TY - JOUR
T1 - Ischemic stroke prediction by exploring sleep related features
AU - Xie, Jia
AU - Wang, Zhu
AU - Yu, Zhiwen
AU - Guo, Bin
AU - Zhou, Xingshe
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogramrelated features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.
AB - Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogramrelated features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.
KW - Ischemic stroke
KW - Sleep cycle
KW - Sleep EEG
KW - Sleep stage
UR - http://www.scopus.com/inward/record.url?scp=85136793706&partnerID=8YFLogxK
U2 - 10.3390/app11052083
DO - 10.3390/app11052083
M3 - 文章
AN - SCOPUS:85136793706
SN - 2076-3417
VL - 11
SP - 1
EP - 25
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 5
M1 - 2083
ER -