Ischemic stroke prediction by exploring sleep related features

Jia Xie, Zhu Wang, Zhiwen Yu, Bin Guo, Xingshe Zhou

科研成果: 期刊稿件文章同行评审

5 引用 (Scopus)

摘要

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.

源语言英语
文章编号2083
页(从-至)1-25
页数25
期刊Applied Sciences (Switzerland)
11
5
DOI
出版状态已出版 - 1 3月 2021

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