Latest Publications
Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases
Abstract
Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads...
Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life.
Compressed sensing data with performing audio signal reconstruction for the intelligent classification of chronic respiratory diseases
Abstract
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...
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.
Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases
Abstract
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...
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. 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.
