Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

AimsSleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats.MethodsThe study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD.ResultsFeatures extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79).ConclusionThis study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably. This study utilized electrocorticographic (ECoG) power, electromyogram (EMG) amplitude, and corticomuscular coherence (CMC) values to construct sleep-wake scoring and Parkinson's disease (PD) prediction models based on the support vector machine (SVM) algorithm. The models enable precise classification and identification of sleep-wake states and exhibit robust performance across different rats.image