Data transforms for spectral analyses of heart rate variability.

Area of Science:

• Physiology
• Biostatistics
• Cardiology

Background:

• High-frequency heart rate variability (HF-HRV) spectral power values are often exponentially distributed.
• This distribution complicates standard parametric statistical analyses.
• Existing transformations like natural log (ln) and reactivity transforms have limitations.

Purpose of the Study:

• To evaluate three data transformation methods for raw HF-HRV spectral power.
• To introduce and assess a novel "percent deviation from the mean" transform.
• To determine which transform best normalizes HF-HRV data for statistical analysis.

Main Methods:

• Comparison of raw HF-HRV data with ln transform, reactivity transform, and the novel percent deviation transform.
• Quantification of effect size by measuring the overlap between standard error margins of transformed data.
• Assessment of data distribution normality and tightness of distribution.

Main Results:

• Raw HF-HRV data showed 19.2% overlap.
• The ln transform reduced overlap to 3.7%.
• The reactivity transform resulted in -57.1% overlap.
• The percent deviation transform achieved -70.2% overlap, indicating the strongest effect size.
• The percent deviation transform yielded more normally and tightly distributed data compared to other methods.

Conclusions:

• The "percent deviation from the mean" transform is a highly effective method for normalizing HF-HRV spectral power data.
• This novel transform offers superior data distribution properties compared to existing ln and reactivity transforms.
• Investigators analyzing HF-HRV data should consider using the percent deviation transform for more robust statistical outcomes.