Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 9; pp. 3396 - 3407
• Main Authors: Jiao, Changzhe, Chen, Chao, Gou, Shuiping, Hai, Dong, Su, Bo-Yu, Skubic, Marjorie, Jiao, Licheng, Zare, Alina, Ho, K. C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Ballistocardiograms; bidirectional LSTM; Cardiovascular diseases; deep feature learning; Electrocardiography; Estimation; Heart beat; Heart rate; heart rate estimation; Heart rate variability; hydraulic bed sensor; Hydraulic systems; Long short-term memory; Monitoring; Multisensor fusion; Noise; Perturbation; Regression models; Robustness (mathematics); Transducers
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3077002

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise ( e.g. , sensor noise) and perturbations ( e.g. , body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.