1. Automated signal quality assessment of mobile phone-recorded heart sound signals.
- Author
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Springer, David B., Brennan, Thomas, Ntusi, Ntobeko, Abdelrahman, Hassan Y., Zühlke, Liesl J., Mayosi, Bongani M., Tarassenko, Lionel, and Clifford, Gari D.
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MOBILE health , *HEART sounds , *BIOMEDICAL signal processing , *CELL phones , *PHONOCARDIOGRAPHY , *TELEMEDICINE , *ALGORITHMS , *SIGNAL processing , *HEART auscultation ,RESEARCH evaluation - Abstract
Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger’s kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
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