Heart rate prediction model based on neural network

Abstract

Heart is an essential organ of human body and heart rate (HR) is the most obvious heart activity in daily life. In order to predict heart rate, a heart rate prediction model based on LSTM (Long Short-Term Memory) neural network is proposed in this paper. This model combines five physiological parameters as input to ensure the validity of heart rate prediction. The results show that Adam-LSTM is a good method for heart rate prediction and reflects the tendency of heart rate change in daily life. At the same time, the experimental results show that the root mean square error of prediction value is small and the validity is high.