Abstract:
The electrocardiogram (ECG), one of the most vital medical signals, represents the
electrical activity of a heart. ECG is a well-established diagnostic tool for cardiac
abnormality. For the goal of efficient and convenient processing ofECG, automated and
computerized ECG processing has become a major topic of research in the field of
biomedical engineering. Modem clinical systems require the storage and transmission of
large amount of ECG signals. Efficient data compression is needed in order to reduce the
amount of data. In ECG signal compression algorithms, the aim is to reach maximum
compression ratio, while keeping the relevant diagnostic information in the
reconstructed signal.
Wavelets have recently emerged as powerful tool for signal compression. In this work,
an ECG compression algorithm is presented which is based on energy compaction
property of the wavelet coefficients. The wavelet transform decomposes the signal into
multi-resolution bands. The lowest resolution band (approximation band) is the smallest
band in size and it includes high amplitude approximation coefficients. The wavelet
coefficients other than these included by the approximation band, detail coefficients,
have small magnitudes. Most of the energy is captured by the approximation coefficients
of the lowest resolution band. In this work, we develop three threshold selection rules
based on the energy compaction property of the wavelet coefficients. All the rules are
applied to lead II of different records of MIT-BIH Arrhythmia Database. Among the
three rules, the best rule which offers high compression ratio (CR) with low percent root
mean square difference (PRD) than the other two rules is selected. A set of 30 records is
taken as test data from MIT-BIH Arrhythmia Database for testing the compression
algorithm by the best rule. A compression mtio of 15.12:1 is achieved with a very good
reconstructed signal quality (pRD=2.33%). The algorithm provides improved
performance in terms of computational efficiency and compression rate where the
clinically significant features in the reconstructed ECG signal are preserved. The
proposed method yields to good results in comparison with other wavelet transform
based compression methods described in the literature.
