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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01 Jun 2014
TL;DR: In this article, a method was proposed to reconstruct the carotid pressure waveform from the radial PSW, which can be more conveniently monitored in comparison with the Carotid PSW.
Abstract: Carotid pressure waveform is often used to substitute the central aortic pressure waveform, which conveys lots of information regarding cardiovascular system. Thus, a method was proposed to reconstruct the carotid pressure waveform from the radial pressure waveform which can be more conveniently monitored in comparison with the carotid pressure waveform. The reconstruction method was using Finite Impulse Response (FIR) model to calculate mathematical transfer function (TF) between the radial pressure waveform and the carotid pressure waveform. Pulse pressure waveforms of 5 subjects were recorded to test the performance of the TF. Except for some details, the reconstructed carotid pressure waveform is acceptable with the best percent root-mean-square difference (PRD) of 13.60%.
Cites background from "Wavelet and wavelet packet compress..."
...The two parameters can be formulated as [10]...
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Proceedings Article•
24 Jun 2012
TL;DR: In order to prove that the improved RF algorithm had higher accuracy, the comparing analysis was conducted adapting ECG data and the modified Random Forest classifier showed increased more accuracy than SVM, MLP and other researchers' results.
Abstract: In order to prove that the improved RF algorithm had higher accuracy, the comparing analysis was conducted adapting ECG data. In pre-processing stage, Band-pass Filter was adapted among Wavelet transform, Median Filter, Finite impulse response and others. As a result, the modified Random Forest classifier showed increased more accuracy than SVM, MLP and other researchers' results. Thus, continuous studies on the selection of the filters and methods, which can efficiently delete baseline-wandering at pre-processing phase and accurately extract R-R interval, should be taken place.
Cites background or methods from "Wavelet and wavelet packet compress..."
...Chazal experimented with median filter [5], Michael tested with FIR (finite impulse response) [6], and Martinez tested with wavelet transform [7], however, we experimented with the band-pass filter like Markovsky [8], Taouli [9], and Gholam-Hosseini [10], the band pass filter was judged to be superior to the others and efficient to distinguish the wavelets of ECG by separating whether narrow or wide...
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...In the case of filters, there were Chazal’s [5], Michael’s [6], Martinez’s [7] works, and so on....
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01 Jan 2010
TL;DR: It is argued that BPM and PI practices facilitate maintenance of ERP systems, and links between maintenance of the ERP system and BPM should be improved.
Abstract: Enterprise Resource Planning (ERP) systems have reached a high level of maturity, and maintenance and improvement of implemented systems is becoming increasingly important. This paper proposes to analyze the ERP maintenance problem along three interrelated dimensions: software maintenance (SM), business process management (BPM) and process improvement (PI). It is argued that BPM and PI practices facilitate maintenance of ERP systems. The proposed analysis framework is applied to study maintenance of the ERP system at a large energy company. The analysis suggests that relatively high level of maturity has been achieved in corrective and adaptive maintenance. However, the level of maturity is lower in perfective and preventive maintenance, and links between maintenance of the ERP system and BPM should be improved.
01 Jan 2001
TL;DR: In this paper, a procedimiento de compresion sin perdidas of electrocardiografia ambulatoria digital is presented, in which a filtrado pasabanda de fase minima, ordenamientoreversible de cadenas de datos de longitud N, con N grande, and codificación secuenciada tipo MTF and aritmetica.
Abstract: RESUMEN Se presenta un procedimiento de compresion sin perdidaspara el procesamiento de la senal electrocardiograficabasado en el ordenamiento reversible de Burrows-Wheeler.Este ordenamiento permite introducir un alto nivel deredundancia en la informacion del registro, elevando el depor si ya alto nivel de redundancia contextual de la senal.Esta circunstancia es naturalmente aprovechada por uncodificador entropico tipo aritmetico para generar tasas decompresion significativas. El procedimiento requiere de: (a)un filtrado pasabanda de fase minima, (b) ordenamientoreversible de cadenas de datos de longitud N, con N grande,y (c) codificacion secuenciada tipo MTF y aritmetica. Cadauno de estos pasos es reversible, por lo que elprocedimiento general no introduce perdidas. Se lograntasas de compresion promedio de 6.0 sobre la base de datospublica PhysioBank, con una diferencia residual porcentualnula o practicamente nula, dependiendo de laimplementacion del filtro. Palabras clave: electrocardiografia ambulatoria digital,compresion sin perdidas, ordenamiento de Burrows-Wheeler.
01 Nov 2014
TL;DR: The results indicate that this technique can be applied without altering a real morphology ECG signals and it applies to all types ofECG signals, whether normal or presenting arrhythmias.
Abstract: In this paper, a new scheme for ElectroCardioGram (ECG) signal compression is proposed. The idea behind this efficient scheme is simple. Primary, the redundancies between the six leads represented in frontal plane (D I to aV F ) was exploited to perform only 2 leads using a converting method; in this step, the scheme provided a near lossless Compression Ratio (CR) up to 3∶1. Secondly, to increase the CR, we have used the recent powerful Discrete Wavelet Transform (DWT) signal processing combined to its thresholded coefficients and Adaptive Decimal to Binary Converter (ADBC). Experiments are carried out on the selected records from the PTB Arrhythmia Database and obtained an average Percent Root mean squared Difference (PRD) as around 0.59% to 16.38% for an average CR of 4.96∶1 to 130.34∶1 respectively. The results indicate that this technique can be applied without altering a real morphology ECG signals and it applies to all types of ECG signals, whether normal or presenting arrhythmias.
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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