Feng Shen, Wu Xianda, Bao Andong, Lin Guanyang, Sun Pengtao, Cen Huan, Chen Sinan, Liu Yuexia, He Wenning, Pang Zhiqiang, Zhang Han
Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China.
School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, China.
Front Physiol. 2023 Jan 19;13:1068824. doi: 10.3389/fphys.2022.1068824. eCollection 2022.
Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ). This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.
在新冠疫情及住院费用的影响下，近期利用智能传感设备在家中检测心血管疾病变得愈发流行。在有合格信号的情况下，心冲击图（BCG）不仅能反映心脏的机械运动，还能以非接触方式检测心力衰竭（HF）。然而，对于潜在的HF患者，借助心电图进行额外的质量评估需要更多步骤，给他们在家中进行HF诊断带来不便。为了在许多实际应用中实现HF检测，我们在本文中提出了一种用于HF检测的机器学习辅助方案，其中使用从力传感器记录的BCG信号而无需心跳位置，并且由于心脏和肺部系统之间的联系，从力传感器分离出的呼吸用力信号提供了更多HF特征。最后，在对比实验中验证了所提出的HF检测方案的有效性。首先，使用压电传感器记录二维生命体征的信号序列，其中包括BCG和呼吸用力信号。然后，提取与BCG和呼吸用力信号相关的线性和非线性特征以用于HF检测。最后，通过使用不同机器学习分类器的留一法（LOO）和留样本外法（LOSO）交叉验证设置，验证了改进后的HF检测性能。所提出的机器学习辅助方案通过使用4种不同分类器在HF检测中实现了稳健性能，在LOO和LOSO实验中分别取得了94.97%和87.00%的准确率。此外，实验结果表明，所设计的呼吸和心肺特征有利于HF检测（左心室射血分数）。本研究提出了一种机器学习辅助的HF诊断方案。实验结果表明，所提出的方案可以充分利用心脏和肺部系统之间的关系，通过同时使用BCG、呼吸和心肺相关特征来潜在地提高在家中进行HF检测的性能。
