A predictive model of death from cerebrovascular diseases in intensive care units  

作　　者：Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah 

机构地区：[1]Department of Biomedical Engineering,Faculty of Health,Tehran Medical Sciences,Islamic Azad University,Tehran,Iran [2]Control and Intelligent Processing Centre of Excellence,School of Electrical and Computer Engineering,College of Engineering,University of Tehran,Tehran,Iran [3]Departments of Biomedical Engineering,Shahed University,Tehran,Iran [4]Loghman Medical Center,Shahid Beheshti University of Medical Sciences,Tehran,Iran

出　　处：《Intelligent Medicine》2023年第4期267-279,共13页智慧医学（英文）

摘　　要：Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical field.Several approaches have been proposed forprediction in this area.Each of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood pressure.Methods The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical Database.The proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU stay.The electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 Hz.The time of admission and time ofdeath are labeled in all data.In this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood pressure.Topredict the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been used.Also,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were used.Paired sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than 0.005.Results The results indicate that the new approach presented in this paper can be compared with other methodsor leads to better results.The best combinatio

关 键 词：Death prediction Cerebrovascular diseases Intensive care unit Heart rate variability Systolic and diastolic blood pressure Return map 

分 类 号：R743[医药卫生—神经病学与精神病学]