chinese Journal of Medical Physics,2022,39(6):713-718.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.010]
点击复制

基于机器学习的睡眠剥夺注意力易损性分类研究()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第6期
页码:
713-718
栏目:
医学影像物理
出版日期:
2022-06-27

文章信息/Info

Title:
Classification of attention vulnerability to sleep deprivation based on machine learning
文章编号:
1005-202X（2022）06-0713-06
作者:
王晨1; 毋琳2; 常英娟1; 朱军强3; 杨庆玲1; 李磊磊1; 孙泽恒4; 赵萌萌4; 方鹏2; 朱元强1
1.空军军医大学第一附属医院放射科， 陕西 西安 710032； 2.空军军医大学军事医学心理学系， 陕西 西安 710032； 3.甘肃省白银市第二人民医院放射科， 甘肃 白银 730914； 4.西安市阎良区人民医院放射科， 陕西 西安 710089
Author(s):
WANG Chen1;  WU Lin2;  CHANG Yingjuan1;  ZHU Junqiang3;  YANG Qingling1;  LI Leilei1;  SUN Zeheng4;  ZHAO Mengmeng4;  FANG Peng2;  ZHU Yuanqiang1
1. Department of Radiology, the First Affiliated Hospital of Air Force Medical University, Xian 710032, China 2. Department of Military Medical Psychology, Air Force Medical University, Xian 710032, China 3. Department of Radiology, the Second Peoples Hospital of Baiyin City, Baiyin 730914, China 4. Department of Radiology, the Peoples Hospital of Xian Yanliang, Xian 710089, China
关键词:
睡眠剥夺; 机器学习; 弥散张量成像; 白质纤维束; 支持向量机
Keywords:
Keywords: sleep deprivation machine learning diffusion tensor imaging white matter fiber tract support vector machine
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2022.06.010
文献标志码:
A
摘要:
目的：旨在寻找可以对睡眠剥夺后注意力易损与耐受个体进行准确区分的白质纤维束。方法：借助弥散张量成像技术获取各向异性分数、轴向扩散系数、径向扩散系数及平均扩散系数等反映白质弥散特性的特征参数，使用支持向量机分类算法构建睡眠剥夺易损性分类模型；采用准确性、敏感性、特异性、阳性预测值和阴性预测值等指标评价分类模型的性能表现；采用置换检验评估分类模型的显著性。结果：与只采用单一类型特征相比，使用组合特征构建的分类器表现性能最佳，其准确性、敏感性、特异性、阳性预测值、阴性预测值及曲线下面积分别为83.67%、80.00%、87.50%、86.96%、80.77%、88.67%。在组合特征构建的分类模型中对分类贡献较大的白质纤维束主要包括放射冠、内囊前肢、丘脑后辐射及皮质脊髓束等投射纤维、上纵束和扣带等联络纤维以及胼胝体和穹窿联合等联合纤维。结论：特定脑区间白质纤维束的微观结构特性可以作为区分睡眠剥夺后注意力易损与耐受个体的影像学标志物。
Abstract:
Abstract: Objective To find white matter fiber tracts that can accurately distinguish between individuals who are vulnerable and resistant to sleep deprivation. Methods The characteristic parameters such as fractional anisotropy, axial diffusivity, radial diffusivity and mean diffusivity which reflect the diffusion characteristics of white matter were obtained using diffusion tensor imaging technology. The support vector machine algorithm was used to construct sleep deprivation vulnerability classification model. Finally, the performance of the classification model was assessed by accuracy, sensitivity, specificity, positive predictive value and negative predictive value and the significance of the classification model was evaluated by permutation test. Results Compared with the classifier constructed with a single type of feature, the combined features-based classifier achieved the best classification performance, with the accuracy, sensitivity, specificity, positive predictive value, negative predictive value and AUC of 83.67%, 80.00%, 87.50%, 86.96%, 80.77% and 88.67%, respectively. In the combined features-based classification model, the most discriminative white matter fiber tracts that contributed to the classification mainly included projection fibers (corona radiata, anterior limb of internal capsule, posterior thalamic radiation and corticospinal tract, etc), association fibers (superior longitudinal fasciculus and cingulum, etc), and commissural fibers (corpus callosum and fornix, etc). Conclusion The microstructure of specific white matter fiber tracts can be used as potential imaging markers to distinguish between individuals vulnerable and resistant to sleep deprivation.

相似文献/References:

[1]王美娇,李莎,岳海振,等. Model Analytics辅助的智能放疗计划建模[J].中国医学物理学杂志,2017,34(9):870.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.002]
　[J].Chinese Journal of Medical Physics,2017,34(6):870.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.002]
[2]周文,王瑜,肖红兵,等. 基于KPCA算法的阿尔茨海默症辅助诊断[J].中国医学物理学杂志,2018,35(4):404.[doi:DOI:10.3969/j.issn.1005-202X.2018.04.007]
　ZHOU Wen,WANG Yu,XIAO Hongbing,et al. Assisted diagnosis of Alzheimer’s disease based on KPCA algorithm[J].Chinese Journal of Medical Physics,2018,35(6):404.[doi:DOI:10.3969/j.issn.1005-202X.2018.04.007]
[3]赵小静,路海月,王梦悦,等. 基于心率变异性的脑力疲劳检测[J].中国医学物理学杂志,2018,35(5):592.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.017]
　ZHAO Xiaojing,LU Haiyue,WANG Mengyue,et al. Mental fatigue detection based on heart rate variability[J].Chinese Journal of Medical Physics,2018,35(6):592.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.017]
[4]余锦娟,林勇. 基于机器学习的骨质疏松性骨折预测研究[J].中国医学物理学杂志,2018,35(11):1329.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.017]
　YU Jinjuan,LIN Yong. Prediction of osteoporotic fractures based on machine learning[J].Chinese Journal of Medical Physics,2018,35(6):1329.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.017]
[5]刘渊,程玉玉,贺睿敏,等.基于机器学习的鼻咽癌转移淋巴结鉴别模型[J].中国医学物理学杂志,2019,36(11):1350.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.020]
　LIU Yuan,CHENG Yuyu,HE Ruimin,et al.Machine learning-based classification model of lymph node metastasis in nasopharyngeal carcinoma[J].Chinese Journal of Medical Physics,2019,36(6):1350.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.020]
[6]孙凯,姚旭峰,马风玲,等.基于机器学习的血细胞分类研究进展[J].中国医学物理学杂志,2020,37(1):127.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.023]
　SUN Kai,YAO Xufeng,et al.Blood cell classification based on machine learning[J].Chinese Journal of Medical Physics,2020,37(6):127.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.023]
[7]雷炳业,潘嘉瑜,吴逢春,等.基于机器学习的神经精神疾病辅助诊断研究进展[J].中国医学物理学杂志,2020,37(2):257.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.022]
　LEI Bingye,PAN Jiayu,et al.Advances in auxiliary diagnosis of neuropsychiatric diseases based on machine learning[J].Chinese Journal of Medical Physics,2020,37(6):257.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.022]
[8]李彩,范炤.基于机器学习的阿尔兹海默症分类预测[J].中国医学物理学杂志,2020,37(3):379.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.023]
　LI Cai,FAN Zhao.Classification and prediction of Alzheimer’s disease based on machine learning[J].Chinese Journal of Medical Physics,2020,37(6):379.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.023]
[9]闫凤,牛振洋,费振乐,等.机器学习在肺癌VMAT计划中对危及器官剂量预测的可行性[J].中国医学物理学杂志,2020,37(7):934.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.025]
　YAN Feng,NIU Zhenyang,FEI Zhenle,et al.Feasibility of machine learning in OAR dosimetric prediction in VMAT plan for lung cancer[J].Chinese Journal of Medical Physics,2020,37(6):934.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.025]
[10]黄仕雄,杨松华,王亮,等.基于预测剂量引导的宫颈癌自动计划研究[J].中国医学物理学杂志,2020,37(9):1101.[doi:10.3969/j.issn.1005-202X.2020.09.004]
　HUANG Shixiong,YANG Songhua,WANG Liang,et al.Automatic planning of radiotherapy for cervical carcinoma based on dose prediction[J].Chinese Journal of Medical Physics,2020,37(6):1101.[doi:10.3969/j.issn.1005-202X.2020.09.004]

备注/Memo

备注/Memo:
【收稿日期】2022-01-10 【基金项目】国家自然科学基金（81801772） 【作者简介】王晨，硕士研究生，住院医师，主要从事功能磁共振成像及神经影像学的临床研究，E-mail: xdrunningboy@gmail.com 【通信作者】朱元强，E-mail: zhu_yq_fmmu@163.com
更新日期/Last Update: 2022-06-27