Fuzzy C-Means Clustering and Sonification of HRV Features
Debanjan Borthakur, Victoria Grace, Paul Batchelor, Harishchandra Dubey, Kunal Mankodiya
arXiv Preprint Archive August 19, 2019 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or methodological paper |
|---|---|
| Keywords | Cs.hc Cs.lg Cs.sd Eess.as Stat.ml |
| Key points | Proposes that unsupervised clustering can help select HRV features for sonification and that vocal synthesis sonification can increase comprehension and learnability of the data, as early steps toward a real-time sound-based biofeedback training system. |
Abstract
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.