1D U-NET++: AN EFFECTIVE METHOD FOR BALLISTOCARDIOGRAM J-PEAK DETECTION

Abstract

Heart rate measurement through Ballistocardiogram (BCG) signal is an efficient method for long-term cardiac activity monitoring in real-time, especially for patients with cardiovascular and cerebrovascular disease. In this study, we propose a one-dimensional (1D) U-net++ to identify the position of J-peak in BCG signals automatically. The proposed 1D U-net++ is based on a 1D convolution neural network through dense skip connection backward transfer data features. The low-level and high-level data features of the BCG signals are combined with the last layer features of 1D U-net++ to shorten the semantic gap when the encoder and decoder feature skip connection. The BCG signals of eight healthy subjects were collected for experimental verification, and the accuracy and precision of J-peak detection reached 99.4% and 99.3%, respectively. The experimental results demonstrate that our proposed method can effectively identify J-peak in BCG signal.