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Bioengineering 2018, 5(4), 107; https://doi.org/10.3390/bioengineering5040107

Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning

1
The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester M5 4WT, UK
2
Warrington Hospital, Lovely Lane, Warrington, Cheshire WA5 1QG, UK
3
Taybank Medical Centre, 10 Robertson Street, Dundee, DD4 6EL, UK
*
Author to whom correspondence should be addressed.
Received: 8 November 2018 / Revised: 26 November 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
(This article belongs to the Special Issue Biosignal Processing)
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Abstract

Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications. View Full-Text
Keywords: osteoporosis; electronic stethoscope; vibro-acoustics; machine learning; resonant frequency; impulse response; signal processing; pattern recognition; classification osteoporosis; electronic stethoscope; vibro-acoustics; machine learning; resonant frequency; impulse response; signal processing; pattern recognition; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Scanlan, J.; Li, F.F.; Umnova, O.; Rakoczy, G.; Lövey, N.; Scanlan, P. Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning. Bioengineering 2018, 5, 107.

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