## 1 Introduction  
Ischemic stroke is a medical condition, which occurs once the arteries to the brain become blocked, resulting in cell death. [1]. Each year, in the United States, approximately 795,000 people suffer from a stroke, and about 600,000 of these are first attacks, 185,000 are recurrent attacks [2]. In medicine, stroke has two subspecies: ischemic stroke and hemorrhagic stroke. Compared with hemorrhage, ischemic stroke accounts for about 87% of all strokes [3]. It occurs when a blood vessel is blocked by a blood clot which means the blood cannot reach the brain. Therefore, the cerebral blood flow (CBF) of patients suffering from the ischemic stroke is lower than those of health [4]. Obviously, ischemic stroke damages brain cells and hence leads to the degeneration of the motor function accompanied with depression and anxiety [5]. If ischemic stroke can be predicted in a timely fashion, the quality of life and the health level of the public can be improved significantly. To predict stroke accurately, two key problems should be solved. First, when there are no apparent symptoms, can we extract effective ischemic stroke predictors to diagnose or predict stroke? Second, for ease of use, can we extract efficient stroke predictors from data that can be obtained during people's daily lives in a non-intrusive manner? By now, researchers have conducted numerous studies to extract useful ischemic stroke predictors, which can be roughly grouped into three categories. The first category of studies focuses on extracting features manually based on expert medical knowledge. For example, [PERSON] et al. [6] and [PERSON] et al. [7] constructed a stroke prediction model by manually selecting 16 features. Although the effectiveness of some clinical features [8; 9; 10]had been verified by existing studies, how to extract efficient features from continuous physiological signals is still an open issue. The second category of studies aims to extract features from Electrocardiograph (ECG), Computerized Tomography Scanner (CT-Scan), or Magnetic Resonance Imaging (MRI) investigation. Particularly, based on ECG, CT-Scan and MRI, the detailed structures of soft tissues, such as brain and heart, can be observed more clearly. For example, [PERSON] et al. [11] and other authors [12; 13; 14] verified the usefulness of the CT-Scan and MRI data of the brain in early detection of acute ischemic stroke. While existing approaches have achieved a satisfactory stoke detection performance, they can only detect stroke that will occur soon or has already occurred. To enable early prediction of stroke occurrence, we need more efficient approaches. The third category of studies extracts features utilizing machine learning techniques, which were widely used to investigate the relationship between the symptoms and stroke incidents. For instance, [PERSON] et al. [15; 16] and [PERSON] et al. [17] proposed an integrated machine learning method to predict stroke based on different environmental factors. All these studies mainly focused on detecting stroke incidents by mining features from discrete data, rather than leveraging continuous data that contain much more useful information. In summary, existing stroke prediction methods have not fully solved the problem of early stroke prediction, and the main shortcoming is that they did not fully exploit unobtrusive sensing techniques to collect long-term health-related time series data during daily life (such as continuous health data that can be collected during sleep), which may leave out useful hidden information when predicting stroke. Specifically, in this paper, we aim to address the early prediction problem of ischemic stroke by exploring sleep-related time series data. On one hand, ischemic stroke occurs most often shortly after awakening in the morning hours [18], and a number of studies have reported a sleep circadian variation in the occurrence of stroke [19; 20].