RISK-Sleep: Real-Time Stroke Early Detection System during Sleep Using Wristbands

Abstract

Stroke is the fifth leading cause of death in the US. Early Recognition and treatment of stroke are essential for a good clinical outcome. It is particularly challenging for Wake-Up Stroke (WUS) to know the time of stroke onset, hence golden time for treatment is easily missed. We propose a Real-tIme StroKe early detection system during Sleep (RISK-Sleep) using wristbands. RISK-Sleep is a solution for early stroke detection tailored for the sleep environment that is cost-effective and practical for daily use. Underneath RISK-Sleep, we define and utilize an abnormal sleep motion model consisting of abnormal intensity and abnormal frequency. The abnormal intensity indicates hemiparesis sleep motion patterns while the abnormal frequency means emergency situations such as full hemiparesis and full paralysis. Based on the model, we seek the best classifier that analyzes the aforementioned two abnormal motion patterns by sliding window in real-time. For performance evaluation, we collect sleep data from 30 healthy people and 14 stroke patients with hemiparesis. Evaluation results show that RISK-Sleep achieves classification accuracy of 96.00% in abnormal intensity with 146-minute window in the KNN classifier with SFS feature selection. In addition, the SVM classifier without feature selection shows classification accuracy of 100% with 108-minute window in abnormal frequency. We expect RISK-Sleep plays a significant role in reducing the incidence of WUS. © 2018 IEEE.