Application of machine learning and signal processing techniques to the design of a smart stethoscope

Lead Research Organisation: University of Cambridge
Department Name: Engineering


Designing machine learning and signal processing algorithms to automatically diagnose cardiovascular and respiratory illnesses, using heart and lung sound recordings obtained via an electronic stethoscope. These algorithms will then contribute towards the design of a 'smart' stethoscope that can be used by physicians to aid in patient diagnosis


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509620/1 01/10/2016 30/09/2021
1936431 Studentship EP/N509620/1 01/10/2017 30/09/2021 Andrew Thomas McDonald
Description Innovation in Heart Valve Disease Meeting
Geographic Reach Local/Municipal/Regional 
Policy Influence Type Participation in a advisory committee
Description An Intelligent Stethoscope
Amount £651,538 (GBP)
Funding ID MR/S036644/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 06/2019 
End 02/2022
Title Valvular heart disease dataset for development of intelligent stethoscope 
Description Collation of heart sound stethoscope recordings, echocardiogram images and anonymised patient metadata. For use in training machine learning algorithms to predict valvular heart disease from stethoscope data. Diagnosis of valvular heart disease is done by echocardiogram and used as the ground truth labels. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? No  
Impact Ongoing development of machine learning algorithm to predict significant valvular heart disease from stethoscope recordings. 
Description Collaboration with Papworth Hospital for clinical studies 
Organisation Papworth Hospital NHS Foundation Trust
Country United Kingdom 
Sector Public 
PI Contribution Application for funding from MRC for study titled "Cardiovascular Acoustics and an Intelligent Stethoscope". Design and management of study, which is collecting heart sounds and echocardiograms from valvular heart disease patients in the NHS. Research and development of an algorithm for automated diagnosis of valvular heart disease from the heart sounds collected.
Collaborator Contribution Co-applicant for funding from MRC. Design and on-going management of the study, and curation of the resulting dataset. Providing clinical knowledge to aid design of algorithms.
Impact Ongoing study "Cardiovascular Acoustics and an Intelligent Stethoscope" into valvular heart disease and AI diagnosis.
Start Year 2015
Title Smart stethoscopes 
Description Development of machine learning model for automatic detection of murmurs in stethoscope recordings 
IP Reference WO2019GB50461 
Protection Patent application published
Year Protection Granted
Licensed No
Impact n/a
Title Cardiovascular Acoustics and an Intelligent Stethoscope Research Study 
Description An observational research study to collect heart sound recordings from patients to understand the acoustic characteristics of murmurs and develop an artificially intelligent stethoscope. The research study is collating a dataset of stethoscope heart sound recordings, echocardiogram ultrasound images and patient data, which are being used to train machine learning algorithms to predict valvular heart disease from heart sounds. The study is funded by a Development Pathway Funding Scheme from the Medical Research Council (MR/S036644/1). 
Type Diagnostic Tool - Non-Imaging
Current Stage Of Development Refinement. Clinical
Year Development Stage Completed 2020
Development Status Under active development/distribution
Impact Still under development. 
Company Name BioPhonics Ltd 
Description Development of novel products for intelligent diagnostics, driven by state-of-the-art machine learning software. 
Year Established 2018 
Impact n/a
Description Innovation in Valve Disease Meeting 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Meeting of NHS consultants, industry professionals, and academics, to discuss innovative methods to improve patient care across the heart valve disease pathway. We presented a demo of our automated heart murmur detection software and discussed how it could be implemented in a practical setting. The discussion raised several interesting points for future research and led to further opportunities for collaboration with clinicians and policymakers.
Year(s) Of Engagement Activity 2020