PhD, Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
چکیده
Background: Due the long-time admission of patients in the ICU, it is very expensive. Therefore, solutions, which can increase the quality of care and decrease costs, can be helpful. Objective: Separation of the patients based on the acute conditions can be useful in providing appropriate therapy. In this study, we present a classifier to predict the OSA based on heart rate variability of patients. Material and Methods: In this analytical study, we used the recorded ECG signals from PhysioNet Database. At first, in the preprocessing stage, the noise from the ECG signal was removed, and R spikes were detected to generate the HRV. The next stage was related to linear and non–linear features extraction. We used the paired sample t-test that is a statistical technique to compare two periods (apnea and non-apnea). These features were applied as the inputs of two different classifiers, including MLP and SVM to find the best method and distinguish patients with higher death risk. Results: The results showed that the SVM classifier is more capable to separate the four periods seperated from each other. The sensitivity for detecting the OSA event was 95.46% and the specificity was 97.57% for the non-OSA period. Conclusion: Accurate and timely diagnosis of the disease can ensure the health of the individual, family, and community. Based on the proposed algorithm, the HRV signal and novel feature, presented in this study, had the highest specificity and sensitivity for the detection of the OSA event of the non-OSA, respectively.
Çifçi N, Uyar M, Elbek O, Süyür H, Ekinci E. Impact of CPAP treatment on cardiac biomarkers and pro-BNP in obstructive sleep apnea syndrome. Sleep and Breathing. 2010;14(3):241-4. doi: 10.1007/s11325-009-0306-y. PubMed PMID: 19813037.
Tabata R, Yin M, Nakayama M, Ikeda M, Hata T, et al. A preliminary study on the influence of obstructive sleep apnea upon cumulative parasympathetic system activity. Auris Nasus Larynx. 2008;35(2):242-6. doi: 10.1016/j.anl.2007.11.001.PubMed PMID: 18242028.
Dumont M, Jurysta F, Lanquart JP, Noseda A, Van De Borne P, Linkowski P. Scale-free dynamics of the synchronization between sleep EEG power bands and the high frequency component of heart rate variability in normal men and patients with sleep apnea–hypopnea syndrome. Clinical Neurophysiology. 2007;118(12):2752-64. doi: 10.1016/j.clinph.2007.08.018. PubMed PMID: 17950029.
Penzel T, Kantelhardt JW, Grote L, Peter JH, Bunde A. Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Transactions on Biomedical Eengineering. 2003;50(10):1143-51. doi: 10.1109/TBME.2003.817636. PubMed PMID: 14560767.
Baumert M, Smith J, Catcheside P, McEvoy RD, Abbott D, Sanders P, Nalivaiko E. Variability of QT interval duration in obstructive sleep apnea: an indicator of disease severity. Sleep. 2008;31(7):959-66. PubMed PMID: 18652091. PubMed PMCID: PMC2491512.
Park DH, Shin CJ, Hong SC, Yu J, Ryu SH, et al. Correlation between the severity of obstructive sleep apnea and heart rate variability indices. Journal of Korean Medical Science. 2008;23(2):226-31. doi: 10.3346/jkms.2008.23.2.226. PubMed PMID: 18437004. PubMed PMCID: PMC2526439.
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215-20. doi: 10.1161/01.cir.101.23.e215. PubMed PMID: 10851218.
Electrophysiology TF. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93(5):1043-65. doi: 10.1161/01.CIR.93.5.1043.
Karimi Moridani M, Setarehdan SK, Motie Nasrabadi A, Hajinasrollah E. Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient. Journal of Medical Engineering & Technology. 2016;40(3):87-98. doi: 10.3109/03091902.2016.1139201. PubMed PMID: 27028609.
Zhang YX. Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta. 2007;73(1):68-75. doi: 10.1016/j.talanta.2007.02.030. PubMed PMID: 19071851.
Zanaty EA. Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egyptian Informatics Journal. 2012;13(3):177-83. doi.org/10.1016/j.eij.2012.08.002.
Moridani MK, Setarehdan SK, Nasrabadi AM, Hajinasrollah E. Mortality risk assessment of ICU cardiovascular patients using physiological variables. Universal Journal of Biomedical Engineering. 2013;1:6-9. doi: 10.13189/ujbe.2013.010102.
Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Information Processing & Management. 2009;45(4):427-37. doi: 10.1016/j.ipm.2009.03.002.
