Hai Dong, Chen Chao, Yi Ruhan, Gou Shuiping, Yu Su Bo, Jiao Changzhe, Skubic Marjorie
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:451-454. doi: 10.1109/EMBC44109.2020.9176726.
Inspired by the application of recurrent neural networks (RNNs) to image recognition, in this paper, we propose a heartbeat detection framework based on the Gated Recurrent Unit (GRU) network. In this contribution, the heartbeat detection task from ballistocardiogram (BCG) signals was modeled as a classification problem where the segments of BCG signals were formulated as images fed into the GRU network for feature extraction. The proposed framework has advantages in fusion of multi-channel BCG signals and effective extraction of the temporal and waveform characteristics of the heartbeat signal, thereby enhancing heart rate estimation accuracy. In laboratory collected BCG data, the proposed method achieved the best heart rate estimation results compared to previous algorithms.
受递归神经网络（RNN）在图像识别中的应用启发，本文提出了一种基于门控递归单元（GRU）网络的心跳检测框架。在此贡献中，将来自心冲击图（BCG）信号的心跳检测任务建模为一个分类问题，其中BCG信号段被构建为输入到GRU网络进行特征提取的图像。所提出的框架在多通道BCG信号融合以及心跳信号的时间和波形特征有效提取方面具有优势，从而提高心率估计精度。在实验室收集的BCG数据中，与先前算法相比，所提出的方法取得了最佳的心率估计结果。
