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In industrialized countries with aging populations, heart failure affects 0.3-2% of the general population. The investigation of 24h-ECG recordings revealed the potential of non linear indices of heart rate variability (HRV)foren hance drisk stratification in patients with ischemic heart failure (IHF). However, long-term analyses are time-consuming, expensive, and delay the initial diagnosis. The objective of this study was to investigate whether 30 min short-term HRV analysis is sufficient for comparable risk stratification in IHF incomparison to 24h-HRV analysis. From 256 IHF patients [221 at low risk (IHFLR) and 35 at high risk (IHFHR)] (a) 24h beat-to-beat time series (b) the first 30 min segment(c) the 30 min most stationary day segment and (d) the 30 min most stationary night segment were investigated. We calculated linear (time and frequency domain)and nonlinear HRV analysis indices. Optimal parameter sets for risk stratification in IHF were determined for 24h and for each 30 min segment by applying discriminant analysis on signifiant clinical and non-clinical indices. Long-and short-term HRV indices from frequency domain and particularly from nonlinear dynamics revealed high univariate significances (p<0.01) discriminating between IHFLR and IHFHR. For multivariate risk stratification, optimal mixed parameter sets consisting of 5 indices (clinical and nonlinear) achieved 80.4% AUC (area under the curve of receiver operating characteristics) from 24h HRV analysis, 84.3% AUC from first 30 min, 82.2% AUC from day time 30 min and 81.7% AUC from nighttime 30 min. The optimal parameter set obtained from the first 30 min showed nearly the same classification power when compared to the optimal 24 h-parameter set. As results from stationary daytime and nighttime, 30 min segments indicate that short-term analyses of 30 min may provide at least a comparable risk stratification power in IHF in comparison to a 24h analysis period. [ABSTRACT FROM AUTHOR]

The article reports on the proposed method at which the risk of sudden cardiac death (SCD) of patients with ischemic-dilated cardiomyopathy can be predicted through analysis of entropy rates. It involves different systems of analysis and steps such as linear and nonlinear heart rate variability (HRV) and a thorough analysis of the factors that determine cardiac responsiveness. It also discusses the noted low heart rate variability in groups of individuals with high risks of SCD.

In congestive heart failure (CHF), dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two highly related pathologies that are not fully characterized. The aim of this study is to assess respiratory sinus arrhythmia (RSA) index of the parasympathetic system, in order to discriminate between both pathologies, DCM and ICM. For this, ECG-signals of 49 subjects (12 DCM patients, 21 ICM patients, 6 ICM patients with diabetes mellitus (DM) type II and 10 control subjects) from the database HERIS II and of 173 subjects (50 DCM, 50 ICM, 15 DCM with DM type II, 15 ICM with DM type II and 47 control subjects) from the database MUSIC2 were analyzed. The RSA was quantified using linear and non-linear analysis methods (fractal dimension and entropy). The results showed a significant difference between ICM and DCM subjects (p=0.013) with a sensitivity of 83% and specificity of 90%. Decreasing RSA values were present in CHF patients, especially in ICM patients, in comparison with healthy subjects. Alterations in the parasympathetic system due to DM were also identified.

Linear dynamic analysis of cardiovascular and respiratory time series was performed in healthy subjects with respect to gender by shifted short-term segments throughout a head-up tilt (HUT) test. Beat-to-beat intervals (BBI), systolic (SYS) and diastolic (DIA) blood pressure and respiratory interval (RESP) time series were acquired in 14 men and 15 women. In time domain (TD), the descending slope of the auto-correlation function (ACF) (BBI&#95;a31cor) was more pronounced in women than in men (p<0.05) during the HUT test and considerably steeper (p<0.01) at the end of orthostatic phase (OP). The index SYS&#95;meanNN was slightly but significantly lower (p<0.05) in women during the complete test, while higher respiratory frequency and variability (RESP&#95;sdNN) were found in women (p<0.05), during 10-20 min after tilt-up. In frequency domain (FD), during baseline (BL), BBI-normalized low frequency (BBI&#95;LFN) and BBI&#95;LF/HF were slightly but significantly lower (p<0.05), while normalized high frequency (BBI&#95;HFN) was significantly higher in women. These differences were highly significant from the first 5 min after tilt-up (p<0.01) and highly significant (p<0.001) during 10-14 min of OP. Findings revealed that men showed instantaneously a pronounced and sustained increase in sympathetic activity to compensate orthostatism. In women, sympathetic activity was just increased slightly with delayed onset without considerably affecting sympatho-vagal balance.

This work has proposed a methodology based on the concept of entropy rates to study the complexity of the short-term heart-rate variability (HRV) for improving risk stratification to predict sudden cardiac death (SCD) of patients with established ischemic-dilated cardiomyopathy (IDC). The short-term HRV was analyzed during daytime and nighttime by means of RR series. An entropy rate was calculated on the RR series, previously transformed to symbol sequences by means of an alphabet. A statistical analysis permitted to stratify high- and low-risk patients of suffering SCD, with a specificity (SP) of 95% and sensitivity (SE) of 83.3%.

Dilated cardiomyopathy (DCM) has an incidence of about 20100 000 new cases per annum and accounts for nearly 10 000 deaths per year in the United States. Approximately 36% of patients with dilated cardiomyopathy (DCM) suffer from cardiac death within five years after diagnosis. Currently applied methods for an early risk prediction in DCM patients are rather insufficient. The objective of this study was to investigate the suitability of short-term nonlinear methods symbolic dynamics (STSD), detrended fluctuation (DFA), and Poincare plot analysis (PPA) for risk stratification in these patients. From 91 DCM patients and 30 healthy subjects (REF), heart rate and blood pressure variability (HRV, BPV), STSD, DFA, and PPA were analyzed. Measures from BPV analysis, DFA, and PPA revealed highly significant differences (p<0.0011) discriminating REF and DCM. For risk stratification in DCM patients, four parameters from BPV analysis, STSD, and PPA revealed significant differences between low and high risk (maximum sensitivity: 90%, specificity: 90%). These results suggest that STSD and PPA are useful nonlinear methods for enhanced risk stratification in DCM patients.

Within 5 years of first diagnosis, nearly 60% of patients with heart failure (HF) suffer from cardiac death. Early diagnosis of HF and reliable risk prediction are still required. Therefore, the objective of this study was to develop a parameter set for enhanced risk stratification in HF patients. In 43 patients suffering from HF (NYHA class > or =II, ejection fraction <45%) and 10 healthy subjects (REF), heart rate and blood pressure variability (HRV and BPV), interactions between heart rate and blood pressure (joint symbolic dynamics, JSD) and blood pressure morphology (BPM) were analysed. BPV, BPM and JSD measures revealed high significance (p<0.0001) in discriminating REF and HF. A set of three parameters from BPV, JSD and BPM was developed for risk stratification (sensitivity 76.5%, specificity 84.2%, area under the receiver operating characteristic curve 81.4%) in patients with HF.

Sudden cardiac death (SCD) is a leading cause of mortality with an incidence of 3 million cases per year worldwide. Therapies for patients who have survived an SCD episode or have a high risk of developing lethal ventricular arrhythmia are well established and depend mainly on risk stratification. In this study we investigated the suitability of the non-linear measure compression entropy (Hc) for improved risk prediction in cardiac patients. We recorded 24-h Holter ECG for 300 patients with congestive heart failure (CHF). During a mean follow-up period of 12 months, 32 patients died due to a cardiac event. Hc depends on the compression parameters window length w and buffer length b, which were optimised by analysing a subgroup of patients. Compression entropies based on the beat-to-beat interval (BBI) were subsequently calculated and compared with standard heartrate variability parameters. Statistical analysis revealed significant differences between high- and low-risk CHF patients in standard HRV measures, as well as compression entropy based on the BBI (cardiac death, p = 0.005; SCD, p = 0.02). In conclusion, the implementation of non-linear compression entropy analysis in multivariate analysis seems to be useful for enhanced risk stratification of cardiac death, especially SCD, in ischaemic cardiomyopathy patients.