Wavelet and wavelet packet compression of electrocardiograms
01 May 1997-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 44, Iss: 5, pp 394-402
TL;DR: Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECGs are clinically useful.
Abstract: Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.
Citations
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01 Dec 2004
TL;DR: Compared to existing 2-D ECG compression methods, the proposed algorithm is unique in that it reveals much more intra- and inter-beat correlation characteristic of ECG signals and can achieve enhanced performance.
Abstract: Electrocardiogram (ECG) signals have both intra- and inter-beat correlations, which can be exploited for compression by arranging the ECG signals into appropriate two-dimensional (2-D) representations In this paper, we propose a novel approach that maps 1-D ECG signals to 2-D arrays effectively and then compresses the 2-D arrays with efficient image compression algorithms Compared to existing 2-D ECG compression methods, the proposed algorithm is unique in that it reveals much more intra- and inter-beat correlation characteristic of ECG signals Therefore, the image compression algorithms can achieve enhanced performance Furthermore, unlike existing 2-D ECG compression methods, the proposed algorithm works well for both regular and irregular ECG signals with extremely varying periods In particular, its performance is insensitive to QRS miss detection cases
7 citations
11 Apr 2014
TL;DR: This paper presents a novel approach for electrocardiogram (ECG) data compression in a healthcare monitoring system, which helps to reduce power consumption during wireless communication and results in the best quality and accuracy in terms of compression ratio and error rate.
Abstract: This paper presents a novel approach for electrocardiogram (ECG) data compression in a healthcare monitoring system, which helps to reduce power consumption during wireless communication. The proposed ECG data compression approach consists of multilevel vector (MLV) compression, integer-linear-programming (ILP)-based compression, and Huffman coding. The MLV compression provides different compression levels for different parts of ECG signal. The ILP-based compression achieves even higher compression ratio while satisfying tolerable error rate. The Huffman coding encodes compressed ECG data without data loss. Experimental results based on the MIT-BIH arrhythmia database show that our approach result in the best quality and accuracy in terms of compression ratio and error rate compared with the previous works.
7 citations
Cites background or methods from "Wavelet and wavelet packet compress..."
...Table II further compares the average values of CR, PRD, and QS resulting from our approach and all the other approaches proposed during the passed years, which are based on DWT [6], [7], [8], [9], [10], DFT [11], skeleton...
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...discrete cosine transform (DCT) [5], wavelet-based transform (DWT) [6], [7], [8], [9], [10], and discrete fourier transform (DFT) [11]....
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TL;DR: It is shown that the incorporation of this connectedness as additional prior information into a modified version of the CoSaMP algorithm can significantly reduce the required number of samples to achieve good quality in the reconstruction of ECG signal reconstruction.
Abstract: Compressive sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist sampling of sparse signals. Extensive previous work has exploited the sparse representation of ECG signals in compression applications. In this paper, we propose the use of wavelet domain dependencies to further reduce the number of samples in compressive sensing-based ECG compression while decreasing the computational complexity. R wave events manifest themselves as chains of large coefficients propagating across scales to form a connected subtree of the wavelet coefficient tree. We show that the incorporation of this connectedness as additional prior information into a modified version of the CoSaMP algorithm can significantly reduce the required number of samples to achieve good quality in the reconstruction. This approach also allows more control over the ECG signal reconstruction, in particular, the QRS complex, which is typically distorted when prior information is not included in the recovery. The compression algorithm was tested upon records selected from the MIT-BIH arrhythmia database. Simulation results show that the proposed algorithm leads to high compression ratios associated with low distortion levels relative to state-of-the-art compression algorithms. © (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
7 citations
29 Nov 2009
TL;DR: The technique helps to show that the decomposition of Modified Lifting Scheme (MLS) to the order N can solve the recurring problem of storage and/or duration of transmission with more efficiency as compared to wavelet packet transform and lifting scheme.
Abstract: This article discusses issues on data compression of electromyographic (EMG) signals A new coding scheme was proposed in our previous work [19], and a summary of the first experimentations had been given The new aspect presented in this paper is a deepening of this method followed by a N-order general decomposition scheme From this generalization, a more complete characterization of the method is made Tests for order N equal to 1, 2 and 3 carried out showed some improvements compared to [19] The technique helps to show that the decomposition of Modified Lifting Scheme (MLS) to the order N can solve the recurring problem of storage and/or duration of transmission with more efficiency as compared to wavelet packet transform and lifting scheme Each of the methods is shortly presented, along with results obtained from actual signals Signal to noise ratio, compression ratio, distortion of the mean frequency, entropy and computation load allow objective comparisons
7 citations
Cites background from "Wavelet and wavelet packet compress..."
...The coding is the final step in the chain of compression....
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01 Aug 2017
TL;DR: A new approach to remove noise present in ECG signal is proposed by using computationally efficient linear phase filter ie.
Abstract: In this paper, a new approach to remove noise present in ECG signal is proposed. Baseline wander and high frequency noise is eliminated by using computationally efficient linear phase filter ie. interpolated finite impulse response (IFIR) filter. The IFIR filter is designed by using Kaiser window function to achieve high stop band attenuation. As compared to other methods, the technique presented could achieve a reduction in computational complexity by 80.14 percent. Data compression is also performed in this study using wavelet packet decomposition along with Run-length encoding. Run-length encoding is used to improve the compression performance. For evaluation of the performance of IFIR filter, computational cost reduction (CRC) parameter is used, which directly depended on multipliers and adders. Different fidelity factors are considered to evaluate the performance of the proposed data compression method, viz., compression ratio (CR), signal to noise ratio (SNR), retained energy (RE) and percent root mean square difference (PRD), their magnitude being 25.13, 38.93, 99.10 and 1.75, respectively. MIT-BIH arrhythmia database has been utilized to judge the entire set of computations mentioned above noise removal and ECG signal compression. This work also includes beat detection of original and reconstructed signals. Simulated results show that decompressed signal is a replica of the input signal.
7 citations
References
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TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >
20,028 citations
Book•
01 May 1992TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
16,073 citations
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
14,157 citations
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Abstract: We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.
8,588 citations
"Wavelet and wavelet packet compress..." refers methods in this paper
...In the work described in this paper, was chosen to be Daubechie's W6 wavelet [10], which is illustrated in Figure 1....
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TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.
Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.
5,890 citations
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...algorithm was inspired by that in [28]....
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