Respiratory disease diagnosis using only the sound of a patient’s cough
ResApp’s technology, originally developed by Associate Professor Udantha Abeyratne at The University of Queensland is based on the premise that cough and breathing sounds carry vital information on the state of the respiratory tract. ResApp was created to diagnose and measure the severity of a wide range of chronic and acute diseases such as pneumonia, asthma, bronchiolitis and chronic obstructive pulmonary disease (COPD) using this insight.
Typically, doctors use stethoscopes to listen to the lungs as the first indication of a respiratory problem. The information available from these sounds is compromised as the sound has to first pass through the chest musculature which muffles high-pitched components of respiratory sounds. In contrast, the lungs are directly connected to the atmosphere during respiratory events such as coughs. These audible sounds, used by ResApp, contain significantly more information than the sounds picked up by a stethoscope. ResApp’s approach is automated and removes the need for human interpretation of respiratory sounds.
We have taken a machine learning approach to develop highly-accurate algorithms which diagnose disease from cough and respiratory sounds. Machine learning is an artificial intelligence technique that constructs algorithms with the ability to learn from data. In our approach, signatures that characterise the respiratory tract are extracted from cough and breathing sounds. We start by matching signatures in a large database of sound recordings with known clinical diagnoses. Our machine learning tools then find the optimum combination of these signatures to create an accurate diagnostic test or severity measure (this is called classification). Importantly, the team at The University of Queensland believes these signatures are consistent across the population and not specific to an individual so there is no need for a personalised database.
Over the last five years the research team led by Associate Professor Abeyratne has pioneered a unique set of signatures and classifier technology that accurately characterises the respiratory tract. Their approach forms a powerful platform for respiratory disease diagnosis and management. The platform is based on sound alone and does not require physical contact with the patient. With modern smartphones integrating high quality microphones, the platform can be delivered without the need for additional hardware.
Sharan RV, Abeyratne UR, Swarnkar VR, Porter P, Automatic croup diagnosis using cough sound recognition, IEEE Transactions on Biomedical Engineering (Early Access), 2018.
Sharan RV, Abeyratne UR, Swarnkar VR, Claxton S, Hukins C, Porter P, Predicting spirometry readings using cough sound features and regression, Physiological Measurement 39(9), 2018.
Kosasih K, Abeyratne UR, Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis, World Journal of Pediatrics 13(5), 2017.
Kosasih K, Abeyratne UR, Swarnkar V, Triasih R, Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis, IEEE Transactions on Biomedical Engineering 62(4), 2015.
Amrulloh YA, Abeyratne UR, Swarnkar V, Triasih R, Setyati A, Automatic cough segmentation from non-contact sound recordings in pediatric wards, Biomedical Signal Processing and Control 21, 2015.
Swarnkar V, Abeyratne UR, Chang AB, Amrulloh YA, Setyati A, Triasih R, Automatic identification of wet and dry cough in pediatric patients with respiratory diseases, Annals Biomedical Engineering 41(5), 2013.
Abeyratne UR, Swarnkar V, Setyati A, Triasih R, Cough sound analysis can rapidly diagnose childhood pneumonia, Annals Biomedical Engineering 41(11), 2013.
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