ArticleMedical & biological engineering & computing2015
Hemodialysis vascular access stenosis detection using auditory spectro-temporal features of phonoangiography.
Article in Medical & biological engineering & computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Preliminary evaluation of a novel surface accelerometer (HAVSE) for dialysis access surveillance: An exploratory pilot study.The journal of vascular access · 2026Article
- Article
- Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.Sensors (Basel, Switzerland) · 2024Article
- Reliable Stenosis Detection Based on Thrill Waveform Analysis Using Non-Contact Arteriovenous Fistula Imaging.Sensors (Basel, Switzerland) · 2024Article
- Quantification and Visualization of Reliable Hemodynamics Evaluation Based on Non-Contact Arteriovenous Fistula Measurement.Sensors (Basel, Switzerland) · 2022Article
- Effect of stenosis shape on the sound emitted from a constricted blood vessel.Medical & biological engineering & computing · 2020Article
- Flexible, Skin Coupled Microphone Array for Point of Care Vascular Access Monitoring.IEEE transactions on biomedical circuits and systems · 2019Article
- Stenosis Characterization and Identification for Dialysis Vascular Access.... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium · 2018Article
- Bruit-enhancing phonoangiogram filter using sub-band autoregressive linear predictive coding.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018Article
- Bilateral photoplethysmography for arterial steal detection in arteriovenous fistula using a fractional-order decision-making quantizer.Medical & biological engineering & computing · 2017Article
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5 authors.
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Abstract
For end-stage renal disease patients undergoing hemodialysis, thrombosis caused by stenosis hinders the long-term use of vascular access. However, traditional spectral bruit analysis techniques for detecting the severity of vascular access stenosis are not robust. Accordingly, the present study proposes an automated method for mimicking a trained practitioner in performing the auscultation process. In the proposed approach, the bruit obtained using a standard phonoangiographic method is transformed into the time-frequency domain, and two spectro-temporal features, namely the auditory spectrum flux and the auditory spectral centroid, are then extracted. The distributions of the two features are analyzed using a multivariate Gaussian distribution (MGD) model. Finally, the distribution parameters of the MGD model are used to detect the presence (or otherwise) of vascular access stenosis. The validity of the proposed approach is investigated using the phonoangiography signals obtained from 16 hemodialysis patients with straight arteriovenous grafts over the upper arm region. The results show that the MGD covariance matrix coefficient of the auditory spectral centroid feature yields an accuracy of 83.87 % in detecting significant vascular access stenosis. Thus, the proposed method has significant potential for the applications of vascular access stenosis detection.
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