Wheezes are abnormal continuous adventitious lung sounds that are strongly related to patients with obstructive airways diseases. Wireless tele-monitoring of these sounds facilitate early diagnosis (short, long term) and management of chronic inflammatory disease of the airways (e.g., asthma) through the use of an accurate and energy efficient m-health system. Therefore, low complexity breath compression schemes with high compression ratio are required. To this end, we propose a compressed sensing based compression/reconstruction solution that enables wheeze detection from a small number of linearly encoded samples, by exploiting the block sparsity of the breath eigen-spectrum during reconstruction at the receiver. Simulation studies, carried out with publicly available breath sounds, show the energy efficiency benefits of the proposed CS scheme, compared to traditional CS recovery approaches.
A. Lalos, K. Moustakas. “Energy Efficient Telemonitoring of Asthmatic Wheezes”. In proceedings of the 2015 European Signal Processing Conference. Nice, France, 31 August- 4 September 2015.