GRU Neural Network Improved Bioimpedance Based Stroke Volume Estimation during Ergometry Stress Test.

ABSTRACT: 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 artifac

SUBMITTER: Urban M 

PROVIDER: S-EPMC9612153 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature