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•
01 Jan 2013-International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy
TL;DR: A novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decomposition was proposed and the mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded Zero tree algorithm.
Abstract: Biomedical signal compression has been increasing interest due to the need of storing or transmitting large amount of multichannel data. Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. A novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decomposition was proposed. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded Zero tree algorithm. The internal optimization was the best basis selection that was performed for a given mother wavelet. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and random selection of the mother wavelet.
2 citations
01 Jan 2014
TL;DR: The results prove the suitability of CS as an ultra-low power compression technique for limited resource WBSNs and show that, indeed CS when implemented as a digital compression technique could outperform state-of-the-art ECG compression in terms of overall energy consumption.
Abstract: Our modern society is today threatened by an incipient healthcare delivery crisis caused by the current demographic and lifestyle trends. Current traditional healthcare infrastructures are increasingly overwhelmed by the escalating levels of supervision and medical management required by the increasingly prevalent aging-related and lifestyle-induced disorders, while healthcare costs are skyrocketing. Consequently, there is a consensus around the need for next-generation advanced citizen-centric eHealth delivery solutions. Wearable personal health systems based on wireless body sensors or more generally wireless body sensor networks (WBSN)for continuous monitoring and care are widely recognized to be crucial ICT tools to cost-effectively achieve such eHealth delivery solutions. More specifically, WBSN-enabled eHealth solutions consist in outfitting patients with wearable, miniaturized and wireless sensors able to measure, pre-process and wirelessly report various physiological, metabolic and kinematic biosignals to tele-health providers, enabling the required personalized, long-term and real-time remote monitoring of chronic patients, its seamless integration with the patient’s medical record and its coordination with nursing/medical support. However, state-of-the-art WBSN-enabled biosignal monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be improved through embedded biosignal compression, in order to reduce airtime over energy-hungry wireless links. Within this thesis, we present novel and promising approaches to tackle the challenge of ultra-low-power biosignal compression on resource-constrained wireless body sensor nodes. Within this thesis, we quantify the potential of the emerging compressed sensing (CS) paradigm for low-complexity and energy-efficient Electrocardiogram (ECG) sensing and data compression for storage or transmission, considering both software and hardware aspects. We have focused in ECG, because it is a key biosignal in all WBSN designs. This thesis is the first work to present and fully investigate the potential of CS as an ultra-low power sensing/compression technique for ECG signals. Our results prove the suitability of CS as an ultra-low power compression technique for limited resource WBSNs. Our results show that, indeed CS when implemented as a digital compression technique could outperform state-of-the-art ECG compression in terms of overall energy consumption. The need for fast and robust reconstruction algorithms inspired us to develop new modelbased reconstruction technique to fully leverage the prior information (beyond simple sparsity) from the underlying signal, improving the compression results for both single lead and joint multi-lead ECG compression. Inspired by the promise of the CS to merge
2 citations
Cites methods from "Wavelet and wavelet packet compress..."
...Interestingly, these algorithms are all based on the digital wavelet transform (DWT), namely, the embedded zerotree wavelet (EZW) [47], the set partitioning in hierarchical trees (SPIHT) [48] and thresholding-based algorithms [49, 50]....
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...Furthermore, the latter thresholding-based algorithm [50], where a fixed percentage of wavelet coefficients are zeroed, was shown to outperform EZW and SPIHT at a lower computational cost....
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01 Sep 2016
TL;DR: The proposed frequency synthesizer was fabricated in a TSMC 0.18 μm CMOS process for wearable RFID and biomedical applications such as integrated sensor at proposed ECG monitoring shirt with P-FCB electrodes.
Abstract: The proposed frequency synthesizer was fabricated in a TSMC 0.18 µm CMOS process for wearable RFID and biomedical applications such as integrated sensor at proposed ECG monitoring shirt with P-FCB electrodes. At 1 V supply voltage, measured results of the proposed prototype shows a wide tuning range from 2.23 to 2.47 GHz, corresponding to 10.2%, a phase noise of −111.16 dBc/Hz at 1 MHz offset from 2.4 GHz, a power consumption of 4.4 mW and a reference spur of −60.4 dBc.
2 citations
Cites methods from "Wavelet and wavelet packet compress..."
...The transformation method includes discrete cosine transform (DCT) [29], wavelet-based transform (DWT) [30], [31], [32], [33], [34], and discrete fourier transform (DFT) [35]....
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TL;DR: A modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression, that retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.
Abstract: This paper presents a modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression. Two more steps in the existing technique have been added to achieve higher compression ratio (CR) and lower percentage rms difference (PRD). The method has been tested on selected records from the MIT-BIH arrhythmia database. Even with two more steps, the method retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.
2 citations
Cites background or methods from "Wavelet and wavelet packet compress..."
...[20] using multi-rate signal processing and transform domain coding techniques; Hilton [12] using wavelet and wavelet packets; Djohan et al....
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...Table 5 shows comparative PRD of MIT-BIH patient 117 for CR of 8, as reported by Hilton [12], Djohan et al....
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...The impulse responses of the decomposition and synthesis QMF pairs are related by [12]:...
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...Table 5 shows comparative PRD of MIT-BIH patient 117 for CR of 8, as reported by Hilton [12], Djohan et al. [11], and Lu et al. [2]....
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...Among all filters tested in [12], biorthogonal 9/7 tap filters had the best compression performance for wavelet coding of ECG signals....
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01 Jan 2013
TL;DR: The method is based on a quadtree partitioning, increases compression ratio while minimizing the PRD (Percent Root Square Difference), and Huffman coding is applied at the last stage of compression.
Abstract: Flow increasingly important of data in hospitals, medical imaging or outpatient now requires mandatory use software compression of signals and images, backup or for their transmission. In this paper, we are trying to find a solution to this problem by compression of EMG signals by 2D fractals. For this purpose, EMG signals are processed in dimension 2. Matrix of size MxN obtained is reorganized according to the correlation between the pixels and adds new. Then fractal processing by combined methods of Fisher and Jacquin are used for decorrelation. Finally, to increase compression ratio, Huffman coding is applied at the last stage of compression. With this method, we show that fractals can be used to compress EMG signals in order to solve the problem of storage and transmission of EMG signals using techniques dedicated to image compression. Our approach permits us to introduce new encoding parameters which are: 2D cutouts of EMG signal into blocks of pixels. The compression method of EMG signals by 2D fractal is based on a quadtree partitioning, increases compression ratio while minimizing the PRD (Percent Root Square Difference). The results are satisfactory and promising.
2 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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