Machine Learning algorithms trained on electrical-impedance tomography data are presented for portable cardiac monitoring. The approach was validated on a simulated thorax and a measured tank experiment.

Our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection.

In this paper an end-to-end CMOS application specific integrated circuit (ASIC) for readout channel in a cardiac electrical impedance tomography (EIT) system is presented.

In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem.