Biofeedback of elderly patients with chronic pain: new nonlinear Heart Rate Variability analysis

Background. Chronic pain presents a substantial clinical challenge affecting individuals across all age groups, regardless of whether they are adults or older adults. To underscore the impact of biofeedback in managing chronic pain, we conducted a statistical analysis to explore its short-term effectiveness and factors influencing treatment outcomes. Purpose – to develop the advanced heart rate variability (HRV) methods that reflect a statistically significant relationship between the impact of biofeedback on chronic pain control and HRV indicators that outline changes in the influence of the sympathetic and parasympathetic systems in pain regulation. Materials and Methods. Elderly patients with mean age 76.3 ± 7.5 years suffering from Chronic Pain associated with Chronic Skeletal Illness. Prior to treatment and after a 15-day period, all participants underwent assessment of pain severity. Additionally, each participant underwent a 5-minute EKG recording before and after treatment to evaluate Heart Rate Variability (HRV). Neuro-vegetative cardiovascular modulation was assessed through EKG analysis of HRV before and after treatment. Biofeedback sessions (5 breaths per minute) were conducted twice daily for 5 minutes over the course of 15 days. For the purpose of this research data analysis, we propose a novel Heart Rate Variability (HRV) methodology incorporating robust entropy estimation and fuzzy logic algorithms. The robust entropy estimation algorithm enables precise computation of entropy values from time series data of limited length, while the fuzzy logic algorithm facilitates integration of various HRV metrics (including time domain, frequency domain, and nonlinear methods) into a unified framework. Results. Through the utilization of this proposed methodology, we assess the therapeutic efficacy of biofeedback and the involvement of the neuro-vegetative cardiovascular system in chronic pain. Conclusions. Our preliminary findings reveal a statistically significant reduction in pain severity, as measured by the Visual Analog Scale (VAS), without a statistically significant alteration in neuro-vegetative cardiovascular modulation using conventional analysis techniques. However, the application of the new HRV methodology incorporating robust entropy estimation and fuzzy logic algorithms enables the detection of significant variations.