Fundamental Considerations of HRV Analysis in the Development of Real-Time Biofeedback Systems

Abstract

Heart rate variability (HRV) biofeedback training is known for its effectiveness in improving physical health, emotional health, and resilience by the ability to regulate heart rhythm. However, there are various challenges in delivering and interpreting the biofeedback information, which prevents an optimal experience. Therefore, this study presents the fundamentals of developing a real-time HRV biofeedback system using deep breathing exercise by exploring the minimum time window of RR-intervals resulting in a reliable analysis. Moreover, it investigates the appropriate HRV measures by examining the significant changes between resting and breathing conditions and the trends consistency across ultra-short-term segments. The overall results suggest that a minimum time window of 20seconds can provide a reliable HRV time-domain analysis. Whereas the possible HRV measures that can be used in a real-time biofeedback system are SDNN, LF, and total power. These outcomes will contribute to the design of a self-monitoring HRV biofeedback system based on a multi-modal approach.

Real Time Risk Prediction of heart patients using HRV and IoT: A Survey

“…These findings will help to shape the design of a multi-modal self-monitoring HRV biofeedback system. [2] HRV is strongly recognized in seizures; it represents autonomous nervous system disruption and can give important clinical information that can be utilized to detect seizures. Electroencephalography (EEG) was the first instrument used to monitor brain activity and seizures for decades.…”