  Linear and non-linear measures of heart rate variability (HRV) are widely
investigated as non-invasive indicators of health. Stress has a profound impact
on heart rate, and different meditation techniques have been found to modulate
heartbeat rhythm. This paper aims to explore the process of identifying
appropriate metrices from HRV analysis for sonification. Sonification is a type
of auditory display involving the process of mapping data to acoustic
parameters. This work explores the use of auditory display in aiding the
analysis of HRV leveraged by unsupervised machine learning techniques.
Unsupervised clustering helps select the appropriate features to improve the
sonification interpretability. Vocal synthesis sonification techniques are
employed to increase comprehension and learnability of the processed data
displayed through sound. These analyses are early steps in building a real-time
sound-based biofeedback training system.
