## 5 Stroke Prediction Model
### Stroke Prediction Modelling  
Based on the clinical features and the sleep-related features, four different classifiers are employed to construct the stroke prediction model, including a Support Vector Machine  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(SS\) & The time series of sleep data \\ \(s_{i}\) & The sleep cycle, \(i\in\{1,n\}\) \\ \(E_{i}\) & The four different frequency bands of EEG, \(i\in\{Delat(D),Theta(T),Alpha(A),Beta(B)\}\) \\ \(C_{x}\) & The channel of EEG, \(x\in\{3,4\}\) \\ \(s_{j}\) & The \(j\)-th sleep stage, \(j\in\{0,1,2,3,5\}\) \\ \(cyx_{i}\) & The \(k\)-th sleep cycle, \(k\in\{1,2,3,4,5\}\) \\ \(E\) & The energy of frequency band \\ \(RP\) & The relative power \\ \(BSI\) & The brain symmetry index \\ \(\text{SampEn}\) & The sample entropy \\ DFA & The detrended fluctuation analysis \\ LZC & The Lempel–Ziv complexity \\ TST & Total Sleep Time, the interval between sleep onset and the end of sleep \\ SE & Sleep Efficiency, computed as the ratio between the hours of actual sleep and total time in bed \\ WASO & Wake After Sleep Onset, the proportion of awake stages during the whole sleep time \\ AT & Awake Times, the number of awakenings during the whole sleep time \\ SL & Sleep Latency, the time interval between going to bed and falling asleep \\ FF Trend 1 & The sleep time of the last cycle is u minutes longer than that of the first cycle, we defined it as an upward trend, it is otherwise defined as a downward trend \\ TF Trend 1 & The third cycle’s deep sleep time (i.e., \(s3\) and \(s4\) stages) is v minutes longer than that of the first \\ cycle, we defined it as an upward trend, it is otherwise defined as a downward trend \\ SF Trend 1 & The second cycle’s deep sleep time is \(w\) minutes longer than that of the first cycle, we defined it as an upward trend, it is otherwise defined as a downward trend \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of main notations defined in this paper.  
Figure 4: A general overview of the proposed two-step stroke prediction model.  
(SVM), Random Forests (RF), a Back Propagation Neural Network (BPNN), Naive Bayesian (NB) and Logistic Regression (LR). Particularly, we utilize the 10-fold cross-validation technique to obtain more robust classification results [77].  
SVM is one of the most popular classification algorithms and has been widely adopted to solve problems like regression and classification [78]. In this study, predicting stroke is a typical binary classification problem. SVM projects the input instances into a high-dimensional feature space, and then discriminates patients and healthy subjects by creating a hyperplane.  
RF is a typical ensemble learning model for classification and regression tasks. It first constructs a set of decision trees at the training stage and then outputs a classification result that is either the mode of the classes or the mean prediction results of each individual tree [79].  
BPNN is a common method for clustering or classifying, which is especially suitable for nonlinear systems. A typical BPNN consists of several layers of neurons and is characterized by network interconnection geometry, node characteristics, and the transfer functions [80].  
NB is a [PERSON] theorem-based probability classifier, which considers that each feature contributes independently to the probability for any possible correlations [1].  
LR is a statistical analysis technique used to predict a binary outcome by evaluating the relationship between a set of independent predictor variables [81].