VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals
Date
2019-04
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Series Info
International Journal of Computer Applications;(0975 – 8887) Volume 181 – No. 48, April 2019
Scientific Journal Rankings
Abstract
The measurement of electrical activity of the heart via
electrodes is named as Electrocardiography (ECG). An
efficient compression technique using the compressive
sensing method is required. Compressive Sensing (CS) holds
the promise to be a key for acquisition and reconstruction of
sparse signals. The reconstruction of such signals makes
sampling rates below Nyquist rate. In this work, a novel
framework was proposed that is based on the idea of CS
theory for the compression of mother and fetal heart beats.
The proposed scheme is based on the sparse representation of
the components derived from the curvelet transform of the
original Electrocardiogram (ECG) signal. The ECG signals
may be approximated by a few coefficients that can be taken
from a wavelet basis. This fact allows a compressed sensing
approach for ECG signal compression to be introduced and to
be a domain of search. ECG signals illustrate redundancy
between adjacent heart beats. This redundancy implies a high
fraction of common support between consecutive heart beats.
The main contribution of this paper lies in the using of
curvelet transform in order to generate sparsity in ECG signal.
This transformation is considered an excellent approach as
illustrated in this paper. Simulation results represent a better
approach than Discrete Wavelet Transform (DWT) that is
based on compression of ECG. MIT-BIH database is used for
experimentation. The MIT-BIH database contains different
kinds of ECG signals that include both abnormal ECG and
normal ECG, which have different sampling rates. MATLAB
tool is used for simulation purpose. The novelty of the method
is that the Compression Ratio (CR) achieved by detail
coefficients is better. The performance measure of the
reconstructed signal is carried out by Percentage Root Mean
Difference (PRD). This paper also introduces the efficient
realization of the different transformation techniques using
FPGA. Thus the contribution of this paper lies into two main
parts. The first part is specialized in determining the proper
transformation that is used in the compression of ECG signals.
The second part of the contribution is summarized in using
suitable hardware to implement this design. Architecture can
be based on the ideas of parallelism and pipelining to get the
minimum throughput and speed. Architecture is cascade and
simple for calculating curvelet coefficients. The reduction of
the memory size can be done by splitting ROM table. The
description and functionalities of the design are modeled by
Verilog HDL. The simulation and synthesis methodology are
used on Virtex-II Pro FPGA that uses less number of
resources of the FPGA.
Description
Keywords
university of FPGA, Compression ratio, Heart beats, Sampling rates, Sparse, Compressive sensing