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Heart rate variability analysis based on time-frequency representation and entropies in hypertrophic cardiomyopathy
F Clariá1, M Vallverdú, R Baranowski
1Department ESAII, Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Spain.
Insights
Nonlinear heart rate variability (HRV) measures, particularly information entropies, show promise for predicting sudden cardiac death risk in hypertrophic cardiomyopathy (HCM) patients. These advanced analyses offer better risk stratification than traditional methods.
Area of Science:
- Cardiology
- Biomedical Engineering
- Autonomic Nervous System Research
Background:
- Hypertrophic cardiomyopathy (HCM) presents a significant risk of sudden cardiac death (SCD) with limited predictability.
- Accurate risk stratification for SCD in HCM patients remains a clinical challenge.
Purpose of the Study:
- To enhance the prognostic value of heart rate variability (HRV) analysis in HCM patients.
- To investigate the utility of linear and nonlinear HRV measures, including time-frequency representation (TFR) and entropy calculations, for assessing autonomic nervous system changes.
- To compare the efficacy of novel HRV analysis techniques against traditional HRV parameters for risk stratification.
Main Methods:
- Analysis of 5-hour Holter recordings from 64 HCM patients (13 high-risk for SCD, 51 low-risk) and 55 healthy controls.
- RR interval signals were filtered into very low (VLF), low (LF), and high frequency (HF) bands.
- Application of time-frequency representation (TFR) variables, Shannon entropy, and Rényi entropy measures.
- Comparison of TFR and entropy-based measures with traditional HRV parameters.
Main Results:
- TFR-based HRV measures demonstrated superior classification of HCM patients versus controls compared to traditional HRV parameters.
- Nonlinear measures, specifically entropies in the HF band, provided highly significant group classification (p < 0.0005) between HCM patients and controls.
- HCM patients exhibited lower entropy values, indicating reduced complexity, compared to controls.
- Entropy measures showed significant differences between high-risk and low-risk HCM patients (p < 0.05), with lower values in high-risk individuals, suggesting increased predictability.
Conclusions:
- Information entropy measures, distinct from TFR, appear valuable for improved risk stratification of sudden cardiac death in HCM patients.
- Nonlinear HRV analysis, particularly entropy calculations, offers enhanced insights into autonomic dysfunction and risk prediction in HCM.
- Advanced HRV analysis techniques hold potential for refining clinical management and preventative strategies in HCM.
Abstract:
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.
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