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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 Article
TL;DR: In this paper, a wavelet packet transform (DWPT) architecture based on a folded distributed arithmetic implementation is proposed, which allows different wavelet coefficients with variable bit precision (data input and output size, and coefficient length) by combining different blocks in cascade.
Abstract: The present paper describes a fully parameterized Discrete Wavelet Packet Transform (DWPT) architecture based on a folded Distributed Arithmetic implementation, which makes possible to design any kind of wavelet bases The proposed parameterized architecture allows different CDF wavelet coefficient with variable bit precision (data input and output size, and coefficient length) Moreover, by combining different blocks in cascade, we can expand as many complete stages (wavelet packet levels) as we require Our architecture need only two FIR filters to calculate various wavelet stages simultaneously, and specific VIRTEX family resources (SRL16E) have been instantiated to reduce area and increase frequency operation Finally, a DWPT implementation for CDF(9,7) wavelet coefficients is synthesized on VIRTEX-II 3000-6 FPGA for different precisions

1 citations

Journal ArticleDOI
TL;DR: It is shown that variational autoencoders are a good option for reducing the dimension of high dimensional data like ECG and the VAE was robust to both tests.
Abstract: In this work, we explore dimensionality reduction techniques for univariate and multivariate time series data. We especially conduct a comparison between wavelet decomposition and convolutional variational autoencoders for dimension reduction. We show that variational autoencoders are a good option for reducing the dimension of high dimensional data like ECG. We make these comparisons on a real world, publicly available, ECG dataset that has lots of variability and use the reconstruction error as the metric. We then explore the robustness of these models with noisy data whether for training or inference. These tests are intended to reflect the problems that exist in real-world time series data and the VAE was robust to both tests.

1 citations

Proceedings ArticleDOI
14 Jun 2016
TL;DR: This work presents a compressed matched subspace detection algorithm to detect fiducial points of ECG waveform from streaming random projections of the data and provides a theoretical analysis to compare the performance of the compressed matched detector performance to that of a matched detector operating with uncompressed data.
Abstract: Wireless biosensors enable continuous monitoring of physiology and can provide early signs of imminent problems allowing for quick intervention and improved outcomes. Wireless communication of the sensor data for remote storage and analysis dominates the device power budget and puts severe constraints on lifetime and size of these sensors. Traditionally, to minimize the wireless communication bandwidth, data compression at the sensor node and signal reconstruction at the remote terminal is utilized. Here we consider an alternative strategy of feature detection with compressed samples without the intermediate step of signal reconstruction. Specifically, we present a compressed matched subspace detection algorithm to detect fiducial points of ECG waveform from streaming random projections of the data. We provide a theoretical analysis to compare the performance of the compressed matched detector performance to that of a matched detector operating with uncompressed data. We present extensive experimental results with ECG data collected in the field illustrating that the proposed system can provide high quality heart rate variability indices and achieve an order of magnitude better RMSE in beat-to-beat heart rate estimation than the traditional filter/downsample solutions at low data rates.

1 citations


Cites methods from "Wavelet and wavelet packet compress..."

  • ...Classical ECG compression methods [1, 2, 3] transform the ECG time series data using a suitable bases and threshold the resulting coefficients to find a sparse approximation to the ECG signal in the transform domain....

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Proceedings ArticleDOI
16 Aug 2005
TL;DR: Embedded lossless Wavelet based coder with hybrid bit scanning is used for ECG signal coding and Experimental results show that this algorithm outperforms than other coders such as Djohn, EZW, SPIHT, LJPEG etc in terms of coding efficiency.
Abstract: Wavelets have emerged as powerful tools for signal coding. In this paper embedded lossless Wavelet based coder with hybrid bit scanning is used for ECG signal coding. Experimental results show that this algorithm outperforms than other coders such as Djohn, EZW, SPIHT, LJPEG etc exits in the literature in terms of coding efficiency by successive partitioned the wavelet coefficients in the space frequency domain and sent them using hybrid bit scanning. Since no zero tree exists this coder is significantly more efficient in compression, simple in implementation and in computation than the previously proposed coders. This algorithm is tested for thirty seven different records from MIT-BIH arrhythmia database and obtained an average percent root mean square difference as around 0.0502% to 3.5399% for an average compression ratio of 1.5:1 to 25.1429:1 and for an average bit rate of 2552.7bps to 254.5 bps. A compression ratio of 8.0688:1 is achieved for MIT-BIH arrhythmia database record 117 with a percent mean square difference as 0.5183% and bit rate as 909.8 bps using Bior6.8 wavelet. All clinical information is preserved after compression and this makes the algorithm an attractive choice for use in portable heart monitoring systems.

1 citations

Proceedings ArticleDOI
30 Oct 2009
TL;DR: This paper proposes a novel wavelet-based approach to implementing of a locally progressive digital signal coding idea that explores both the newly developed fast procedures for the evaluation of the discrete wavelet transform for signal blocks and some efficient wave let-based signal encoders.
Abstract: The main goal of progressive encoding and transmission of digital signals is to allow the user to identify relevant features in a signal as quickly as possible at minimum cost. Locally progressive encoding and transmission can be achieved by first transmitting a low resolution approximation (“rough” estimate) of the original signal, then sending further details related to one or another selected (mostly, at the user's request) block of the signal. In this paper, we propose a novel wavelet-based approach to implementing of a locally progressive digital signal coding idea. The proposed approach explores both the newly developed fast procedures for the evaluation of the discrete wavelet transform for signal blocks and some efficient wavelet-based signal encoders.

1 citations


Cites background from "Wavelet and wavelet packet compress..."

  • ...Introduction Over the past 10-15 years the discrete wavelet transform (DWT) has gained widespread acceptance in signal processing in general and in image compression in particular, [1-5]....

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References
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Journal ArticleDOI
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 1992
TL;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

Journal ArticleDOI
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

Journal ArticleDOI
Ingrid Daubechies1
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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Journal ArticleDOI
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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