Dongmei He1, Dongmei Ren2, Zhiwei Guo3, Binghu Jiang4. 1. Department of Neurology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, 637000, Sichuan, China. 2. Department of Psychology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, 637000, Sichuan, China. 3. Institute of Brain Function, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, 637000, Sichuan, China. 4. Institute of Brain Function, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, 637000, Sichuan, China. Electronic address: fskjbh@126.com.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.