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.
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Dissertation•
15 Nov 2007
TL;DR: In this article, the authors compare polynome orthogonaux-based methods for the compression of signaux ECG with polynomes of Legendre et Tchebychev.
Abstract: La compression des signaux ECG trouve encore plus d'importance avec le developpement de la telemedecine. En effet, la compression permet de reduire considerablement les couts de la transmission des informations medicales a travers les canaux de telecommunication. Notre objectif dans ce travail de these est d'elaborer des nouvelles methodes de compression des signaux ECG a base des polynomes orthogonaux. Pour commencer, nous avons etudie les caracteristiques des signaux ECG, ainsi que differentes operations de traitements souvent appliquees a ce signal. Nous avons aussi decrit de facon exhaustive et comparative, les algorithmes existants de compression des signaux ECG, en insistant sur ceux a base des approximations et interpolations polynomiales. Nous avons aborde par la suite, les fondements theoriques des polynomes orthogonaux, en etudiant successivement leur nature mathematique, les nombreuses et interessantes proprietes qu'ils disposent et aussi les caracteristiques de quelques uns de ces polynomes. La modelisation polynomiale du signal ECG consiste d'abord a segmenter ce signal en cycles cardiaques apres detection des complexes QRS, ensuite, on devra decomposer dans des bases polynomiales, les fenetres de signaux obtenues apres la segmentation. Les coefficients produits par la decomposition sont utilises pour synthetiser les segments de signaux dans la phase de reconstruction. La compression revient a utiliser un petit nombre de coefficients pour representer un segment de signal constitue d'un grand nombre d'echantillons. Nos experimentations ont etabli que les polynomes de Laguerre et les polynomes d'Hermite ne conduisaient pas a une bonne reconstruction du signal ECG. Par contre, les polynomes de Legendre et les polynomes de Tchebychev ont donne des resultats interessants. En consequence, nous concevons notre premier algorithme de compression de l'ECG en utilisant les polynomes de Jacobi. Lorsqu'on optimise cet algorithme en supprimant les effets de bords, il devient universel et n'est plus dedie a la compression des seuls signaux ECG. Bien qu'individuellement, ni les polynomes de Laguerre, ni les fonctions d'Hermite ne permettent une bonne modelisation des segments du signal ECG, nous avons imagine l'association des deux systemes de fonctions pour representer un cycle cardiaque. Le segment de l'ECG correspondant a un cycle cardiaque est scinde en deux parties dans ce cas: la ligne isoelectrique qu'on decompose en series de polynomes de Laguerre et les ondes P-QRS-T modelisees par les fonctions d'Hermite. On obtient un second algorithme de compression des signaux ECG robuste et performant.
6 citations
TL;DR: The test results and performance indices have proved beyond doubt that the EBP-NN method is very efficient for the data compression and help in the management of ECG data in both offline and real-time applications.
Abstract: This paper deals with an efficient algorithm, which has been developed for Electrocardiogram (ECG) data compression using error back propagation neural networks (EBP-NN). Four EBP-NN have been trained to retrieve all the 12 standard leads of the ECG signal. The combination of leads and the network topologies have been finalized after an extensive study of correlation between the ECG leads using CSE database. Each network has a topology of N-4-4-N, where N represents the number of samples in one cycle in any particular lead. It has been observed that this method compresses the data as well as improves the quality of retrieved signal due to elimination of high frequency noise. The compression ratio (CR) in EBP-NN method goes on increasing with the increase in the number of ECG cycles. This method is best suited for data compression in Holter monitoring, ambulatory care and telemedicine. The performance of the algorithm has been evaluated by comparing the vital reference points like onsets, offsets, amplitud...
6 citations
Cites methods from "Wavelet and wavelet packet compress..."
...I • Some of the commonly used TC techmques are Karhunen-Loeve transform (KL T), Fourier Transform (FT), Cosine Transform (CT), Walsh transform (WT), Harr transform (HT), the optimally Warped Transform sub-band coding and the Wavelet Transform [15,16]....
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08 Sep 2005
TL;DR: In this paper it is shown that the compression ratio achieved is an improvement over those obtained by previously reported thresholding-based algorithms.
Abstract: A filter bank-based algorithm for ECG compression is developed in this paper. The proposed method utilises a nearly-perfect reconstruction cosine modulated filter bank to split the incoming signals into several subband signals that are then quantised through thresholding and Huffman encoded. The advantage of the proposed method is that the threshold is chosen so that the quality of the retrieved signal is guaranteed. In this paper it is shown that the compression ratio achieved is an improvement over those obtained by previously reported thresholding-based algorithms.
6 citations
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...Keywords: ECG compression, filter banks, subband coding....
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10 Sep 2008
TL;DR: An ECG compression algorithm based on a Discrete Wavelet lifting Transform (DWLT) and Multistage Vector Quantization (MSVQ) methods for a ubiquitous ECG monitoring system over the Zigbee Network is proposed.
Abstract: The use of a Zigbee network in various health monitoring systems has become well accepted. However, such a system has limited storage, power supply, bandwidth for communication, and processing speed when massive volumes of bio signals, especially ECG signals are involved. Various effective methods, including using low power hardware, have been proposed to maximize the limited resources. Data compression is one of the most efficient methods. In this paper, we propose an ECG compression algorithm based on a Discrete Wavelet lifting Transform (DWLT) and Multistage Vector Quantization (MSVQ) methods for a ubiquitous ECG monitoring system over the Zigbee Network. We performed an evaluation of the results of the proposed compression method through an ECG monitoring system and experimentation with a pervasive computing test bed.
6 citations
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...The International Conference on Mobile Technology, Applications & Systems 2008 (Mobility Conference), 10-12 September, 2008, Ilan, Taiwan....
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20 Nov 2014
TL;DR: An efficient technique to eliminate high frequency and power line interference noise from digitized Electrocardiogram (ECG) signal and compression of that enhanced signal in a lossless manner is proposed.
Abstract: This paper proposes an efficient technique to eliminate high frequency and power line interference noise from digitized Electrocardiogram (ECG) signal and compression of that enhanced signal in a lossless manner. ECG data of different diseases taken from PTB diagnostic ECG database (PTB-DB) is used to test the performance of the module. At first, contaminated ECG signal is passed through a Butterworth lowpass filter to remove high frequency noises whose order is chosen on experimental basis. To remove power line interference noise, an IIR notch filter is designed properly. Enhanced signal is compressed using a strict lossless technique. The proposed module can reduce noise as well as ECG data file size efficiently.
6 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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