2026-08-12 –, Muschel — N1
Heart rate time signals are one of the most readily available and cost effective biosignals for the study of human behavior. Their availability and ease of use make them ideal for open science. Many programming languages provide libraries that enable the manipulation of the time series generated by measuring the time in milliseconds between each heart beat: the Inter-Beat-Interval (IBI). However advanced functionality is only provided by private software, and many of opensource libraries seem to be designed as simple feature extraction libraries, and many have fallen into disuse. In this work we present the HeartRateLab: a new, powerful, and complete computational framework, written in julia, for the processing, analysis, modeling, and evaluation of human heart IBIs. The Julia language enables the ideal environment for explicit scientific management of operational, data-driven definitions of features used to define heart rate variability, and their physiological statistical relevance in several different domains.
With the use of Julia's rich and interconnected scientific modeling environment, and the open scientific community, the HeartRateLab enables the most complete set of processing functions for IBI timeseries. Complete sets of features can be extracted from entire recordings, or using resampling or rolling window approaches with the overpowered capabilities of the language. Using Julia's DifferentialEquations.jl, Turing.jl, and other scientific libraries, the HeartRateLab provides the tools for modeling complex systems, including data-driven models for parameter recovery.
The HeartRateLab showcases the stable complexity management environment that the Julia language and its community provide for scientific programming, while demonstrating intricate, applicable, but also very simple and embodied principles about the rhythms to which our hearts beat.
As a tool for his Ph.D. in Cognitive Science at the Institute for Human-Computer Interaction, Technical University of Graz, Alberto Barradas has collected many methods and standards that are now integrated and presented in this Julia computing laboratory.
Accessible scientific tooling makes live experimentation accessible, and is presented with a general overview on the study of Heart Rate (HR) and Heart Rate Variability (HRV) and its use in sport and cognitive science. Live visualizations and demonstrations are prepared for this presentation, including suggestions for integrating biofeedback into contemplative practices.
We hope the community finds this contribution valuable, as we have found it for our work in cognitive and sports science, teaching and learning, and personal practice.
Alberto Barradas is a Cognitive Scientist and Behavioral Data Analyst. His work on open and reproducible science is focused on the study of cognitive performance with the use of digital technology. Alberto is a PhD candidate at the Institute of Human-Computer Interaction TUGraz, and a statistician at the Statistical Ambulance of the Medical University of Graz.
Find out more about public work at github.com/abcsds