Compressed sensing based method for ECG compression

Polanía, Luisa F.; Carrillo, Rafael E.; Blanco–Velasco, Manuel; Barner, Kenneth E.

doi:10.1109/icassp.2011.5946515

Cited by 121 publications

(93 citation statements)

References 6 publications

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“…For example, Lee and Buckley [4] applied the DCT transform to an ECG data matrix composed of regular heartbeats and Bilgin [5] applied JPEG2000 compression to a similarly constructed matrix. Recently, we proposed in [6] a compressed sensing (CS) based ECG compression framework that utilizes distributed compressed sensing (see [7] and references therein) to exploit the inter-beat correlation structure. Following this line of thought, we propose a bidimensional L.F. Polania ECG compression scheme based on matrix completion, that exploits both intra and inter-beat correlations.…”

Section: Introductionmentioning

confidence: 99%

Matrix completion based ECG compression

Polanía

Carrillo

Blanco–Velasco

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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An innovative electrocardiogram compression algorithm is presented in this paper. The proposed method is based on matrix completion, a new paradigm in signal processing that seeks to recover a low-rank matrix based on a small number of observations. The low-rank matrix is obtained via normalization of electrocardiogram records. Using matrix completion, the ECG data matrix is recovered from a few number of entries, thereby yielding high compression ratios comparable to those obtained by existing compression techniques. The proposed scheme offers a low-complexity encoder, good tolerance to quantization noise, and good quality reconstruction.

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Section: Introductionmentioning

confidence: 99%

Matrix completion based ECG compression

Polanía

Carrillo

Blanco–Velasco

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…In view of the this observation, we propose an encoding algorithm that divides the ECG spectrum into low-pass and high-pass components, and use the Fourier and the wavelet coefficients respectively for their encoding. In this paper, we make use of the 'db4' wavelet basis in view of its reported superiority [3]. Conceptual block diagram of hybrid Fourier/wavelet encoder.…”

Section: Computing Inmentioning

confidence: 99%

“…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) [5], wavelet packets [6] SPIHT (set partitioning in hierarchical trees) algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

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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“…As shown in Fig. 1(a), the conventional CS system employs a random demodulator which consists of a pseudorandom number (PN) generator, a mixer, and an integrator [15]- [18]. The mixer performs randomization by multiplying the input with a PN sequence.…”

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confidence: 99%

Digital-Assisted Asynchronous Compressive Sensing Front-End

Zhou

Ramírez

Palermo

et al. 2012

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Abstract-Compressive sensing (CS) is a promising technique that enables sub-Nyquist sampling, while still guaranteeing the reliable signal recovery. However, existing mixed-signal CS frontend implementation schemes often suffer from high power consumption and nonlinearity. This paper presents a digital-assisted asynchronous compressive sensing (DACS) front-end which offers lower power and higher reconstruction performance relative to the conventional CS-based approaches. The front-end architecture leverages a continuous-time ternary encoding scheme which modulates amplitude variation to ternary timing information. Power is optimized by employing digital-assisted modules in the front-end circuit and a part-time operation strategy for high-power modules. An -member Group-based Total Variation ( -GTV) algorithm is proposed for the sparse reconstruction of piecewise-constant signals. By including both the inter-group and intra-group total variation, the -GTV scheme outperforms the conventional TV-based methods in terms of faster convergence rate and better sparse reconstruction performance. Analyses and simulations with a typical ECG recording system confirm that the proposed DACS front-end outperforms a conventional CS-based front-end using a random demodulator in terms of lower power consumption, higher recovery performance, and more system flexibility.

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Compressed sensing based method for ECG compression

Cited by 121 publications

References 6 publications

Matrix completion based ECG compression

Matrix completion based ECG compression

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

Digital-Assisted Asynchronous Compressive Sensing Front-End

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