• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.343-344
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• 2006

In this paper, the biomedical signal compression method with multi-stage vector quantization is proposed. It utilizes characteristic of wavelet coefficients in which the energy is concentrated on approximation coefficients. The transmitted codebook index consists code vectors obtained by wavelet coefficients of ECG and Error signals from the 1024 block length, respectively. The proposed compression method showed 2.1298% in average PRD and 1.8 kbits/sec in CDR.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.311-321
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• 2003

In an emergency telemedicine system such as the High-quality Multimedia based Real-time Emergency Telemedicine(HMRET) service, it is very important to examine the status of the patient continuously using the multimedia data including the biological signals(ECG, BP, Respiration, $SpO_2)$ of the patient. In order to transmit these data real time through the communication means which have the limited transmission capacity, it is also necessary to compress the biological data besides other multimedia data. For this purpose, we investigate and compare the ECG compression techniques in the time domain and in the wavelet transform domain, and present an effective lossless compression method of the biological signals using PEG Huffman table for an emergency telemedicine system. And, for the HMRET service, we developed the lossless compression and reconstruction program or the biological signals in MSVC++ 6.0 using DPCM method and JPEG Huffman table, and tested in an internet environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.625-629
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• 2005

According to the rapid development of the information and communication technology, the demand on the efficient compression technology for the multimedia data is increased magnificently. In this Paper, we designed new compression algorithm structure using wavelet base for the compression of ECG signal and audible signal data. We examined the efficiency of the compression between 2-band structure and wavelet packet structure, and investigated the efficiency and reconstruction error by wavelet base function using Daubechies wavelet coefficient and Coiflet coefficient for each structure. Finally, data were compressed further more using Huffman code, and resultant Compression Rate(CR) and Percent Root Mean Square difference(PRD) were compared with those of existent DCT.