模糊C均值聚类与HRV特征的发音
Fuzzy C-Means Clustering and Sonification of HRV Features
摘要：
心率变异性（HRV）的线性和非线性测量作为健康的非侵入性指标被广泛研究。压力对心率有着深远的影响，人们发现不同的冥想技巧可以调节心跳节奏。本文旨在探讨从HRV分析中识别适当的声学指标的过程。发声是一种听觉显示，涉及将数据映射到声学参数的过程。这项工作探讨了使用听觉显示来帮助分析无监督机器学习技术所利用的HRV。无监督聚类有助于选择适当的特征，以提高发音的可解释性。采用声音合成发音技术来增加通过声音显示的处理数据的理解和可学习性。这些分析是建立基于声音的实时生物反馈训练系统的早期步骤。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
