School of Electronics and Information Engineering, South China Normal University, Guangzhou 510006, China.
Guangzhou SENVIV Technology Co. Ltd, Guangzhou 510006, China.
J Healthc Eng. 2022 Jan 27;2022:6388445. doi: 10.1155/2022/6388445. eCollection 2022.
As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. . This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error ( ) and an averaged relative error ( ) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.
由于使用力传感器从心冲击图（BCG）信号中检测心跳会受到呼吸努力和伪影运动的干扰，因此需要先进的信号处理算法来检测每个 BCG 信号的 J 波峰，以便识别心跳间隔。然而，现有的方法通常依赖于基于规则的固定大小检测，而不考虑覆盖多个 BCG 信号的大时间尺度的节律特征。 。本文提出了一种基于 ResNet 和双向长短期记忆（BiLSTM）的深度学习框架，用于进行 BCG 信号的逐拍检测。与现有方法不同，所提出的网络将 BCG 信号的多尺度特征作为输入，因此可以同时利用单个 BCG 信号的形态特征和多个 BCG 信号的节律特征的互补优势。通过实验验证和分析了所提出模型的多尺度特征的不同时间尺度。使用留一交叉验证对来自 21 名健康受试者的 BCG 信号进行了验证，以验证所提出的心跳检测方案的性能。研究了不同时间尺度对检测性能的影响以及所提出的模型对不同睡眠姿势的性能。数值结果表明，所提出的多尺度模型对睡眠姿势具有鲁棒性，相对于 R-R 间隔，心跳间隔的平均绝对误差（ ）和平均相对误差（ ）分别为 9.92 ms 和 2.67 ms，优于现有检测协议。 。在这项工作中，设计了一种使用 BCG 信号进行心跳检测的多尺度深度学习模型。我们通过实验证明，使用 BCG 信号的多尺度特征进行检测可以提供优于现有工作的性能。进一步的研究将检查多尺度模型在实际场景中的最终性能，即患有心血管疾病的患者的检测与夜间睡眠监测。
