Classification of attention vulnerability to sleep deprivation based on machine learning(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第6期
- Page:
- 713-718
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Classification of attention vulnerability to sleep deprivation based on machine learning
- 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
- Keywords:
- Keywords: sleep deprivation machine learning diffusion tensor imaging white matter fiber tract support vector machine
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.06.010
- 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.
Last Update: 2022-06-27