Fuzzy C-Means Clustering and Sonification of HRV Features
Document Type
Conference Proceeding
Date of Original Version
9-1-2019
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.
Publication Title, e.g., Journal
Proceedings - 4th IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2019
Citation/Publisher Attribution
Borthakur, Debanjan, Victoria Grace, Paul Batchelor, Harishchandra Dubey, and Kunal Mankodiya. "Fuzzy C-Means Clustering and Sonification of HRV Features." Proceedings - 4th IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2019 (2019): 53-57. doi: 10.1109/CHASE48038.2019.00024.