Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples
Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples作者机构：Electrical and Electronics Engineering Department Faculty of Engineering Assiut University Assiut Egypt
出 版 物：《International Journal of Communications, Network and System Sciences》 (通讯、网络与系统学国际期刊（英文）)
年 卷 期：2014年第7卷第1期
页 面：53-70页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：ECG Signal Segmentation Lossless and Lossy Compression Techniques Discrete Wavelet Transform Energy Packing Efficiency Run-Length Coding
摘 要：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 da
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