TITLE:
Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples
AUTHORS:
Mohammed M. Abo-Zahhad, Tarik K. Abdel-Hamid, Abdelfatah M. Mohamed
KEYWORDS:
ECG Signal Segmentation; Lossless and Lossy Compression Techniques; Discrete Wavelet Transform; Energy Packing Efficiency; Run-Length Coding
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.7 No.1,
January
24,
2014
ABSTRACT: This paper presents a hybrid technique for the
compression of ECG signals based on DWT and exploiting the correlation between
signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding
techniques for the compression of different parts of the signal; where lossless
compression is adopted in clinically relevant parts and lossy compression is
used in those parts that are not clinically relevant. The proposed compression
algorithm begins by segmenting the ECG signal into its main components
(P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The
resulting waves are grouped into Region of Interest (RoI) and Non Region of
Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes
are applied to the RoI and NonRoI parts respectively. Ideally we would like to
compress the signal losslessly, but in many applications this is not an option.
Thus, given a fixed bit budget, it makes sense to spend more bits to represent
those parts of the signal that belong to a specific RoI and, thus, reconstruct
them with higher fidelity, while allowing other parts to suffer larger
distortion. For this purpose, the correlation between the successive samples
of the RoI part is utilized by adopting DPCM approach. However the NonRoI part
is compressed using DWT, thresholding and coding techniques. The wavelet
transformation is used for concentrating the signal energy into a small number
of transform coefficients. Compression is then achieved by selecting a subset
of the most relevant coefficients which afterwards are efficiently coded.
Illustrative examples are given to demonstrate thresholding based on energy
packing efficiency strategy, coding of DWT coefficients and data packetizing. The
performance of the proposed algorithm is tested in terms of the compression
ratio and the PRD distortion metrics for the compression of 10 seconds of data
extracted from records 100 and 117 of MIT-BIH database. The obtained results
revealed that the proposed technique possesses higher compression ratios and
lower PRD compared to the other wavelet transformation techniques. The
principal advantages of the proposed approach are: 1) the deployment of
different compression schemes to compress different ECG parts to reduce the
correlation between consecutive signal samples; and 2) getting high compression
ratios with acceptable reconstruction signal quality compared to the recently
published results.