Spectral analysis of heart period variance (HPV)--a tool to stratify risk following myocardial infarction

Heart rate variability analysis based on time-frequency representation and entropies in hypertrophic cardiomyopathy patients

In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time-frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz) and high frequency band (HF, 0.15-0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of the entropy being lower in high risk patients, p-value < 0.05, indicating an increase of predictability. Furthermore, measures from information entropy, but not from TFR, seem to be useful for enhanced risk stratification in HCM patients with an increased risk of sudden cardiac death.

5-HT3 receptor antagonists regulate autonomic cardiac dysfunction in primary fibromyalgia syndrome

Fibromyalgia syndrome (FMS) frequently presents with autonomic and/or functional symptoms. Tropisetron, a selective serotonin-3 antagonist, is widely used for the treatment of this disease. However, its effects on autonomic function are not well known. In the present study, we evaluated whether tropisetron improved cardiac autonomic symptoms in FMS. Thirty-six patients were treated with physiotherapy and 5 mg tropisetron intravenously for 5 days. An additional 36 patients were treated with physiotherapy alone. Thirty-six volunteers served as healthy controls. The ISAX apparatus was used for spectral analyses of cardiac R-R intervals. High frequencies and mid frequencies were analysed to assess sympathetic and parasympathetic activity. The findings were correlated with pain intensity. ISAX findings were significantly different in FMS patients compared to healthy controls and did not correlate with pain perception. Ten of 12 pathological parameters disappeared during treatment in the tropisetron group. Our results indicate that tropisetron reduced not only pain perception but also had a favourable effect on cardiac dysfunction during treatment.

Changes of autonomic information flow due to idiopathic dilated cardiomyopathy

Risk stratification of patients with idiopathic dilated cardiomyopathy (IDC) is an epidemiologically relevant question. But the results based on conventional heart rate variability (HRV) analysis are still unsatisfactory. The adjustments within the cardiovascular system incorporate nonlinear and complex mechanisms of information exchange which may have additional prognostic value. It is an objective of the present work to evaluate the prognostic value of autonomic information flow (AIF) measures in IDC patients compared to conventional HRV measures in a first explorative study. Holter recordings of 32 patients with idiopathic dilated cardiomyopathy (IDC) and 12 normal subjects (NRM) were analyzed. The IDC patients consisted of two groups: 10 high risk (HR) patients, after aborted sudden cardiac death (SCD); 22 low risk (LR) patients, without SCD. Sensitivity, specificity, positive predictive value, negative predictive value and ROC characteristics of a comprehensive set of AIF measures, organized according to the conventional HRV standards, and conventional HRV measures were investigated. The significant risk predictors were evaluated by Spearman's rank correlation. While the only traditional HRV measure discriminating IDC patients from NRM was ln(LF) most of the AIF measures had a discriminatory value. Concerning the prognosis of the IDC patients by conventional HRV we found that SDNN and all frequency band measures (lnHF, lnLF, lnVLF) significantly discriminated HR from LR. Among the AIF measures the time shift related peak decay (PD(dHF)) reflecting the HF band information flow had a prognostic value. PD(dHF) was identified as a promising candidate which might improve the predictive value of traditional HRV analysis, predominantly represented by SDNN. A subsequent comprehensive clinical study is necessary to validate this hypothesis.

Hidden markov models and mutual information analysis to characterize nonlinear dynamics in heart rate variability

A study of nonlinear dynamics of the heart rate variability (HRV) was performed using hidden Markov models (HMM) and Mutual Information (MI). A methodology based on HMM has been developed in the present work. Cardiac RR series were analyzed in the three frequency bands: HF (0.15-0.45Hz), high frequency band; LF (0.04-0.15Hz), low frequency band; VLF (0.003-0.04Hz), very low frequency band. These series (0, observations) were modeled using HMM. The model &#955;=(A,B,&#8719;) was selected so that P(O/&#955;) was locally maximized. Ergodic topology and N=10 states were also considered for this analysis. Different measures based on HMM were defined and obtained from RR time series of 37 Idiopathic Dilated Crdiomyopathy (IDC) patients and 46 healthy subjects (NRM), during awake and sleep stages. Two groups of IDC patients were considered: 11 high risk (HR) patients, after aborted sudden cardiac death (SCD) or who died during the follow up; 26 low risk (LR) patients, without SCD. Some HMM measures showed high percentages (up to 100%) of well classified subjects in all groups.

Evaluation of different rhythms by hidden Markov models in heart rate variability of hypertrophic cardiomyopathy patients

Synchronization and regularity between different rhythms were evaluated in the HRV using hidden Markov models (HMMs) at very low (VLF), low (LF) and high (HF) frequency bands. Phase synchronization of these rhythms was studied in RR series of hypertrophic cardiomyopathy (HCM) patients during the sleeping period. Two groups of patients were considered in the HCM group: high risk (HR), patients after aborted sudden death (SCD) or that died during follow up, and low risk (LR), patients without SCD. RR time-series were filtered in the following frequency-bands, VLF, LF and HF. The RR phase differences of HF vs. VLF, HF vs. LF and LF vs. VLF were calculated and then the amplitude range partitioned into 8 bins. Finally, these series (O, observations) were modeled using HMM. The models lambda = (A,B,pi) were selected such that P(O/lambda) was locally maximized. Ergodic topology and N = {5,10,15,20} states were considered also for this analysis. Ergodic HMMs with 10 states were found to be sufficient to characterize the HRV rhythms of HR and LR patients. Different synchronization strength was observed studying the phase entropies. However, only the parameters obtained from the HMM were able to differentiate the different groups, with p-value < 0.0005.

Time-frequency analysis of the RT and RR variability to stratify hypertrophic cardiomyopathy patients

The RT interval is a measure of the ventricular repolarization and is partially influenced by the sympathovagal balance. The analysis of the variation of the duration of the RT and RR intervals might bring new information about the arrhythmogenic vulnerability and autonomic imbalance. The RR signal and its spectral density (SD) are characterized by two different patterns during the sleep period. On the basis of this information, RT and RR sequences have been automatically classified into two patterns, R and N. In this work, we propose a methodology to define new variables that are able to distinguish patients with hypertrophic cardiomyopathy (HCM) who later developed sudden cardiac death (SCD) from HCM patients without such episode during the follow-up. These variables are based on the instantaneous frequency calculation using time-frequency representation of the RT and RR signals previously classified into R and N patterns. In this study, three spectral bands have been considered: low-frequency band (LF, 0-0.07 Hz), mid-frequency band (MF, 0.07-0.15 Hz), and high-frequency band (HF, 0.15-0.45 Hz). Then a suitable combination of mean energy and mean frequency of the RT and RR signals in the MF and HF bands has allowed HCM patients with SCD to be discriminated from HCM patients without SCD (P < 0.001).