Blood pressure variability as sign of autonomic imbalance in patients with idiopathic dilated cardiomyopathy.

BACKGROUND: The problem of identifying idiopathic dilated cardiomyopathy (IDC) patients who are at risk of sudden death is still unsolved. The presence of autonomic imbalance in patients with IDC might predict sudden death and tachyarrhythmic events. The aim of this study was to analyze the suitability of blood pressure variability (BPV) compared to heart rate variability (HRV) for noninvasive risk stratification in IDC patients. METHODS: Continuous noninvasive blood pressure and high-resolution electrocardiogram were recorded from 91 IDC patients for 30 minutes. During a median follow-up period of 28 months (range: [17-38] months), 14 patients died due to sudden death or necessary resuscitation due to a life-threatening arrhythmia. HRV and BPV analyses were performed in time domain, frequency domain, and nonlinear dynamics. Using the Mann-Whitney U test and Cox regression, we estimated the accuracy of clinical and nonclinical parameters in discriminating high-risk from low-risk patients. RESULTS: Dynamics of blood pressure regulation was significantly changed in high-risk patients, indicating an increased BPV. BPV indexes from nonlinear symbolic dynamics revealed significant univariate (sensitivity: 85.7%; specificity 77.9%; area under receiver-operator characteristics [ROC] curve: 87.8%) differences. In an optimum multivariate set consisting of two clinical indexes (left ventricular end-diastolic diameter, New York Heart Association) and one nonlinear index (symbolic dynamics), highly significant differences between low- and high-risk IDC groups were estimated (sensitivity of 92.9%, specificity of 86.5%, and area under ROC curve of 95.3%). CONCLUSION: Diastolic BPV indexes, especially those from symbolic dynamics, appear to be useful for risk stratification of sudden death in patients with IDC.

Detection of Liver Dysfunction Using a Wearable Electronic Nose System Based on Semiconductor Metal Oxide Sensors.

The purpose of this exploratory study was to determine whether liver dysfunction can be generally classified using a wearable electronic nose based on semiconductor metal oxide (MOx) gas sensors, and whether the extent of this dysfunction can be quantified. MOx gas sensors are attractive because of their simplicity, high sensitivity, low cost, and stability. A total of 30 participants were enrolled, 10 of them being healthy controls, 10 with compensated cirrhosis, and 10 with decompensated cirrhosis. We used three sensor modules with a total of nine different MOx layers to detect reducible, easily oxidizable, and highly oxidizable gases. The complex data analysis in the time and non-linear dynamics domains is based on the extraction of 10 features from the sensor time series of the extracted breathing gas measurement cycles. The sensitivity, specificity, and accuracy for distinguishing compensated and decompensated cirrhosis patients from healthy controls was 1.00. Patients with compensated and decompensated cirrhosis could be separated with a sensitivity of 0.90 (correctly classified decompensated cirrhosis), a specificity of 1.00 (correctly classified compensated cirrhosis), and an accuracy of 0.95. Our wearable, non-invasive system provides a promising tool to detect liver dysfunctions on a functional basis. Therefore, it could provide valuable support in preoperative examinations or for initial diagnosis by the general practitioner, as it provides non-invasive, rapid, and cost-effective analysis results.

Quantification of Cardiovascular Regulation Applying Heart Rate Variability Analyses for Different Warm and Moist Chest Compresses in Healthy Subjects.

Background: In integrative medicine, complementary healing methods, such as external applications (massages, rhythmic rubs, and compresses), are part of the practice and clinical application and have proven their therapeutic effect in various fields. Objective: Aim of this exploratory, controlled, single-blinded study was to investigate the effects of three different warming chest compresses on cardiovascular regulation by analyzing heart rate variability (HRV) in healthy subjects. Methods: Over a period of 4 weeks, three different warming chest compresses (a hot water compress, a ginger powder compress, and a mustard flour compress) in 30 healthy subjects were analyzed. For all subjects, 48-h long-term electrocardiograms were recorded, and afterward, epochs of 5 min length extracted and analyzed by different linear and nonlinear HRV indices. Results: A moist chest compress did not result in any significant short- and long-term stimulation of the autonomic regulation, except for a short-term significant decrease in heart rate (meanNN, p < 0.05). Warm and moist chest compresses with ginger flour led to significantly increased HRV (sdNN, p < 0.05; symbolic dynamics2, p < 0.05) and its complexity (renyi4 entropy, p < 0.05) and a significant decrease in heart rate (meanNN, p < 0.00036), and thus to a short-term relaxation effect. In contrast, warm and moist chest compresses with mustard flour led to significantly decreased HRV and their complexity (time-, frequency-, and nonlinear dynamics domain, p < 0.00036), which can be interpreted as a stress reaction of the autonomous nervous system. Conclusions: The application of chest compresses led to short-term relaxation effects (ginger) as well as short-term stress effects (mustard) but not to any significant longer-term effect on HRV in healthy subjects.

Yoga in school sports improves functioning of autonomic nervous system in young adults: A non-randomized controlled pilot study.

BACKGROUND: Yoga in school is a beneficial tool to promote the good health and well-being of students by changing the way they react to stress. The positive effects of yoga-taught in schools-on children, youth and young adults have been demonstrated in former studies using mostly subjective psychometric data. AIM: The present trial aims to evaluate the potential effects of yoga on autonomic regulation in young adults by analyzing heart rate variability (HRV). METHODS: This study is a non-randomized, explorative, two-arm-pilot study with an active control group. Fourteen healthy young adults took part in a 10-week yoga program (90 min once a week) in school and were compared to a control group of 11 students who participated in conventional school sports (90 min once a week over 10 weeks). 24-hour electrocardiograms (ECGs) were recorded at baseline and following the 10-week intervention. From 20-minute of nocturnal sleep phases, HRV parameters were calculated from linear (time and frequency domain) and nonlinear dynamics (such as symbolic dynamics and Poincaré plot analysis). Analyses of variance (ANOVA) followed by t-tests as post-hoc tests estimating both statistical significance and effect size were used to compare pre-post-intervention for the two groups. RESULTS: The statistical analysis of the interaction effects did not reveal a significant group and time interaction for the individual nocturnal HRV indices. Almost all indices revealed medium and large effects regarding the time main effects. The changes in the HRV indices following the intervention were more dramatic for the yoga group than for the control group which is reflected in predominantly higher significances and stronger effect sizes in the yoga group. CONCLUSION: In this explorative pilot trial, an increase of HRV (more parasympathetic dominance and overall higher HRV) after ten weeks of yoga in school in comparison to regular school sports was demonstrated, showing an improved self-regulation of the autonomic nervous system.

Peripheral arterial disease alters heart rate variability in cardiovascular patients.

BACKGROUND: Autonomic regulation analysis is useful in risk stratification of ventricular tachycardia and sudden cardiac death in chronic heart failure (CHF). Heart rate variability (HRV) reflects the condition of autonomic regulation. For analyzing the autonomic control the whole cardiovascular system has to be considered. Therefore, the aim of our study was to assess the influence of peripheral arterial disease (PAD) on the autonomic regulation. METHODS: In 53 men (age: 67 +/- 11 years) from the cardiovascular unit we compared standard HRV parameters in 27 with (ankle brachial index, ABI < 0.9) and 26 patients without (ABI >0.9) PAD as well as with 12 healthy subjects as reference. High-resolution electrocardiograms were recorded over 30 minutes under resting conditions. Pulse wave velocity as well as ABI was estimated using the vascular screening system VASERA. RESULTS: In cardiac patients with PAD, we found both significant differences in linear and nonlinear HRV parameters. Higher increase of low-than high-frequency components indicated higher elevated sympathetic than vagal activation. Altered autonomic control can be interpreted as a compensatory mechanism for diminished vascular arteriolar vasodilator capacity in PAD. To maintain the arterial blood pressure, an elevated setpoint of sympathovagal balance is required. CONCLUSIONS: Our data indicate PAD alters the HRV in cardiovascular patients. PAD should be considered in the assessment of cardiac autonomic regulation especially in risk stratification.

Respiratory Sinus Arrhythmia Quantified with Linear and Non-Linear Techniques to Classify Dilated and Ischemic Cardiomyopathy.

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.

Temporal Analysis of Cardiovascular and Respiratory Complexity by Multiscale Entropy Based on Symbolic Dynamics.

The effect of an orthostatic stress on cardiovascular and respiratory complexity was investigated to detect impaired autonomic regulation in patients with vasovagal syncope (VVS). A total of 16 female patients and 12 age-matched healthy female subjects were enrolled in a passive 70° head-up tilt test. Also, 12 age-matched healthy male subjects were enrolled to study gender differences. Analysis was performed dynamically using various short-term (5 min) windows shifted by 1 min as well as by 20 min of orthostatic phase (OP) to evaluate local and global complexity. Complexity was determined over multiple time scales by the established method of refined composite multiscale entropy (RCMSE) and by a new proposed method of multiscale entropy based on symbolic dynamics (MSE-SD). Concerning heart rate variability (HRV) during OP, both methods revealed the highest complexity for female controls followed by lower complexity in male controls (p < 0.01) and by the lowest complexity in female patients (p < 0.01). For blood pressure variability (BPV), no gender differences in controls were shown by any method. However, MSE-SD demonstrated highly significantly increased BPV complexity in patients during OP (p < 0.01 on 4 time-scales after 7 min, p < 0.001 on 5 time-scales after 11 min) while RCMSE did not reveal considerable differences (p < 0.05 on 2 time scales after 7 min). Respiratory complexity was further increased in patients primary shown by MSE-SD. Findings indicated impaired autonomic regulation in VVS patients characterized by predominantly increased BPV complexity accompanied with decreased HRV complexity. In addition, results suggested extending the concept of complexity loss with disease.

Dynamics of the cardiovascular autonomic regulation during orthostatic challenge is more relaxed in women.

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_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_meanNN was slightly but significantly lower (p<0.05) in women during the complete test, while higher respiratory frequency and variability (RESP_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_LFN) and BBI_LF/HF were slightly but significantly lower (p<0.05), while normalized high frequency (BBI_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.

Multivariate and multidimensional analysis of cardiovascular oscillations in patients with heart failure.

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.

Compression entropy contributes to risk stratification in patients with cardiomyopathy.

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.

Short-term heart rate variability--influence of gender and age in healthy subjects.

In the recent years, short-term heart rate variability (HRV) describing complex variations of beat-to-beat interval series that are mainly controlled by the autonomic nervous system (ANS) has been increasingly analyzed to assess the ANS activity in different diseases and under various conditions. In contrast to long-term HRV analysis, short-term investigations (<30 min) provide a test result almost immediately. Thus, short-term HRV analysis is suitable for ambulatory care, patient monitoring and all those applications where the result is urgently needed. In a previous study, we could show significant variations of 5-min HRV indices according to age in almost all domains (linear and nonlinear) in 1906 healthy subjects from the KORA S4 cohort. Based on the same group of subjects, general gender-related influences on HRV indices are to be determined in this study. Short-term 5-min HRV indices from linear time and frequency domain and from nonlinear methods (compression entropy, detrended fluctuation analysis, traditional and segmented Poincaré plot analysis, irreversibility analysis, symbolic dynamics, correlation and mutual information analysis) were determined from 782 females and 1124 males. First, we examined the gender differences in two age clusters (25-49 years and 50-74 years). Secondly, we investigated the gender-specific development of HRV indices in five age decade categories, namely for ages 25-34, 35-44, 45-54, 55-64 and 65-74 years. In this study, significant modifications of the indices according to gender could be obtained, especially in the frequency domain and correlation analyses. Furthermore, there were significant modifications according to age in nearly all of the domains. The gender differences disappeared within the last two age decades and the age dependencies disappeared in the last decade. To summarize gender and age influences need to be considered when performing HRV studies even if these influences only partly differ.

Cardiovascular and cardiorespiratory coupling analyses: a review.

Recently, methods have been developed to analyse couplings in dynamic systems. In the field of medical analysis of complex cardiovascular and cardiorespiratory systems, there is growing interest in how insights may be gained into the interaction between regulatory mechanisms in healthy and diseased persons. The couplings within and between these systems can be linear or nonlinear. However, the complex mechanisms involved in cardiovascular and cardiorespiratory regulation very likely interact with each other in a nonlinear way. Recent advances in nonlinear dynamics and information theory have allowed the multivariate study of information transfer between time series. They therefore might be able to provide additional diagnostic and prognostic information in medicine and might, in particular, be able to complement traditional linear coupling analysis techniques. In this review, we describe the approaches (Granger causality, nonlinear prediction, entropy, symbolization, phase synchronization) most commonly applied to detect direct and indirect couplings between time series, especially focusing on nonlinear approaches. We will discuss their capacity to quantify direct and indirect couplings and the direction (driver-response relationship) of the considered interaction between different biological time series. We also give their basic theoretical background, their basic requirements for application, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory coupling analyses.

Short-term vs. long-term heart rate variability in ischemic cardiomyopathy risk stratification.

In industrialized countries with aging populations, heart failure affects 0.3-2% of the general population. The investigation of 24 h-ECG recordings revealed the potential of nonlinear indices of heart rate variability (HRV) for enhanced risk 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 in comparison to 24 h-HRV analysis. From 256 IHF patients [221 at low risk (IHFLR) and 35 at high risk (IHFHR)] (a) 24 h 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 24 h and for each 30 min segment by applying discriminant analysis on significant 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 24 h HRV analysis, 84.3% AUC from first 30 min, 82.2 % AUC from daytime 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 24 h analysis period.

Ischemic risk stratification by means of multivariate analysis of the heart rate variability.

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures.

Lagged segmented Poincaré plot analysis for risk stratification in patients with dilated cardiomyopathy.

The objectives of this study were to introduce a new type of heart-rate variability analysis improving risk stratification in patients with idiopathic dilated cardiomyopathy (DCM) and to provide additional information about impaired heart beat generation in these patients. Beat-to-beat intervals (BBI) of 30-min ECGs recorded from 91 DCM patients and 21 healthy subjects were analyzed applying the lagged segmented Poincaré plot analysis (LSPPA) method. LSPPA includes the Poincaré plot reconstruction with lags of 1-100, rotating the cloud of points, its normalized segmentation adapted to their standard deviations, and finally, a frequency-dependent clustering. The lags were combined into eight different clusters representing specific frequency bands within 0.012-1.153 Hz. Statistical differences between low- and high-risk DCM could be found within the clusters II-VIII (e.g., cluster IV: 0.033-0.038 Hz; p = 0.0002; sensitivity = 85.7 %; specificity = 71.4 %). The multivariate statistics led to a sensitivity of 92.9 %, specificity of 85.7 % and an area under the curve of 92.1 % discriminating these patient groups. We introduced the LSPPA method to investigate time correlations in BBI time series. We found that LSPPA contributes considerably to risk stratification in DCM and yields the highest discriminant power in the low and very low-frequency bands.

Multivariate short-term heart rate variability: a pre-diagnostic tool for screening heart disease.

This study has aimed to develop a novel pre-diagnostic tool for primary care screening of heart disease based on multivariate short-term heart rate variability (HRV) analyzed by linear (time and frequency domain) and nonlinear methods (compression entropy (CE), detrended fluctuation analysis (DFA), Poincaré plot analysis, symbolic dynamics) applied to 5-min ECG segments. Firstly, we applied HRV analysis to separate healthy subjects (REF) from heart disease patients (PAT). Then to optimize the results, we subdivided both groups according to gender: REF (♂ = 78, ♀ = 53) versus PAT (♂ = 378, ♀ = 115). Finally, we divided REF and PAT into two age subgroups (30-50 years vs. 51-70 years of age) to consider the influence of age on HRV. Heart disease patients were classified using a scoring system based on cut-off values calculated from all HRV indices obtained from the REF. After combining the optimum indices from all different analyzing methods, sensitivities of more than 72% and a specificity of 100% in all subgroups were revealed. Nonlinear indices proved to be better for discriminating heart disease patients from healthy subjects. Multivariate short-term HRV, analyzed by both linear and nonlinear methods appears to be a suitable pre-diagnostic tool for screening heart disease in primary care settings.

Patients on weaning trials classified with support vector machines.

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h (GSucc); 39 patients who failed the weaning trial (GFail) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (GRein). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients (GSucc, GFail and GRein) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications.

Methods derived from nonlinear dynamics for analysing heart rate variability.

Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time- and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

Complexity of the short-term heart-rate variability.

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%.

Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincare plot analysis in risk stratification in patients with dilated cardiomyopathy.

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.