IEEE J Biomed Health Inform. 2021 Sep;25(9):3396-3407. doi: 10.1109/JBHI.2021.3077002. Epub 2021 Sep 3.
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.
非侵入式心率估计在心血管疾病的日常监测中具有重要意义。本文提出了一种双向长短期记忆（bi-LSTM）回归网络，用于从心冲击图（BCG）信号中进行非侵入式心率估计。所提出的深度回归模型为 BCG 心率估计中存在的挑战提供了有效的解决方案，例如 BCG 信号与真实参考之间的不匹配、多传感器融合以及有效的时间序列特征学习。允许在估计中存在标签不确定性，可以降低数据标注的人工成本，同时进一步提高心率估计性能。与最先进的 BCG 心率估计方法相比，所提出的深度回归模型具有更强的拟合和泛化能力，对 BCG 信号中的噪声（例如传感器噪声）和干扰（例如身体运动）具有更好的鲁棒性，为长期心率监测提供了更可靠的解决方案。
