Multichannel ECG Compression using Block-Sparsity-based Joint Compressive Sensing

Kumar, Sushant; Deka, Bhabesh; Datta, Sumit

doi:10.1007/s00034-020-01483-x

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…Analysis of recent sources The need to compress multichannel signals and images appears in practice of data processing quite often. It is, in particular, typical for multichannel electrocardiogram (ECG) compression [14,15], color image coding [16], compression of dual-polarization radar [17], multispectral [10,[18][19][20], and hyperspectral [11,21] images. A common and typical property of all these types of signals and images is that data in channels (components) are quite highly correlated (at least, for most component data).…”

Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

Особливості Стиснення Мультиспектральних Зображень Із Втратами

ZRIAKHOV,

KRYVENKO,

ЛУКІН

2024

Вісник Хмельницького національного університету. Серія: Технічн

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This paper concerns some aspects of lossy compression applied to six-component Landsat multispectral images where all component images have identical spatial resolution. It is shown that most component images are correlated although the cross-correlation coefficients for component images vary in rather wide limits – from 0.11 in one dataset and 0.72 in another dataset to almost unity in both cases. Compression based on discrete cosine transform is considered where 2D blocks have the size of 32×32 pixels. Embedded deblocking after decompression is applied as well. Lossy compression can be applied component-wise and in a 3D manner for spectral decorrelation of data where 6-point discrete cosine transform is applied. It is shown that, in the latter case, a significantly larger compression ratio can be achieved. The attained benefit in CR is about 30-80% compared to the component-wise compression or, alternatively, the benefit in peak signal-to-noise ratio can reach a few dB for the same compression ratio. The coders are studied for peak signal-to-noise ratio varying in the limits from about 23 dB to about 46 dB that correspond to image quality starting from annoying to practically invisible distortions. In addition, there are some image pre-processing operations that are able to further improve the compression ratio. They are image normalization and band re-ordering. All possible variants of band ordering are studied for both test images. The impact of band ordering on compressed image quality is analyzed in terms of rate/distortion curves for the 2D and 3D versions of the considered coder. It is demonstrated that, due to optimal ordering, a few % improvement of CR can be gained. However, the optimal band order for the considered multichannel images is not the same and this can cause problems in practice to be studied in the future.

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Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

Особливості Стиснення Мультиспектральних Зображень Із Втратами

ZRIAKHOV,

KRYVENKO,

ЛУКІН

2024

Вісник Хмельницького національного університету. Серія: Технічн

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“…By presenting this proposed scheme, they have achieved better compression ratio and signal-to-noise ratio. Kumar et al [14] had the goal to reduce the energy consumption while transmitting the multi-channel ECG data in wireless body area networks. To accomplish this, the authors presented a method for multichannel ECG compression based on block sparsity.…”

Section: Related Workmentioning

confidence: 99%

Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

Mishra¹,

Jain²

2022

Intelligent Automation &Amp; Soft Computing

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In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is further to be improved. In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization algorithm (ROA). With the optimal sensing matrix, the compressed signal is reconstructed using Step Size optimized Sparsity Adaptive Matching Pursuit algorithm (SAMP). The results of this work demonstrate that the optimised CS framework achieves a higher compression rate and probability of reconstruction than the standard CS framework.

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“…In [83], a sparse encoding algorithm consisting of two subcategories based on geometry-based methods and WT based iterative thresholding was reported. In [84] an energy-efficient novel block-sparsity-based multichannel ECG compression scheme that utilizes spatiotemporal correlation and multiscale information of the signal using wavelet transform of the signal was reported. In [85], a deep learning technique based on convolutional auto-encoder is applied to achieve ECG signal compression without any independent encoding method.…”

Section: ) Ecg Compressionmentioning

confidence: 99%

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Arora¹,

Mishra

2021

IEEE Access

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Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations.

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Multichannel ECG Compression using Block-Sparsity-based Joint Compressive Sensing

Cited by 12 publications

References 26 publications

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

Origins of ECG and Evolution of Automated DSP Techniques: A Review

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