Estimation of Heart Rate Using Regression Models and Artificial Neural Network in Middle-Aged Adults

Abstract

Purpose: Heart rate is the most commonly used indicator in clinical medicine to assess the functionality of the cardiovascular system. Most studies have focused on age-based equations to estimate the maximal heart rate, neglecting multiple factors that affect the accuracy of the prediction. Methods: We studied 121 middle-aged adults at an average age of 57.2years with an average body mass index (BMI) of 25.9. The participants performed on a power bike with a starting wattage of 0W that was increased by 25W every 3min until the experiment terminated. Ambulatory blood pressure and electrocardiography were monitored through gas metabolic analyzers for safety concerns. Six descriptive characteristics of participants were observed, which were further analyzed using a multivariate regression model and an artificial neural network (ANN). Results: The input variables for the multivariate regression model and ANN were selected by correlation for the reduction of dimension. The accuracy of estimation by multivariate regression model and ANN was 9.74 and 9.42%, respectively, which outperformed the traditional age-based model (with an accuracy of 10.31%). Conclusion: This study provides comprehensive approaches to estimate the maximal heart rate using multiple indicators, revealing that both the multivariate regression model and ANN incorporated with age, resting heart rate (RHR), and second-order heart rate (SOHR) are more accurate than univariate models.