The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Amos Katz - One of the best experts on this subject based on the ideXlab platform.
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the weighted diagnostic distortion wdd measure for ecg Signal Compression
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: Yaniv Zigel, Arnon D Cohen, Amos KatzAbstract:In this paper, a new distortion measure for electrocardiogram (ECG) Signal Compression, called weighted diagnostic distortion (WDD) is introduced. The WDD measure is designed for comparing the distortion between original ECG Signal and reconstructed ECG Signal (after Compression). The WDD is based on PQRST complex diagnostic features (such as P wave duration, QT interval, T shape, ST elevation) of the original ECG Signal and the reconstructed one. Unlike other conventional distortion measures [e.g. percentage root mean square (rms) difference, or PRD], the WDD contains direct diagnostic information and thus is more meaningful and useful. Four Compression algorithms were implemented (AZTEC, SAPA2, LTP, ASEC) in order to evaluate the WDD. A mean opinion score (MOS) test was applied to test the quality of the reconstructed Signals and to compare the quality measure (MOS/sub error/) with the proposed WDD measure and the popular PRD measure. The evaluators in the WIGS test were three independent expert cardiologists, who studied the reconstructed ECG Signals in a blind and a semiblind tests. The correlation between the proposed WDD measure and the MOS test measure (MOS/sub error/) was found superior to the correlation between the popular PRD measure and the MOS/sub error/.
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ecg Signal Compression using analysis by synthesis coding
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: Yaniv Zigel, Arnon D Cohen, Amos KatzAbstract:An electrocardiogram (ECG) Compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The Compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The Compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The Compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean Compression rate of approximately 100 bits/s (Compression ratio of about 30:1) has been achieved with a good reconstructed Signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG Compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As In the quantitative test, the proposed Compression algorithm was found to be superior to the other tested Compression algorithms.
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Analysis by synthesis ECG Signal Compression
Computers in Cardiology 1997, 1997Co-Authors: Yaniv Zigel, A. Cohen, A. Wagshal, Amos KatzAbstract:The authors introduce a new ECG Compression algorithm, and a new distortion measure. The distortion measure, called the Weighted Diagnostic Distortion (WDD), is based on comparing PQRST complex features (such as: RR interval, QT interval, ST elevation, etc.) of the original ECG Signal and the reconstructed one. The Compression algorithm is based on analysis by synthesis coding. It consists of a beat codebook, long and short term predictors, and an adaptive residual quantizer. The Compression algorithm uses the WDD measure in order to encode, by means of analysis by synthesis, every beat of the original ECG Signal. The Compression algorithm has been applied to the MIT-BIH Arrhythmia Database. A rate of approximately 100 bits per second has been achieved with a very good quality (WDD below 4%, and PRD below 8%).
Cormac Herley - One of the best experts on this subject based on the ideXlab platform.
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wavelets subband coding and best bases
Proceedings of the IEEE, 1996Co-Authors: Kannan Ramchandran, Martin Vetterli, Cormac HerleyAbstract:The emergence of wavelets has led to a convergence of linear expansion methods used in Signal processing and applied mathematics. In particular, subband coding methods and their associated filters are closely related to wavelet constructions. We first review such constructions with a Signal processing perspective. We then discuss the idea behind Signal adapted bases and associated algorithms before showing how wavelets and subband coding methods are used in Signal Compression applications.
Soura Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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Signal Compression by subband coding
Automatica, 1999Co-Authors: Bruce A. Francis, Soura DasguptaAbstract:This is a survey/tutorial paper on data Compression using the technique of subband coding. This is widely used in practice, for example, in the MPEG audio coder. A subband coder has two main components: a filter bank that decomposes the source into components, usually with respect to defined frequency bands; and a bank of quantizers. Without the quantizers, the subband coder-decoder is a linear periodically time-varying discrete-time system, and hence falls into the class of multirate digital Signal processing systems. The paper reviews the theory of such systems, studies the perfect reconstruction problem and a variant of it, reviews subband coding theory for a stationary input Signal, and describes recent work for cyclostationary Signals.
Yaniv Zigel - One of the best experts on this subject based on the ideXlab platform.
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the weighted diagnostic distortion wdd measure for ecg Signal Compression
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: Yaniv Zigel, Arnon D Cohen, Amos KatzAbstract:In this paper, a new distortion measure for electrocardiogram (ECG) Signal Compression, called weighted diagnostic distortion (WDD) is introduced. The WDD measure is designed for comparing the distortion between original ECG Signal and reconstructed ECG Signal (after Compression). The WDD is based on PQRST complex diagnostic features (such as P wave duration, QT interval, T shape, ST elevation) of the original ECG Signal and the reconstructed one. Unlike other conventional distortion measures [e.g. percentage root mean square (rms) difference, or PRD], the WDD contains direct diagnostic information and thus is more meaningful and useful. Four Compression algorithms were implemented (AZTEC, SAPA2, LTP, ASEC) in order to evaluate the WDD. A mean opinion score (MOS) test was applied to test the quality of the reconstructed Signals and to compare the quality measure (MOS/sub error/) with the proposed WDD measure and the popular PRD measure. The evaluators in the WIGS test were three independent expert cardiologists, who studied the reconstructed ECG Signals in a blind and a semiblind tests. The correlation between the proposed WDD measure and the MOS test measure (MOS/sub error/) was found superior to the correlation between the popular PRD measure and the MOS/sub error/.
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ecg Signal Compression using analysis by synthesis coding
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: Yaniv Zigel, Arnon D Cohen, Amos KatzAbstract:An electrocardiogram (ECG) Compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The Compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The Compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The Compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean Compression rate of approximately 100 bits/s (Compression ratio of about 30:1) has been achieved with a good reconstructed Signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG Compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As In the quantitative test, the proposed Compression algorithm was found to be superior to the other tested Compression algorithms.
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Analysis by synthesis ECG Signal Compression
Computers in Cardiology 1997, 1997Co-Authors: Yaniv Zigel, A. Cohen, A. Wagshal, Amos KatzAbstract:The authors introduce a new ECG Compression algorithm, and a new distortion measure. The distortion measure, called the Weighted Diagnostic Distortion (WDD), is based on comparing PQRST complex features (such as: RR interval, QT interval, ST elevation, etc.) of the original ECG Signal and the reconstructed one. The Compression algorithm is based on analysis by synthesis coding. It consists of a beat codebook, long and short term predictors, and an adaptive residual quantizer. The Compression algorithm uses the WDD measure in order to encode, by means of analysis by synthesis, every beat of the original ECG Signal. The Compression algorithm has been applied to the MIT-BIH Arrhythmia Database. A rate of approximately 100 bits per second has been achieved with a very good quality (WDD below 4%, and PRD below 8%).
Kannan Ramchandran - One of the best experts on this subject based on the ideXlab platform.
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wavelets subband coding and best bases
Proceedings of the IEEE, 1996Co-Authors: Kannan Ramchandran, Martin Vetterli, Cormac HerleyAbstract:The emergence of wavelets has led to a convergence of linear expansion methods used in Signal processing and applied mathematics. In particular, subband coding methods and their associated filters are closely related to wavelet constructions. We first review such constructions with a Signal processing perspective. We then discuss the idea behind Signal adapted bases and associated algorithms before showing how wavelets and subband coding methods are used in Signal Compression applications.
