Decomposing of cardiac and respiratory signals from electrical bio-impedance data using filtering method

This paper presents an attempt to decompose cardiac and respiratory signals from an electrical bioimpedance (EBI) dataset. To accomplish this task, the conventional filtering method is used. FIR (low pass filter (LPF) and high pass filter (HPF)) was intended to decompose the impedance respirogram (IRG) and impedance cardiogram (ICG), (the clean ECG was also extracted by filtering method). The decomposed components can be analysed and processed further, each one separately. Investigation was accomplished under the assumption that the total EBI dataset is the summation of cardiac and respiratory components, motion artefacts, stochastic disturbance and noise. The impedances were measured using a Zurich Instruments HF2IS Impedance Spectroscope. A sixteen electrodes configuration belt was used around a human thorax, to measure the EBI. This study showed that it is not possible to decompose cardiac and respiratory signals completely through conventional filtering method.