Improved ECG compression method using discrete cosine transform

Bendifallah, A.; Benzid, Redha; Boulemden, Mohammed

doi:10.1049/el.2010.3191

Cited by 58 publications

(33 citation statements)

References 8 publications

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“…In addition, if X were Gaussian, the optimal transform is known to be the Karhunen-Loève transform (KLT). Further, assuming the KLT coefficients are arranged in the descending order, one would keep the first K coefficients such that their energy is within a factor of the aggregate signal energy [5]. However, practicality of aforementioned statistical techniques still remains problematic, as numerous heart conditions, both normal and abnormal, bearing specific temporal signatures arise, making comprehensive statistical modeling of ECG signals difficult.…”

Section: Computing Inmentioning

confidence: 99%

“…In this context, various researchers have reported ECG signals to be sparse in wavelet bases, and in particular "Daubechies 4" (db4) wavelet basis [3,4]. In the process, various researchers observed signal sparsity in wavelet and related domains, and demonstrated the respective efficacy of discrete cosine transform (DCT) [5], wavelet packets [6] SPIHT (set partitioning in hierarchical trees) algorithm [7]. Signal sparsity is also central to compressed sensing of ECG signals [3].…”

Section: Introductionmentioning

confidence: 99%

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Telecardiology under Resource Constraint: Low:complexity Compact Representation of ECG

Tamboli

Jana

2016

2016 Computing in Cardiology Conference (CinC)

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Compact, yet faithful, representation of ECG signals to meet bandwidth and power constraints remains central to successful telecardiology, primarily in infrastructuredeficient areas. Towards practical realization, we seek desired compactness in the class of low-complexity transform representations. A typical ECG signal consists of a strong rhythmic (low-pass) component, with compact Fourier transform representation, and temporally localized (high-pass) features, efficiently represented by wavelet transform. Accordingly, we propose a compact representation consisting of suitable Fourier and wavelet coefficients. As computation of such coefficients is at most O(n log n), the proposed representation inherits the desired low complexity. Our method achieves targeted representation accuracy using fewer transform coefficients compared to the well-known fixed transform representations. IntroductionElectrocardiogram (ECG) is an indispensable tool in monitoring and management of cardiac health. ECG records are increasingly being maintained in electronic form, and machines directly producing digitized ECG signals are now commonplace [1]. Very often, such signals are stored locally, or transmitted to a remote location, respectively, for decision making at a later point, or at a distance [2]. Accordingly, with a view to minimizing storage requirement and/or communication bandwidth, one desires to represent ECG signals as compactly as possible without adversely affecting eventual clinical interpretation.Interestingly, the problem of practical ECG compression has been studied since several decades. Efficacy of a compression algorithm depends on signal sparsity. In this context, various researchers have reported ECG signals to be sparse in wavelet bases, and in particular "Daubechies 4" (db4) wavelet basis [3,4]. In the process, various researchers observed signal sparsity in wavelet and related domains, and demonstrated the respective efficacy of discrete cosine transform (DCT) SPIHT (set partitioning in hierarchical trees) algorithm [7]. Signal sparsity is also central to compressed sensing of ECG signals [3]. Moreover, in denoising applications, a sparser representation allows more coefficients to fall below a threshold thus allowing more noise to be removed [8].[In an earlier work, we proposed a Hybrid Fourier/wavelet technique for ECG signal approximation [9]. The method was demonstrated on signals derived from ANSI/AAMI EC13 dataset [10]. In this paper, we present a comparative study of ECG signal representation using Fourier transform (FT), wavelet transform (WT), discrete cosine transform (DCT) and the proposed hybrid method. The signals for the experimental dataset were picked from MIT-BIH Arrhythmia database [10]. Compared to the DCT, FT and WT representations, the proposed method saves of 46.15%, 25% and 4.54% coefficients on average, for certain target representation accuracy, respectively.The rest of the paper is organized as follows. We begin with describing theoretical background and the key idea in Sec. ...

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Section: Computing Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Telecardiology under Resource Constraint: Low:complexity Compact Representation of ECG

Tamboli

Jana

2016

2016 Computing in Cardiology Conference (CinC)

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“…DCT has strong "energy compaction property" & provide high de-correlation. In DCT compression signal information can restore in a restrict number of DCT coefficients [15,29,30]. DCT-II provide very impressive CR but at the cost of high distortion.…”

Section: Dct Domainmentioning

confidence: 99%

A Review of ECG Data Compression Techniques

Singh¹,

Kaur²,

Singh³

2015

IJCA

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Electrocardiogram (ECG) data compression reduced the storage requirements to develop a more efficient telecardiology system for cardiac analysis and diagnosis. The ECG compression without loss of diagnostic information is based on the fact that consecutive samples of the digitized ECG carry redundant information that can be removed with very less computing effort. This paper focuses on providing a comparison of the major techniques (direct, transform, parameter extraction and 2D approaches) of ECG data compression which are intended to attain a lossless compressed data with relatively high compression ratio (CR) and low percent root mean square difference (PRD).The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

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“…Nowadays, sparse biopotential signals are being used to convey valuable information for diagnostic purposes [1,2,3]. In many cases, recording the blood pressure (BP), photoplethysmography (PPG), and electrocardiography (ECG) signals for a relatively long period may be necessary.…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the volume of data sampled in a monitoring device, biosignals are typically sampled and converted to digital signals by an analog to digital converter (ADC) at rates equal to the Nyquist rate or higher [4,5,6]. The sampled data are then compressed using compression techniques to reduce the size of the memory needed to save the data [1,2] and always post-processed with microprocessor systems [7,8]. In this paper, we mainly present a biopotential low-power systemon-chip (SoC) design, which consists of a 32-bit microprocessor digital system and a novel signal conditioning circuit.…”

Section: Introductionmentioning

confidence: 99%

Signal processing system-on-chip design for biomedical applications

Wang

et al. 2017

IEICE Electron. Express

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In view of the high demand for low-power biomedical signalprocessing schemes occupying very less on-chip areas, we propose a lowpower highly integrated system-on-chip (SoC) design comprising a nonuniform biomedical sensing signal conditioning circuit and a 32-bit microprocessor. The proposed design is implemented using a 0.18 µm CMOS EFLASH process in an area of 4.2 mm 2 , and is applied in PPG, ECG and BP measurements. Experimental results show that our SoC design provides extremely good performances for sparse features of all three biomedical applications. The charge consumption is 25 mAh for three months, for an experimental board design including sleep current and junction leakage current.

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Improved ECG compression method using discrete cosine transform

Cited by 58 publications

References 8 publications

Telecardiology under Resource Constraint: Low:complexity Compact Representation of ECG

Telecardiology under Resource Constraint: Low:complexity Compact Representation of ECG

A Review of ECG Data Compression Techniques

Signal processing system-on-chip design for biomedical applications

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