hrvester

hrvester is designed to analyze Heart Rate Variability (HRV) data from .fit files. It focuses on orthostatic tests (measuring HRV while laying down and then standing up) to provide insights into an athlete’s recovery and readiness for training.

The package implements research-based preprocessing methods including:

• Kubios-style artifact detection with time-varying thresholds
• Multiple correction methods: Linear interpolation, cubic spline, and Lipponen-Tarvainen algorithms
• Comprehensive quality assessment following clinical HRV standards
• Automated orthostatic test analysis with phase-specific processing
• Dashboard summary with training recommendations:

What is HRV?

Heart Rate Variability (HRV) is the variation in time intervals between heartbeats. It’s a non-invasive measure that reflects the activity of your autonomic nervous system, and it can be used to assess your body’s readiness for training, recovery status, and overall stress levels.

Features

• Preprocessing Pipeline: Artifact correction methods including research-validated algorithms
  • Kubios-style Detection: Time-varying thresholds with dRR series analysis for artifact identification
  • Multiple Correction Methods: Linear interpolation, cubic spline, and Lipponen-Tarvainen algorithms (Lipponen & Tarvainen, 2019)
  • Quality Assessment: Quality metrics with A/B/C/D/F grading system
• Orthostatic Test Processing: Pipeline from raw .fit files to readiness scores, including:
  • RR Interval Preprocessing: Artifact detection, physiological plausibility checks, and correction methods
  • HRV Metric Calculation: Key HRV metrics like RMSSD and SDNN calculation
  • Resting Heart Rate Estimation: Algorithm to determine resting heart rate
  • Neural Recovery Score: Algorithm for athlete readiness assessment (Jamieson, 2011)
  • Trend Analysis: Moving averages and trend detection for monitoring
• Data Management:
  • Caching: Automatic caching system with quality metrics storage to avoid reprocessing
  • Quality Filtering: Configurable quality thresholds to exclude poor measurements
  • Batch Processing: Process directories of .fit files
• Visualization & Reporting:
  • Dashboards: HRV trend visualization with ggplot2
  • Training Recommendations: Insights based on HRV trends and readiness scores
  • Export Capabilities: Multiple output formats for integration with other tools

Installation

You can install the development version of hrvester from GitHub with:

# install.packages("pak")
pak::pak("sims1253/hrvester")

Quick Start

Here’s a basic example showing how to process a single HRV orthostatic test:

library(hrvester)

# Process a single FIT file with preprocessing
result <- process_fit_file("your_file.fit", 
                          correction_method = "linear",
                          min_quality_threshold = 0.7)

# View the results
result

Preprocessing Methods

The package offers multiple artifact correction methods:

# Read a FIT file
fit_data <- FITfileR::readFitFile("your_file.fit")

# Extract RR intervals with different correction methods
rr_linear <- extract_rr_data(fit_data, correction_method = "linear")
rr_cubic <- extract_rr_data(fit_data, correction_method = "cubic") 
rr_lipponen <- extract_rr_data(fit_data, correction_method = "lipponen")
rr_none <- extract_rr_data(fit_data, correction_method = "none")

# Compare quality metrics
methods <- list("Linear" = rr_linear, "Cubic" = rr_cubic, 
                "Lipponen" = rr_lipponen, "None" = rr_none)

sapply(methods, function(x) attr(x, "quality_metrics")$quality_grade)

Batch Processing

Process multiple files with quality filtering:

# Process an entire directory of FIT files
results <- process_fit_directory("fit_files/", 
                                min_quality_threshold = 0.7,
                                correction_method = "linear")

# View summary statistics
summary(results)

# Show quality distribution
table(results$laying_quality_grade)

Advanced Usage

Custom Quality Assessment

# Extract RR data and assess quality manually
rr_data <- extract_rr_data(fit_data, correction_method = "linear")
quality_metrics <- attr(rr_data, "quality_metrics")

# Check quality details
quality_metrics[c("artifact_percentage", "signal_quality_index", "quality_grade")]

# Check measurement quality
if(quality_metrics$signal_quality_index >= 90 && 
   quality_metrics$artifact_percentage < 5) {
  print("High-quality measurement")
} else {
  print("Consider re-measurement")
}

Readiness Analysis

# Analyze readiness - get current and baseline data
current_metrics <- tail(results, 1)  # Most recent measurement
baseline_metrics <- head(tail(results, 8), 7)  # Previous 7 days

# Analyze readiness status
readiness <- analyze_readiness(current_metrics, baseline_metrics)

# View training recommendations
cat("Training status:", readiness$status)
readiness$recommendations

Research Foundation

The preprocessing pipeline implements methods from the HRV literature:

• Lipponen-Tarvainen: Research-validated algorithm with <2% HRV error (Lipponen & Tarvainen, 2019)
• Linear Interpolation: Standard linear interpolation method for artifact correction
• Cubic Spline: Kubios-compatible cubic spline interpolation method
• Neural Recovery Score: BioForce HRV methodology combining RMSSD, orthostatic response, and heart rate recovery components (Jamieson,

Citation

If using in research, please cite:

@software{hrvester,
  title = {hrvester: Athlete-Centric HRV Analysis Toolkit},
  author = {Maximilian Scholz},
  year = {2024},
  url = {https://github.com/sims1253/hrvester}
}

License

MIT License. See LICENSE for details.