Analysis of the vascular sounds of the arteriovenous fistula's anastomosis.

In this paper, the vascular sounds of the arteriovenous fistula at the anastomosis and five centimeters downstream the anastomosis were analyzed. The analysis of the sounds was based on features extracted from the power spectral density (PSD) and wavelet decomposition. The database consists of 15 recordings at the anastomosis and 15 reference recordings obtained from 15 patients.

Acoustical detection of venous stenosis in hemodialysis patients using principal component analysis.

In this paper, a feature extraction method based on principal component analysis was developed for classification of the vascular access's condition in hemodialysis patients. The assessment of the method was carried out by discriminating between before and after angioplasty sound recordings as well as before angioplasty and reference recordings. The results showed that when before and after angioplasty recordings were compared by patient, the classification agreed with the result of angioplasty procedure.

Application of the Empirical Mode Decomposition in the study of murmurs from Arteriovenous fistula stenosis.

The Empirical Mode Decomposition (EMD) is a method to decompose non linear, non stationary time series into a sum of different modes, named Intrinsical Mode Functions each one having a characteristic frequency. In the present work we used the EMD to investigate the properties of the recorded sounds from the Arteriovenous fistula on hemodialysis patients. Phonoangiographic signals coming from two different vessel conditions, stenotic and non-stenotic, were analyzed by using EMD, the mean energy and mean instantaneous frequency per IMF proved to be good features for classification.

Arteriovenous fistula stenosis detection using wavelets and support vector machines.

The objective of this exploratory study was to develop signal processing methods for assisting in the diagnosis of arteriovenous fistula stenosis on patients suffering from endstage renal disease and undergoing haemodialysis treatments. The proposed method is based on the classification of vessels sounds utilizing parameter extraction from wavelets transform coefficients. The coefficients energy of selected scales (frequency bands) were fed to a support vector machine based system for classification.