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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TL;DR: Simulation results on several records from the MIT-BIH arrhythmia database show that the proposed coding algorithm outperforms some recently developed ECG compression algorithms.
49 citations
Cites methods from "Wavelet and wavelet packet compress..."
...avelet transform [1,7–9]....
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...In [1,7], both the mbedded-zerotree-wavelet (EZW) and the set-partitioningn-hierarchical-tree (SPIHT) algorithm, which have shown ery good results in image coding, were applied to ECG ignals....
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...[7] Hilton ML....
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TL;DR: The data reconstruction algorithm has been developed using the reversed logic and it is seen that data is reconstructed preserving the significant ECG signal morphology.
49 citations
TL;DR: A hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression that is significantly more efficient in compression and may find applications in digital Holter recording, in ECG signal archiving and inECG data transmission through communication channels.
48 citations
Cites methods from "Wavelet and wavelet packet compress..."
...Four known wavelet transforms are employed for this purpose [13]....
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Journal Article•
20 Feb 2007-World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering
TL;DR: In this paper, a wavelet transform with a modified version of the set partitioning in hierarchical trees (SPIHT) coding algorithm was used for ECG data compression and the results showed the high efficiency of this method in ECG compression.
Abstract: — In this paper we present a novel approach for waveletcompression of electrocardiogram (ECG) signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. SPIHT algorithm has achieved prominent success in image compression. Here we use a modified version of SPIHT for one dimensional signals. We applied wavelet transform with SPIHT coding algorithmon different records of MIT-BIH database. The results show the high efficiency of this method in ECG compression. Keywords — ECG compression, wavelet, SPIHT. I. I NTRODUCTION LECTROCARDIOGRAM (ECG) signal is a very usefulsource of information for physicians in diagnosing heartabnormalities. With the increasing use of ECG in heart diagnosis, such as 24 hour monitoring or in ambulatorymonitoring systems, the volume of ECG data that should be stored or transmitted, has greatly increased. For example, a 3channel, 24 hour ambulatory ECG, typically has storagerequirement of over 50 MB. Therefore we need to reduce thedata volume to decrease storage cost or make ECG signalsuitable and ready for transmission through commoncommunication channels such as phone line or mobilechannel. So, we need an effective data compression method.The main goal of any compression technique is to achievemaximum data reduction while preserving the significantsignal morphology features upon reconstruction. Datacompression methods have been mainly divided into twomajor categories: 1) direct methods, in which actual signalsamples are analyzed (time domain), 2) transformationalmethods, in which first apply a transform to the signal and dospectral and energy distribution analysis of signals.Examples of direct methods are: differential pulse codemodulation (DPCM), amplitude zone time epoch coding (A TEC), turning point, coordinate reduction time encoding system (CORTES), Fan algorithm, ASEC. Reference [1] is agood review of some direct compression methods used inECG compression.
48 citations
15 Sep 1999
TL;DR: In this article, the wavelet transform was used to detect the two components, the aortic valve component A2 and pulmonary valve component P2, of the second sound S2 in normal PCG signal.
Abstract: Presents the application of the wavelet transform analysis method to the phonocardiogram (PCG) signal. Heart sound is a highly nonstationary signal. So in the analysis of heart sound it is important to study the frequency and time information. To investigate the exact features of heart sound we adopt Short-Time Fourier Transform (STFT) and Wavelet Transform (WT) as a time-frequency representation. As a result, it is found that the first sound in the PCG signal and the two components of the second sound are inaccurately detected. On the other hand, it is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second sound S2 in a normal PCG signal. Furthermore, the wavelet transform provides more features and characteristics of the PCG signals that will help the physician to obtain qualitative and quantitative measurements of the heart sound.
47 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
Additional excerpts
...algorithm was inspired by that in [28]....
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