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There is potential for the use of mobile phones to remotely identify patients with a high risk of heart conditions using automated auscultation. However, accurate heart sound analysis is dependent on the quality of heart sound recordings. This paper investigates the signal quality classification of phonocardiograms (PCGs) recorded on two devices (a 3M Littmann 3200 electronic stethoscope and an iPhone 3G). These recordings were professionally annotated and classified using a support vector machine (SVM) and a combination of ten signal quality metrics computed from each recording as input features. One third of all mobile phone-recorded PCGs were found to be of high quality. The classifier was able to distinguish good and bad-quality iPhone recordings with 87.0% accuracy, the Littmann recordings with accuracy of 76.4% and the combined set with accuracy of 85.6% on unseen test data. Therefore, the quality of PCGs made with a range of stethoscopes can be accurately classified using this technique. © 2014 IEEE.

Original publication




Conference paper

Publication Date



1335 - 1339