Compressed Sensing of Acoustic Cardiopulmonary Signals Using a CNN-based Reconstruction Method

Cardiopulmonary sounds contain a rich reservoir of vital and pathological information critical for clinical diagnosis. This paper presents a novel approach to cardiopulmonary data capturing with compressive sensing and reconstruction using a Convolutional Neural Network (CNN) based on the U-Net architecture. Applying traditional compressive sensing techniques to cardiopulmonary sounds presents several challenges. Cardiopulmonary sounds are inherently complex, with a substantial variation between captures. The traditional algorithms for compressive sensing rely on signal sparsity, whereas finding a sparse representation domain for cardiopulmonary sounds is a difficult task. Instead of finding a sparse domain manually, we propose training a convolutional encoder-decoder neural network for a pseudo-randomly undersampled set of signals without explicitly enforcing the sparsity concept. In this research, a CNN was trained for pseudo-randomly decimated input signals, evaluating a compression ratio of up to 30. The network is trained for respiratory sounds using the SPRSound dataset and for Phonocardiogram (PCG) signals using the CirCor Digiscope PCG dataset. Both these datasets have been evaluated for signal integrity after reconstruction and delivered promising results. The algorithm achieves reconstruction quality similar to that of previous research with a compression ratio three times higher than that of previous research applied to respiratory sounds. Since the principles of compressive sensing are applied in the sampling stage, the data compression requires no computation in the compression stage, and can therefore easily be implemented in low-cost edge devices.Clinical relevance- This work enables efficient compression of cardiopulmonary sounds, maintaining high signal integrity even at three times higher compression ratios than previous methods applied to respiratory sounds. It supports low-power, portable devices for real-time monitoring, improving accessibility for telemedicine and point-of-care diagnostics in respiratory and cardiovascular conditions.