Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases.

Objective: To develop and evaluate innovative methods for compressing and reconstructing complex audio signals from medical auscultation, while maintaining diagnostic integrity and reducing dimensionality for machine classification.

Methods: Using the ICBHI Respiratory Challenge 2017 Database, we assessed various compression frameworks, including discrete Fourier transform with peak detection, time-frequency transforms, dictionary learning and singular value decomposition. Reconstruction quality was evaluated using mean squared error (MSE).

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome.