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Journal ArticleDOI

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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Journal ArticleDOI
TL;DR: A novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph using the spectral decomposition of the discrete graph Laplacian L, based on defining scaling using the graph analogue of the Fourier domain.

1,681 citations

Posted Content
TL;DR: In this paper, the spectral graph wavelet operator is defined based on spectral decomposition of the discrete graph Laplacian, and a wavelet generating kernel and a scale parameter are used to localize this operator to an indicator function.
Abstract: We propose a novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph. Our approach is based on defining scaling using the the graph analogue of the Fourier domain, namely the spectral decomposition of the discrete graph Laplacian $\L$. Given a wavelet generating kernel $g$ and a scale parameter $t$, we define the scaled wavelet operator $T_g^t = g(t\L)$. The spectral graph wavelets are then formed by localizing this operator by applying it to an indicator function. Subject to an admissibility condition on $g$, this procedure defines an invertible transform. We explore the localization properties of the wavelets in the limit of fine scales. Additionally, we present a fast Chebyshev polynomial approximation algorithm for computing the transform that avoids the need for diagonalizing $\L$. We highlight potential applications of the transform through examples of wavelets on graphs corresponding to a variety of different problem domains.

1,119 citations

Journal ArticleDOI
TL;DR: This paper proposes to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud and presents a prototype of a Smart e-Health Gateway called UT-GATE.

867 citations

Journal ArticleDOI
TL;DR: This paper quantifies the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote and shows that CS represents a competitive alternative to state- of- the-art digital wavelet transform (DWT)-basedECG compression solutions in the context of WBSn-based ECG monitoring systems.
Abstract: Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.

680 citations

Journal ArticleDOI
TL;DR: This statement examines the relation of the resting ECG to its technology to establish standards that will improve the accuracy and usefulness of the ECG in practice and to recommend recommendations for ECG standards.

649 citations

References
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Proceedings ArticleDOI
20 Sep 1995
TL;DR: This paper proposes a new ECG signal compression algorithm using a discrete symmetric wavelet transform that may find applications in digital Holter recording, inECG signal archiving and in ECG data transmission through communication channels.
Abstract: This paper proposes a new ECG signal compression algorithm using a discrete symmetric wavelet transform. This proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels. Using the new method, a compression ratio of 8 to 1 can be achieved with PRD=3.9%, in contrast to the AZTEC compression ratio of 6.8 to 1 with PRD=10.0% and the fan algorithm compression ratio of 7;4 to 1 with PRD=8.1%.

106 citations


"Wavelet and wavelet packet compress..." refers background or methods in this paper

  • ...describe a wavelet ECG coder that dynamically allocates bits to the various scales of the wavelet transform in an attempt to obtain optimal compression in the ratedistortion sense [17]....

    [...]

  • ...Recently, wavelets have been applied to several problems in electrocardiology, including data compression [15]–[17], the analysis of ventricular late potentials [18], and the detection of ECG characteristic points [19], [20]....

    [...]

Journal ArticleDOI
TL;DR: A preliminary investigation into the wavelet transform application to the study of both ECG and heart rate variability data is described, suggesting that it is well suited to this task.

102 citations


"Wavelet and wavelet packet compress..." refers methods in this paper

  • ...Recently, wavelets have been applied to several problems in electrocardiology, including data compression [9] and the analysis of ventricular late potentials [18]....

    [...]

Journal ArticleDOI
TL;DR: A multilead electrocardiography (ECG) data compression method is presented, which applies a linear transform to the standard ECG lead signals and compressed using various coding methods, including multirate signal processing and transform domain coding techniques.
Abstract: A multilead electrocardiography (ECG) data compression method is presented. First, a linear transform is applied to the standard ECG lead signals, which are highly correlated with each other. In this way a set of uncorrelated transform domain signals is obtained. Then, the resulting transform domain signals are compressed using various coding methods, including multirate signal processing and transform domain coding techniques. >

77 citations


"Wavelet and wavelet packet compress..." refers methods in this paper

  • ...Most lossy ECG compression algorithms belong to either of the following categories: direct data compression methods [7]‐[9], which detect redundancies by direct analysis of actual signal samples or transform methods [ 5 ], [6], [10], which first transform the signal to some other time-frequency representation better suited for detecting and removing redundancies....

    [...]

Journal ArticleDOI
TL;DR: The author presents a new adaptive compression method for ECGs that achieves a high-quality approximation at less than 250 bits/s and the corresponding rates for other transform based schemes (the DCT and the DLT) are always higher.
Abstract: Presents a new adaptive compression method for ECGs. The method represents each R-R interval by an optimally time-warped polynomial. It achieves a high-quality approximation at less than 250 bits/s. The author shows that the corresponding rates for other transform based schemes (the DCT and the DLT) are always higher. Also, the new method is less sensitive to errors in QRS detection and it removes more (white) noise from the signal. The reconstruction errors are distributed more uniformly in the new scheme and the peak error is usually lower. The reconstruction method is also useful for adaptive filtering of noisy ECG signals. >

64 citations


"Wavelet and wavelet packet compress..." refers methods in this paper

  • ...Most lossy ECG compression algorithms belong to either of the following categories: direct data compression methods [7]–[9], which detect redundancies by direct analysis of actual signal samples or transform methods [5], [6], [10], which first transform the signal to some other time-frequency representation better suited for detecting and removing redundancies....

    [...]

  • ...An overview of ECG compression research before 1990 is presented in [1]; more recent work includes [2]–[6]....

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Journal ArticleDOI
TL;DR: The method is compared to the discrete cosine transform and is found to yield a significantly higher data compression for a given signal quality (quantified by mean squared error and peak error).
Abstract: A method for the compression of ECG (electrocardiogram) data is presented. The method is based on high-degree polynomial expansions. Data rules of about 350 b/s are achievable at an acceptable signal quality. The high compression is obtained by a carefully selected subdivision of the ECG signal into intervals that make optimal use of the special properties of the polynomial base functions. Each interval corresponds to one ECG period. The method is compared to the discrete cosine transform and is found to yield a significantly higher data compression for a given signal quality (quantified by mean squared error and peak error). >

63 citations


"Wavelet and wavelet packet compress..." refers methods in this paper

  • ...An overview of ECG compression research before 1990 is presented in [15]; more recent work includes [13], [22], [20], [3], and [21]....

    [...]