## 7 Conclusions and Future Work  
A predictive model relies on the association relationship among multi-dimension features, and thus feature extraction plays an important role in improving the ischemic stroke identification performance. In this paper, we not only extract clinical features from clinical history, demographic information, physical and biomedical measurements but also extract features from a polysomnogram to reflect the change pattern of sleep. Experimental results show that the extracted features can characterize sleep patterns more comprehensively, and hence generates more information for classification. However, it does not imply that all the extracted features are useful, as some of them might be redundant and even irrelevant to the classification problem. Therefore, we used a statistical test and information gain to select the extracted features, which include clinical features, sleep structure related features and EEG sleep related features. Finally, we developed an ischemic stroke prediction model that consists of two steps: the first one uses a machine learning model to make the basic prediction, and the second one further optimizes the prediction result by controlling the false negative rate.  
Experimental results proved that the combined characteristics are better than using clinical features alone. At the sleep structured level, the analysis on the result indicates that would-be patients have shorter sleep time and lower sleep efficiency, as well as increasing in wake after sleep onset. In sleep trends, a prolonged lighter sleep stages were found in most experiment groups, and the control group gained more stable sleep-wake stages. It indicates that the patients have significantly reduced sleep efficiency. From the point of view of the electroencephalogram, changes in EEG morphology and frequency correlate with reductions in CBF and stroke symptoms can result from inadequate CBF.  
In addition, more and more data from evidence-based medicine demonstrate a higher risk of stroke in the early morning hours (03:00 a.m. to 06:00 a.m.) [89]. As shown in Table 7, we found that during light sleep of the third sleep cycle (according to data analysis the third sleep cycle is usually between 2:00 a.m. and 4:00 a.m.), there is a significant difference between the controls and the patients. Although there are similar phenomena in other waveforms (including delta, alpha, beta), it is more obvious in the theta band. Experimental results also show that potential ischemic patients tend to have more slow waves in most frequency domain features and nonlinear features, which is just the opposite of the control subjects.  
As future work, we plan to extend this work in two directions. First, feature extraction plays an important role in machine learning; thus, by considering other features (i.e., fractal dimension [90] and Lyapunov exponent [91]) we will try to obtain better prediction performance. Second, deep learning is achieving amazing results in various research areas; therefore, we will explore deep learning models for the construction of more effective stroke prediction models.  
Conceptualization, Z.W.; data curation, J.X.; formal analysis, J.X. and Z.W.; funding acquisition, Z.W., Z.Y. and B.G.; methodology, J.X. and Z.W.; project administration, J.X.; supervision, Z.W., Z.Y., B.G. and X.Z.; writing--original draft, J.X.; writing--review and editing, Z.W., Z.Y. and B.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (No. 2016 YFB1001400), and the National Natural Science Foundation of China (No. 61332005, 61772428), the Innovative Talents Promotion Program of Shaanxi Province (No. 2018 KJXX-011).  
Not applicable.  
The authors declare no conflict of interest.