Exploratory Analysis of Ultra-Short-Term Heart Rate Variability Features in Virtual Rehabilitation Sessions
| Autor: | Andrew Smith, Janet Holly, James R. Green, Alistair Boyle, Roger Selzler, Courtney Bridgewater, Adrian D. C. Chan, Markus Besemann, Francois Charih, Dorothyann Curran |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Rehabilitation Traumatic brain injury business.industry medicine.medical_treatment Feature extraction Feature selection medicine.disease Correlation 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Feature (computer vision) medicine Heart rate variability Virtual rehabilitation 030212 general & internal medicine business 030217 neurology & neurosurgery |
| Zdroj: | MeMeA |
| Popis: | We are currently collecting multi-modal data from patients undergoing rehabilitation therapy using virtual reality for mild traumatic brain injury (mTBI), post-traumatic stress disorder (PTSD), and complex pain, with the goal of developing novel unobtrusive estimators of Sympathetic Activation of the Autonomic Nervous System (SAANS). In this study, we investigate whether heart rate variability (HRV) features extracted from electrocardiogram (ECG) measurements are correlated with clinical estimates of patient SAANS. Although previous studies have shown such correlation, the minimum amount of ECG data typically required for HRV feature extraction preclude their application in the present study; SAANS is expected to change quickly during therapy sessions and a rapid estimator is required. This paper investigates the use of ultra-short-term features extracted from ECG measurements for SAANS estimation with data collected during rehabilitation sessions with a Computer-Assisted Rehabilitation Environment (CAREN). A comparison between different scores and time excerpts were analysed using a single patient (n=1), over fifteen sessions, and sixty-seven activities. Preliminary results show that there was a significant difference between the AFTER and START excerpts (p = 0.045) for the meanNN feature, and between score "four" to scores "zero" (p = 0.01), and "three" (p = 0.02) for the stdNN feature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|