TY - GEN
T1 - Enabling efficient stroke prediction by exploring sleep related features
AU - Xie, Jia
AU - Wang, Zhu
AU - Yu, Zhiwen
AU - Guo, Bin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - Stroke is one typical chronic disease, which is caused by the degenerative disorder of the central nervous system and has a serious impact on the daily lives of human beings. Thereby, it is of great value to enable early diagnosis or prediction of stroke by monitoring peoples daily physiological data and designing useful stroke predictors when the symptoms are not apparent. Specifically, in this paper, we propose a novel approach for stroke prediction by exploring sleep related features. In the first step, we present a stroke prediction framework, which integrates common medical features with fine-grained sleep features for stroke risk prediction. In the second step, we propose a stroke risk prediction model, which consists of two key components to control the false negative rate of stroke prediction. We evaluate the framework using a real polysomnogram dataset that contains 66 patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall and AUC are 83.1%, 83.6%, and 0.782, respectively.
AB - Stroke is one typical chronic disease, which is caused by the degenerative disorder of the central nervous system and has a serious impact on the daily lives of human beings. Thereby, it is of great value to enable early diagnosis or prediction of stroke by monitoring peoples daily physiological data and designing useful stroke predictors when the symptoms are not apparent. Specifically, in this paper, we propose a novel approach for stroke prediction by exploring sleep related features. In the first step, we present a stroke prediction framework, which integrates common medical features with fine-grained sleep features for stroke risk prediction. In the second step, we propose a stroke risk prediction model, which consists of two key components to control the false negative rate of stroke prediction. We evaluate the framework using a real polysomnogram dataset that contains 66 patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall and AUC are 83.1%, 83.6%, and 0.782, respectively.
KW - Sleep Cycle
KW - Sleep Stage
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=85060316609&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld.2018.00105
DO - 10.1109/SmartWorld.2018.00105
M3 - 会议稿件
AN - SCOPUS:85060316609
T3 - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
SP - 452
EP - 461
BT - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
A2 - Loulergue, Frederic
A2 - Wang, Guojun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Ma, Xiaoxing
A2 - Li, Peng
A2 - Roveri, Manuel
A2 - Han, Qi
A2 - Chen, Lei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Y2 - 7 October 2018 through 11 October 2018
ER -
