Recently, nonlinear analysis of R-to-R interval (RRI) in heart rate has brought research attention in medicine to improve predictive accuracy of medication in severely injured patients. It seems conventional vital sig...Recently, nonlinear analysis of R-to-R interval (RRI) in heart rate has brought research attention in medicine to improve predictive accuracy of medication in severely injured patients. It seems conventional vital signs information such as heart rate and blood pressure to identify critically injured patients eventually replaced by heartrate complexity (HRC) analysis to the electrocardiogram (ECG) of patients in trauma centers. In this respect, different nonlinear analysis tools such as;power spectra, entropy, fractal dimension, auto-correlation function and auto-correlation have been adapted for this complexity analysis of ECG signal. Reidbord and Redington [1] were one of the early reports on applications of nonlinear analysis of the heart physiology. Moody and his colleagues could confidently predicted survival in heart failure cases by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics [2]. Further studies were reported in cases of arrhythmia or general anesthesia by Pomfrett [3], Fortrat [4], Lass [5] and references therein. Recently, noteworthy works of Batchinsky and coworkers have shown that prehospital loss of RRI complexity is associated with mortality in trauma patients [6-8]. They have also shown that prediction of trauma survival by analysis of heart rate complexity is even possible by reducing data set size from 800-beat to 200 or lower beat data sets. In this article, we will use different data nonlinear analysis tools such as;power spectrum, entropy, Lyapunov exponent, capacity dimension and correlation function to analyze HRC as a sensitive indictor of physiologic deterioration. In these analyses, we will use real data of 270-beat sections of ECG from 45 emergency patients brought to Shiraz Rejaee Hospetal trauma center prior to any medication. As we can see, using some manipulation on raw data will provide more informative vital signs in our nonlinear analyses.展开更多
文摘Recently, nonlinear analysis of R-to-R interval (RRI) in heart rate has brought research attention in medicine to improve predictive accuracy of medication in severely injured patients. It seems conventional vital signs information such as heart rate and blood pressure to identify critically injured patients eventually replaced by heartrate complexity (HRC) analysis to the electrocardiogram (ECG) of patients in trauma centers. In this respect, different nonlinear analysis tools such as;power spectra, entropy, fractal dimension, auto-correlation function and auto-correlation have been adapted for this complexity analysis of ECG signal. Reidbord and Redington [1] were one of the early reports on applications of nonlinear analysis of the heart physiology. Moody and his colleagues could confidently predicted survival in heart failure cases by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics [2]. Further studies were reported in cases of arrhythmia or general anesthesia by Pomfrett [3], Fortrat [4], Lass [5] and references therein. Recently, noteworthy works of Batchinsky and coworkers have shown that prehospital loss of RRI complexity is associated with mortality in trauma patients [6-8]. They have also shown that prediction of trauma survival by analysis of heart rate complexity is even possible by reducing data set size from 800-beat to 200 or lower beat data sets. In this article, we will use different data nonlinear analysis tools such as;power spectrum, entropy, Lyapunov exponent, capacity dimension and correlation function to analyze HRC as a sensitive indictor of physiologic deterioration. In these analyses, we will use real data of 270-beat sections of ECG from 45 emergency patients brought to Shiraz Rejaee Hospetal trauma center prior to any medication. As we can see, using some manipulation on raw data will provide more informative vital signs in our nonlinear analyses.
