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Respiratory sinus arrhythmia (RSA) is a phenomenon in which the heart rate (HR) changes with respiration, increasing during inspiration and decreasing during expiration. RSA biofeedback training has an effect in relieving negative mental conditions, such as anxiety and stress. Respiration is an important indicator affecting the parasympathetic activation within the body during RSA biofeedback training. Although there are existing studies that consider individual differences when selecting optimized respiration using heart rate variability, the studies that use the high frequency components of HRV, which is an indicator of parasympathetic activation, are insufficient. For this reason, this paper proposes a process to identify optimized respiration for efficient RSA feedback, consisting of three steps: (1) application, (2) optimization, and (3) validation. In the application phase, we measured PPG data against various respiratory cycles based on the HF components of HRV and calculated the proposed heart stabilization indicator (HSI) from the data. Then, we determined the optimized respiration cycle based on the HSI in the optimization step. Finally, we analyzed seven stress-related indices against the optimized respiration cycle. The experimental results show that HSI is associated with the parasympathetic nervous system activation, and the proposed method could help to determine the optimal respiratory cycle for each individual. Lastly, we expect that the proposed design could be used as an alternative to improving the efficiency of RSA biofeedback training.

Process Design for Optimized Respiration Identification Based on Heart Rate Variability for Efficient Respiratory Sinus Arrhythmia Biofeedback