ABSTRACT
This paper examines the feasibility of the trend covariability between QT and RR Intervals (QTIs and RRIs) be a novel mean of the sudden cardiac death (SCD) risk stratification. Twenty four hour beat to beat QTIs and RRIs are measured from Holter ECG recordings of 25 normal control subjects (SCD-C), 14 low SCD risk patients (SCD-L) with high blood pressure or light cardiac arrhythmia and 11 SCD high risk patients (SCD-H) with heart attack history. The Kalman filtering technique has been applied to decompose 24 hour short term mean QTIs and RRIs sequences into trend components and additive random variations. The correlation coefficients (TC-QT/RR) and cross entropies (TE-QT/RR) between the QT and RR trend signals are estimated. Cross entropy TE-QT/RR achieved the best stratification of subject groups. TE-QT/RR distribution for SCD-C, -L -H subject groups were 1.697 ± 0.058, 1.160 ± 0.099, 0.920 ± 0.067. The differences in entropy values are statistically significant for all classes pairs (SCD-H and -C (p<0.00001); -L and -C (p<0.001); -H and -L (p<0.05) The result indicates that the TE-QT/RR could be a novel index for the SCD risk stratification.
Subject(s)
Death, Sudden, Cardiac/epidemiology , Death, Sudden, Cardiac/prevention & control , Diagnosis, Computer-Assisted/methods , Electrocardiography/methods , Electrocardiography/statistics & numerical data , Heart Failure/diagnosis , Heart Failure/mortality , Comorbidity , Heart Failure/prevention & control , Humans , Incidence , Japan/epidemiology , Prognosis , Reproducibility of Results , Risk Assessment/methods , Sensitivity and Specificity , Survival Analysis , Survival RateABSTRACT
This paper proposes a method to characterize circadian changes in QT intervals for diabetic studies. Although properties of QT intervals for diabetic patients are extensively studied, their circadian changes are not fully understood. Recently, the traditional cosinor method has been utilized for a study examining the relationship between QT circadian changes and the insulin resistance of the diabetic patients. For better characterization of the circadian change in QT intervals of this kind, spline smoothing technique applied to a decimated data set of QT intervals is proposed. New indices named QT circadian transition time (QTCT) and QT circadian transition amplitude (QTCA) associated with the subjects' awakening process are defined to characterize diabetic patients' condition. The method is applied to ten normal and fifteen type 2 diabetic patients. The proposed indices showed significantly lower values for type 2 diabetic patients compared to the control subjects indicating their effectiveness for the characterization.
