Ultra-short-term analysis of heart rate variability for real-time acute pain monitoring with wearable electronics

Abstract

In medical care, it is essential to assess and manage acute painful conditions adequately. Heart rate variability (HRV) analysis is based on the acquisition of electrocardiogram (ECG), which is available from both patient monitor and wearable device. As HRV analysis can reflect autonomic nervous system activity which is unconsciously regulated, HRV analysis in ultra-short-term is getting attention in indicating the reaction due to acute pain. Different HRV features in different window lengths are involved in pain monitoring studies as a signal index or part of a multi-parameter model. In this work, seven HRV features and median heart rate (HR) in ultra-short-term are evaluated for their competence in indicating experimental acute pain. Also, the choice of time window length in HRV analysis and its relation with pain detection are discussed. The results of the normalized HRV analysis from healthy volunteers show that the changes of lnRMSSD, pNN20 and median HR associated with the intensity of experimental electrical pain; and in the tests with experimental thermal pain, lnLF and ln(LF/HF) changed along with pain intensity. The fusion of the HRV features could tell pain from no pain. With either experimental pain stimulation, optimal time window length was observed around or larger than 40 seconds with better correlation analysis result and HRV feature fusion performance.