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| 1 | Support vector ma-chine-based arrhythmia classification using reduced features ofheart rate variability signal 显示文摘 | Asl BM Setarehdan SK Mohebbi M | 2008 | Artif Intell Med2008，44，1： | 1 |
| 2 | Support vector machine - based arrhythmia classification using reduced features of heart rate variability signal显示文摘 | B M Asl S K Setarehdan M Mohebbi | 2008 | Artificial intelligence in medicine2008，44，1： | 1 |
| 3 | Evoked hemodynamic response estimation using ensemble empirical mode decomposition based adaptive algorithm applied to dual channel functional near infrared spectroscopy显示文摘 | BERIVANLOU N H SETAREHDAN S K NOUBARI H A | 2014 | Journal of Neuroscience Methods2014，224，6： | 1 |
| 4 | Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals 显示文摘 | Ebrahimi F Setarehdan S K Ayala-Moyeda J | 2013 | ComputerMethods and Programs in Biomedicine2013，112，1： | 1 |
| 5 | Automatic Cardiac LV Boundary Detection and Tracking Using Hybrid Fuzzy Temporal and Fuzzy Multiscale Edge Detection显示文摘 | KAMALEDIN SETAREHDAN S SORSGHAN JOHN J | 1999 | IEEE Trans on Biomedical Engineering1999，46，11： | 1 |
| 6 | Automatic sleep staging using empirical mode decompo- sition, discrete wavelet transform, time-domain, and nonlin- ear dynamics features of heart rate variability signals 显示文摘 | EBRAHIMI F SETAREHDAN S K AYALA-MOYEDA J | 2013 | Comput Methods Programs Biomed2013，112，1： | 1 |
| 7 | Evoked hemodynamic response estimation using ensemble empirical mode decomposition based adaptive algorithm applied to dual channel functional near infrared spectroscopy (fNIRS) 显示文摘 | Berivanlou N H Setarehdan S K Noubari H A | 2014 | Journal of Neuroscience Methods2014，224，1： | 1 |
| 8 | Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection显示文摘 | Setarehdan S K Soraghan J J | | 0，，11： | 1 |
| 9 | Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection显示文摘 | Soraghan JJ | 1999 | IEEE Trans On Biom Eng1999，46，11： | 1 |
| 10 | Automatic left ventricular centre point extraction in echocardiographic images显示文摘 | Soraghan JJ | 1997 | Signal Processing J EURASIP1997，61，3： | 1 |
| 11 | A predictive model of death from cerebrovascular diseases in intensive care units显示文摘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(��),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 combination of features based on GA to achieve maximum predictive accuracywas m(mean),L_(Mean),A,SBP_(SVMax),DBP_(Max-Min).The accuracy,specificity,and sensitivity based on the best featuresobtained from GA were 97.7%,98.9%,and 95.4%for cerebral ischemia disease with a prediction horizon of0.5–1 hour before death.The d-factor for the best feature combination based on the GA model is less than 1(d-factor=0.95).Also,the bracketed by 95 percent prediction uncertainty(95PPU)(%)was obtained at 98.6.Conclusion The combination of HRV and blood pressure signals might increase the accuracy of the predictionof the death episode and reduce the minimum hospitalization time of the patient with cerebrovascular diseasesto determine the future status. | Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah | 2023 | Intelligent Medicine2023，3，4： | 0 |
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