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.

中文翻译：

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使用双向 LSTM 回归根据心冲击图进行无创心率估计


无创心率估计对于心血管疾病的日常监测具有重要意义。在本文中，开发了一种双向长短期记忆（bi-LSTM）回归网络，用于根据心冲击图（BCG）信号进行无创心率估计。所提出的深度回归模型为BCG心率估计中现有的挑战提供了有效的解决方案，例如BCG信号与地面真实参考之间的不匹配、多传感器融合和有效的时间序列特征学习。允许估计中的标签不确定性可以减少数据注释的手动成本，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的强大拟合和泛化能力对BCG中的噪声（例如，传感器噪声）和扰动（例如，身体运动）保持了更好的鲁棒性信号并为长期心率监测提供更可靠的解决方案。