Mai Yaozong, Chen Zizhao, Yu Baoxian, Li Ye, Pang Zhiqiang, Han Zhang
IEEE J Biomed Health Inform. 2022 Aug;26(8):3720-3730. doi: 10.1109/JBHI.2022.3162396. Epub 2022 Aug 11.
Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.
受益于非侵入式传感技术，从心冲击图（BCG）信号中检测心跳对于家庭护理应用具有重要意义，如心血管疾病（CVD）风险预测和睡眠分期等。在本文中，我们提出了一种基于UNet和双向长短期记忆（Bi-LSTM）的有效深度学习模型，用于从BCG信号中自动检测心跳。所开发的深度学习模型为BCG辅助心跳检测中的现有挑战提供了有效的解决方案，特别是对于低信噪比的BCG，其中BCG信号中的波形由于测量姿势、节律和伪影运动而不规则。为了进行验证，通过对43名具有不同测量姿势和心率范围的受试者的BCG记录来评估所提出检测方法的性能。与心电图的R-R间期相比，在不同姿势和信号质量下检测到的心跳间期的准确性在平均绝对误差和平均相对误差方面都很有前景，优于现有技术方法。数值结果表明，所提出的UNet-BiLSTM模型对BCG信号中的噪声和干扰（如呼吸作用和伪影运动）具有鲁棒性，并为长期心率监测提供了可靠的解决方案。
