IEEE J Biomed Health Inform. 2024 Aug;28(8):4553-4564. doi: 10.1109/JBHI.2024.3400961. Epub 2024 Aug 6.
This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.
本研究介绍了一种结合传统雷达信号处理和新型深度学习架构的非接触式血压监测方法。在预处理阶段，通过在时域中集成卡尔曼滤波、多尺度带通滤波器和周期性提取方法，创建适合同步的数据集。这些数据包括胸部微变化的数据，包含了反映心脏微运动的复杂生理和生物医学信息数组。基于雷达的堆叠可变形卷积网络（RSD-Net）在可变形卷积框架内集成通道和空间自注意力机制，以增强从雷达信号中提取特征。该网络架构系统地使用可变形卷积从单个信号中进行初始深度特征提取。然后，使用自注意力机制对来自单个源的特征图以及多特征图通道注意力进行连续血压估计。通过使用非侵入性 24GHz 六端口连续波雷达系统获取的开源数据集来验证模型的性能。该数据集包含 30 名健康个体在不同条件下的读数，包括休息、瓦尔萨尔瓦动作、呼吸暂停和倾斜台检查。它证明了该方法在非接触式连续血压评估中的有效性和弹性。评估指标显示收缩压和舒张压预测的皮尔逊相关系数分别为 0.838 和 0.797。收缩压和舒张压测量的平均误差（ME）和标准偏差（SD）分别为-0.32±6.14mmHg 和-0.20±5.50mmHg。消融研究评估了 RSD-Net 不同结构组件的贡献，验证了它们在模型性能整体中的重要性。
