Department of Electronics and Medical Signal Processing, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany.
Department of Research and Development, Osypka Medical GmbH, Albert-Einstein-Straße 3, 12489 Berlin, Germany.
Sensors (Basel). 2022 Oct 17;22(20):7883. doi: 10.3390/s22207883.
Cardiovascular diseases (CVDs) are one of the leading members of non-communicable diseases. An early diagnosis is essential for effective treatment, to reduce hospitalization time and health care costs. Nowadays, an exercise stress test on an ergometer is used to identify CVDs. To improve the accuracy of diagnostics, the hemodynamic status and parameters of a person can be investigated. For hemodynamic management, thoracic electrical bioimpedance has recently been used. This technique offers beat-to-beat stroke volume calculation but suffers from an artifact-sensitive signal that makes such measurements difficult during movement. We propose a new method based on a gated recurrent unit (GRU) neural network and the ECG signal to improve the measurement of bioimpedance signals, reduce artifacts and calculate hemodynamic parameters. We conducted a study with 23 subjects. The new approach is compared to ensemble averaging, scaled Fourier linear combiner, adaptive filter, and simple neural networks. The GRU neural network performs better with single artifact events than shallow neural networks (mean error -0.0244, mean square error 0.0181 for normalized stroke volume). The GRU network is superior to other algorithms using time-correlated data for the exercise stress test.
心血管疾病（CVDs）是非传染性疾病的主要成员之一。早期诊断对于有效治疗、减少住院时间和医疗保健成本至关重要。如今，在测功机上进行运动应激测试已用于识别 CVDs。为了提高诊断的准确性，可以研究人的血液动力学状态和参数。最近，胸电生物阻抗已用于血液动力学管理。该技术提供了逐拍的搏出量计算，但由于信号对伪影敏感，使得在运动期间进行此类测量变得困难。我们提出了一种基于门控递归单元（GRU）神经网络和 ECG 信号的新方法，以改善生物阻抗信号的测量，减少伪影并计算血液动力学参数。我们对 23 名受试者进行了一项研究。新方法与集合平均、缩放傅里叶线性组合器、自适应滤波器和简单神经网络进行了比较。GRU 神经网络在单个伪影事件方面的性能优于浅层神经网络（归一化搏出量的平均误差为-0.0244，均方误差为 0.0181）。对于运动应激测试，使用时间相关数据，GRU 网络优于其他算法。
