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Published on: June 5, 2019
Measuring heart rate variability with wavelet thresholds and energy components in healthy subjects and patients with
Jou-Wei Lin1, Juey-Jen Hwang, Liang-Yu Lin
1National Taiwan University Hospital Yun-Lin Branch, Yun-Lin, Taiwan, ROC.
Insights
Wavelet analysis effectively distinguishes congestive heart failure (CHF) patients from healthy individuals by analyzing heart rate variability (HRV). Treatment with beta-blockers improved HRV in CHF patients, though not to healthy levels.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Congestive heart failure (CHF) significantly impacts heart rate variability (HRV).
- Differentiating CHF patients from healthy individuals using HRV requires sensitive analytical methods.
- Assessing the therapeutic effects of medications on HRV is crucial for patient management.
Purpose of the Study:
- To evaluate the efficacy of wavelet analysis in differentiating CHF patients from healthy controls based on HRV.
- To assess the impact of beta-blocker therapy on HRV in CHF patients.
Main Methods:
- Recruited 9 CHF patients and 15 healthy volunteers for 24-hour Holter recordings.
- Calculated wavelet thresholds and energy components from HRV data.
- Administered atenolol to CHF patients for 3 months, followed by repeat Holter recordings and analysis.
Main Results:
- Wavelet thresholds were significantly lower in CHF patients compared to healthy subjects (16.90 vs. 62.40, p < 0.001).
- The energy component of HRV in the finest wavelet domain was significantly lower in CHF patients (40.65 x 10(-6) vs. 243.01 x 10(-6), p = 0.001).
- Beta-blocker treatment improved these HRV parameters in CHF patients, but they did not reach the levels of healthy subjects.
Conclusions:
- Wavelet analysis provides robust and accurate measurements for background HRV.
- Wavelet-derived parameters can effectively differentiate CHF patients from healthy individuals.
- Beta-blocker therapy demonstrates a positive effect on HRV in CHF patients.
Background:
Analysis of wavelet thresholds and energy components can be used to differentiate patients with congestive heart failure (CHF) from healthy subjects and to demonstrate the improvement of heart rate variability after treatment with beta-blockers in the patient group.
Methods:
Nine patients with CHF without concurrent beta-blocker therapy and 15 healthy volunteers were recruited and received 24-hour Holter recording. Wavelet thresholds and energy components were calculated. The patients with CHF were then treated with atenolol for 3 months. Another Holter recording was repeated and associated wavelet parameters were estimated.
Results:
Wavelet thresholds were statistically higher in normal subjects than those derived from patients with CHF (62.40 +/- 20.65 vs. 16.90 +/- 9.72, p < 0.001). The difference was still significant after control of heart rate and the total number of heart beats (74.85 +/- 22.68 vs. 32.51 +/- 18.73, p < 0.001). The energy component of HRV in the finest wavelet domain was also significantly higher in the normal subjects (243.01 +/- 237.69 x 10(-6) vs. 40.65 +/- 39.65 x 10(-6), p = 0.001). These two measures increased after treatment with beta-blockers in the patients with CHF, but not to the extent of those of normal subjects.
Conclusion:
Analysis of wavelet thresholds and energy components has provided robust and accurate measurements for background HRV.
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