Phonocardiogram Signal Segmentation and Classification Using Variance Fractal Dimension

Nirmla Verma, Amit Kumar Biswas,. R. M. Potdar

Abstract


This paper presents an algorithm for S1 and S2 heart sound segmentation using variance fractal dimension. Heart sounds analysis can provide lots of information about heart condition whether it is normal or abnormal. Heart sounds signals are time-varying signals. We explore the performance of applying fractal coding on heart sound data. Some conventional fractal coding problems have been studied with heart sound data to provide an overview on this subject. Heart sound is assumed as a non-stationary signal embedding two main sounds S1 and S2, murmurs and eventually unusual ambient sound. The variance fractal dimension is applied to adaptively identify the boundaries of sound lobes. S1 components are detected using QRS synchronization while for S2 components a non-supervised classification approach is applied, based on temporal features of the lobes. Some preliminary results are presented using recorded heart sounds.


Keywords – Heart Sound Segmentation, Variance Fractal Dimension, Clustering.


Full Text:

PDF


DOI: https://doi.org/10.26483/ijarcs.v4i8.1817

Refbacks

  • There are currently no refbacks.




Copyright (c) 2016 International Journal of Advanced Research in Computer Science