Improvement of Stroke Volume Estimation with Bioimpedance Measurement by LSTM Network Approach Based on ECG during Ergometry

Abstract

Stroke volume (SV) and cardiac output (CO) estimations with non-invasive approaches like thoracic electrical bioimpedance (TEB) measurement become state of the art in clinical practice. Despite the advantages like low costs, low risk of infection and relatively easy application, there are also disadvantages like the sensitivity to movement artifacts and, electrode displacement mistakes. The bioimpedance signal acquired with a tetrapolar measurement has a relatively weak signal strength compared with another common recorded signal, e.g., the electrocardiogram (ECG). For reconstruction and filtering of the dZ/dt signal, different approaches exist like ensemble averaging (EA), scaled fourier linear combiner (SFLC), wavelet denoising and adaptive filter. We propose an artificial neural network with long short-term memory (LSTM) layer for signal reconstruction during ergometry. The LSTM network performs well compared with other algorithms, e.g., with better amplitude (C point) reconstruction. The SV estimation with the LSTM network was at least comparable or even better than the estimation based on SFLC.