Multi-Scale Domain Classification Based Heart Sound Compression
Ravi M. Potdar1, Manoj K. Kowar2, Amit Biswas3, Mayur Amtey4

1Prof. M. K. Kowar, Director, BIT Durg, India.
2Amit Biswas, Associate Professor, BIT, Durg.
3Ravindra Manohar Potdar, Sr. Associate Professor BIT Durg, India.
4Mayur Amtey, BIT Raipur, India.

Manuscript received on April 15, 2012. | Revised Manuscript received on April 20, 2012. | Manuscript published on May 05, 2012. | PP: 41-44 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0502032212/2012©BEIESP
Open Access | Ethics and Policies | Cite 
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: In recent days, fractal compression has gained a wide popularity due to its inherent features and efficiency in compressing data. In the present communication, fractal compression technique has been applied on heart sound signals for effective compression. Fractal heart sound coding based on the representation of a heart sound signal (1D or vector) by a contractive transform, on the sound data, for which the fixed point (reconstructed heart sound) is close to the original heart sound. The work is intended to provide an approach on this process by introducing the idea of multi-scale Domain pool classification using Variance Fractal Dimension (VFD) based on complexity of the heart sound data. A pre-processing analysis of the heart sound data by VFD to identify the complexity of each sound data samples block for classification has been undertaken. The performance result of the present work has focused in terms of good fidelity signal reconstruction versus encoding time and amount of compression.

Keywords: Phonocardiogram, Fractal Compression, Variance Fractal Dimension, Domain Classification