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

Scanlan, Jamie; Li, Francis F; Umnova, Olga; Rakoczy, Gyorgy; Lövey, Nóra; Scanlan, Pascal

Scanlan, Jamie; Li, Francis F; Umnova, Olga; Rakoczy, Gyorgy; Lövey, Nóra; Scanlan, Pascal.

Afiliación

Scanlan J; The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester M5 4WT, UK. j.scanlan1@edu.salford.ac.uk.
Li FF; The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester M5 4WT, UK. f.f.li@salford.ac.uk.
Umnova O; The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester M5 4WT, UK. o.umnova@salford.ac.uk.
Rakoczy G; The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester M5 4WT, UK. george.rakoczy04@gmail.com.
Lövey N; Warrington Hospital, Lovely Lane, Warrington, Cheshire WA5 1QG, UK. n.lovey@nhs.net.
Scanlan P; Taybank Medical Centre, 10 Robertson Street, Dundee, DD4 6EL, UK. pscanlan@nhs.net.

Bioengineering (Basel) ; 5(4)2018 Dec 05.

Article en En | MEDLINE | ID: mdl-30563076

Palabras clave

classification; electronic stethoscope; impulse response; machine learning; osteoporosis; pattern recognition; resonant frequency; signal processing; vibro-acoustics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Screening_studies Idioma: En Revista: Bioengineering (Basel) Año: 2018 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Suiza

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