Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study

The purpose of the study was to examine the development of a multiple linear regression model to estimate heart rate variability (HRV) parameters using easy-to-measure independent variables in preliminary experiments. HRV parameters (time domain: SDNN, RMSSD, NN50, pNN50; frequency domain: TP, VLF, LF, HF) and the independent variables (e.g., sex, age, body height, body weight, BMI, HR, HR(max), HRR) were measured in 75 healthy adults (male n = 27, female n = 48) for estimating HRV. The HRV estimation multiple linear regression model was developed using the backward elimination technique. The regression model’s coefficient of determination for the time domain variables was significantly high (SDNN = R(2): 72.2%, adjusted R(2): 69.8%, P < .001; RMSSD = R(2): 93.1%, adjusted R(2): 92.1%, P < .001; NN50 = R(2): 78.0%, adjusted R(2): 74.9%, P < .001; pNN50 = R(2): 89.1%, adjusted R(2): 87.4%, P < .001). The coefficient of determination of the regression model for the frequency domain variable was moderate (TP = R(2): 75.6%, adjusted R(2): 72.6%, P < .001; VLF = R(2): 41.6%, adjusted R(2): 40.3%, P < .001; LF = R(2): 54.6%, adjusted R(2): 49.2%, P < .001; HF = R(2): 67.5%, adjusted R(2): 63.4%, P < .001). The coefficient of determination of time domain variables in the developed multiple regression models was shown to be very high (adjusted R(2): 69.8%–92.1%, P < .001), but the coefficient of determination of frequency domain variables was moderate (adjusted R(2): 40.3%–72.6%, P < .001). In addition to the equipment used for measuring HRV in clinical trials, this study confirmed that simple physiological variables could predict HRV.