Novel Detection Algorithm For Impedance Pneumography Signal's Base Impedance Sharp Transitions

Measuring the respiration through the impedance pneumography has been introduced in late 90s and is considered now as a standard for measurement. The measurement procedure is performed using a carrier signal projected to the thoracic cavity with a high frequency component. Two challenges are to be overcome in this case; the first one is designing a system that is capable of detecting these tiny changes in the impedance (impedance rate of change), and the other is a designing an algorithm that detects the rapid, large changes in the base impedance. In this research, three solutions are proposed, implemented, tested and a comparison amongst them is made. These three solutions are; conventional convolutional band pass filter, simple back-to-back differentiator-integrator and multirate filter; respectively. The three solutions are implemented and tested on both simulated respiration signals and real offline (recorded) signals. The output signals from multi-rate filter are found to be consistent with the simulated and recorded ones. While convolutional band pass filter failed completely with detecting the sharp and harmonic transitions, the simple back-to-back differentiator-integrator failed with detecting the harmonic transitions, yet it succeeded in detecting sharp transitions only.