## 1 Introduction
For example, patients' sleep was found to be markedly altered compared with a normal group, specific performance in a higher amount of slow wave sleep was recorded, whereas fast wave sleep was found to be deeply suppressed (such as stage 0 being increased, however stage 4 being reduced) [21; 22], coupled to present electroencephalograph (EEG) abnormalities such as an increase in delta frequency, sometimes an increase and at other times a decrease in theta activity and/or a decrease in alpha and beta frequencies [23]. [PERSON] et al. [24] had found that participants with rapid eye movement (REM) sleep behavior disorder were approximately 1.5 times more likely to develop stroke, including the ischemic type. In another similar study, it was also proven that sleep disorders were highly prevalent in patients at risk for stroke and may be modifiable risk factors for stroke [22]. [PERSON] et al. [25] certified that the delta/alpha power ratio (DAR) and the (delta + theta)/(alpha + beta) ratio (DTABR) exhibited optimal classifier accuracy between ischemic stroke patients and control groups. On the other hand, EEG offers a continuous, real-time, non-invasive measure of brain function [26] and it is the most sensitive neuro-diagnostic tool for detecting cerebral ischemia and can show changes of ischemic stroke within minutes of onset [27]. EEG abnormalities are closely tied to CBF [27]. When normal CBF of 50 mL/100 g/min declines to 25 mL/100 g/min, EEG will first lose faster frequencies (\(\geq\)8 Hz), and then if CBF continues to drop to 18 mL/100 g/min, which is a crucial ischemic threshold, the slower frequencies (4 Hz to 7 Hz) of EEG will increase. Figure 1 shows the relationship of EEG and ischemic stroke. Therefore, by extracting sleep-related features (including sleep structure and sleep EEG) to predict ischemia, the first key problem can be solved. In particular, these sleep features do not compete with elegant clinical trials, but the sleep information it adds is complementary to other information. In addition, wearable devices (like smart waist-belts), can be utilized to collect daily sleep-related data, which are as accurate as clinical equipment. Furthermore, EEG is a well-known, user-friendly examination and is less complex than the imaging modality [28]. It is usually recorded by using 2 to 19 electrodes, the numbers of which are set according to the needs of the patient, placed upon the scalp according to the International 10-20 System. Since the recruitment of stroke participants is extremely expensive and time-consuming, this study will not collect experimental datasets by usingportable data acquisition devices. Nevertheless, it is notable that we can collect datasets which are similar to the one utilized in this study through portable data acquisition devices, and the proposed method can be directly used for data analysis and stroke prediction. As for the second problem, we will solve it in the the rest of the paper. There has been little work that has explored sleep-related features and further combined them with other clinical features (e.g., high blood pressure, cholesterol [7]) to predict the occurrence of stroke. In our work, we present a novel ischemic stroke prediction approach, which integrates clinical features with fine-grained sleep-related features for stroke risk prediction. Specifically, to fully characterize the sleep pattern of a high-risk population of ischemic stroke, we extracted both sleep structure related and sleep EEG related features. To sum up, the contributions of this study are three-fold:  
First, compared with most of the previous studies, which merely used sleep data from a few minutes to tens of minutes, we had leveraged all night's sleep data to make this prospective study. Specifically, we extracted both sleep structure related and sleep EEG related features to characterize the ischemic stroke pattern more comprehensively.