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Updated: Jul 30, 2025
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
Development of a Heart Rate Variability Prediction Equation Through Multiple Linear Regression Analysis Using
Sung-Woo Kim1,2, Hun-Young Park1,2, Hoeryong Jung3
1Physical Activity and Performance Institute, Konkuk University, Seoul, Republic of Korea.
Abstract:
Heart rate variability (HRV) is an effective tool for objectively evaluating physiological stress indices in psychological states. This study aimed to develop multiple linear regression equations to predict HRV variables using physical characteristics, body composition, and heart rate (HR) variables (eg, sex, age, height, weight, body mass index, fat-free mass, percent body fat, resting HR, maximal HR, and HR reserve) in Korean adults. Six hundred eighty adults (male, n = 236, female, n = 444) participated in this study. HRV variable estimation multiple linear regression equations were developed using a stepwise technique. The regression equation's coefficient of determination for time-domain variables was significantly high (SDNN = adjusted R2: 73.6%, P < .001; RMSSD = adjusted R2: 84.0%, P < .001; NN50 = adjusted R2: 98.0%, P < .001; pNN50 = adjusted R2: 99.5%, P < .001). The coefficient of determination of the regression equation for the frequency-domain variables was high without VLF (TP = adjusted R2: 75.0%, P < .001; LF = adjusted R2: 77.6%, P < .001; VLF = adjusted R2: 30.1%, P < .001; HF = adjusted R2: 71.3%, P < .001). Healthcare professionals, researchers, and the general public can quickly evaluate their psychological conditions using the HRV variables prediction equation.
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