Noise-robust bioimpedance approach for cardiac output measurement.

Objective: Congestive heart failure is a problem affecting millions of Americans. A continuous, non-invasive, telemonitoring device that can accurately monitor cardiac metrics could greatly help this population, reducing unnecessary hospitalizations and cost.

Approach: Machine learning (ML) algorithms trained on electrical-impedance tomography (EIT) data are presented for portable cardiac monitoring.

Deep Neural Networks for Identifying Cough Sounds.

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. A convolutional neural network and a recurrent neural network are implemented to address these problems, respectively.

Technologies for developing ambulatory cough monitoring devices.

Cough is a prevailing symptom in most lung diseases. While cough sounds themselves can be very instrumental in the diagnosis of certain diseases, their intensity and frequency also infers the intensity of the particular illness. There is an imperative need for a robust system for identifying and analyzing cough sounds.