Department of Neurology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, 637000, Sichuan, China.
Department of Psychology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, 637000, Sichuan, China.
Sleep Med. 2022 Jul;95:126-129. doi: 10.1016/j.sleep.2022.04.024. Epub 2022 Apr 30.
The main classification systems of sleep disorders are based on the subjective self-reported criteria. Objective measures are essential to characterize the nocturnal sleep disturbance, identify daytime impairment, and determine the course of these symptoms. The aim of this study was to establish a resting-state fMRI-based support vector machine (SVM) classifier to diagnose insomnia disorder.
We enrolled 20 patients with insomnia disorder and 21 healthy controls, and obtained their simultaneous polysomnographic electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. The SVM classifiers were trained to capture insomnia. Classifier performance was quantified by a 5-fold cross validation and on independent test dataset.
The fMRI-based SVM classifier was able to diagnose insomnia with an accuracy of 89.3% (sensitivity of 90.9%, specificity of 87.7%). The robustness of SVM classifier was encouraging.
We established an encouraging resting-state fMRI-based SVM classifier to automatically diagnose insomnia disorder. As an objective measure for assessing insomnia disorder, it would be of additional value to the current self-reported subjective criteria.
睡眠障碍的主要分类系统基于主观的自我报告标准。客观的测量对于描述夜间睡眠障碍、识别日间损害以及确定这些症状的病程至关重要。本研究的目的是建立一个基于静息态 fMRI 的支持向量机 (SVM) 分类器来诊断失眠症。
我们纳入了 20 例失眠症患者和 21 例健康对照者，并同时获得了他们的多导睡眠图脑电图和功能磁共振成像 (EEG-fMRI) 记录。SVM 分类器用于捕捉失眠症。通过 5 折交叉验证和独立测试数据集来量化分类器的性能。
基于 fMRI 的 SVM 分类器能够以 89.3%的准确率（敏感性为 90.9%，特异性为 87.7%）诊断失眠症。SVM 分类器的稳健性令人鼓舞。
我们建立了一个令人鼓舞的基于静息态 fMRI 的 SVM 分类器，可自动诊断失眠症。作为评估失眠症的客观测量，它将对当前的自我报告主观标准具有附加价值。
