Ischemic Stroke Prediction by Exploring Sleep Related Features

Abstract

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 electroencephalogram-related 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.

Left ventricular hypertrophy detection using electrocardiographic signal

“…2, is a feedforward neural network with multilayer structure and trained by error back propagation algorithm 50 . Recently, BPN still remains one of the popular machine learning methods or an important base for different AI applications, including predicting or classifying many health and medical events [50][51][52][53][54][55] , such as BPN associated models for predicting medical expenses and all-cause risk of 30-day readmission, detecting of COVID-19 disease and ischemic stroke, diagnosing hypertrophic cardiomyopathy and hypertensive heart disease, classifying arrythmia disease (with ECG signals), etc.…”

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

“…Recently, BPN still remains one of the popular machine learning methods or an important base for different AI applications, including predicting or classifying many health and medical events (Ref. [50][51][52][53][54][55]), such as BPN associated models for predicting medical expenses and all-cause risk of 30-day readmission, detecting of COVID-19 disease and ischemic stroke, diagnosing hypertrophic cardiomyopathy and hypertensive heart disease, classifying arrythmia disease (with ECG signals), etc.…”