Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a challenge. The aim of this study was to estimate HR from a bed-based pressure mat BSG signal using a deep learning approach. We trained a U-Net deep neural network through supervised learning by deriving ground truth as the location of the heartbeats from simultaneously recorded ECG signals after peak matching. For improved generalization, we modified an existing U - Net to include an IC-layer. A predictive performance of 80% was achieved using the U-Net without the IC-layer. The inclusion of the IC-layer, while improving the generalization ability of the model to detect heartbeats, did not improve the HR estimation performance.
Deep Learning , Electrocardiography , Heart Rate , Humans , Neural Networks, Computer
