An Efficient Lossless Compression Algorithm for Electrocardiogram Signals

Campobello, Giuseppe; Segreto, Antonino; Zanafi, Sarah; Serrano, Salvatore

doi:10.23919/eusipco.2018.8553597

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…In distributed embedded data collection systems and IoT devices, compression fills a critical role due to tight constraints on power, communications and computational resources. Lossless compression has been applied to reduce the volume of off-device traffic [30], by exploiting application specific data properties [31], deduplication [32], prediction [33], and similarities between concurrent data streams [34]. General-purpose compression algorithms such as LZW have proved prohibitively expensive for such low-power devices [35] due to their excessive energy costs.…”

Section: Link Compressionmentioning

confidence: 99%

L ² C: Combining Lossy and Lossless Compression on Memory and I/O

Eldstål-Ahrens

Arelakis²,

Sourdis

2022

ACM Trans. Embed. Comput. Syst.

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In this article, we introduce L 2 C, a hybrid lossy/lossless compression scheme applicable both to the memory subsystem and I/O traffic of a processor chip. L 2 C employs general-purpose lossless compression and combines it with state-of-the-art lossy compression to achieve compression ratios up to 16:1 and to improve the utilization of chip’s bandwidth resources. Compressing memory traffic yields lower memory access time, improving system performance, and energy efficiency. Compressing I/O traffic offers several benefits for resource-constrained systems, including more efficient storage and networking. We evaluate L 2 C as a memory compressor in simulation with a set of approximation-tolerant applications. L 2 C improves baseline execution time by an average of 50% and total system energy consumption by 16%. Compared to the lossy and lossless current state-of-the-art memory compression approaches, L 2 C improves execution time by 9% and 26%, respectively, and reduces system energy costs by 3% and 5%, respectively. I/O compression efficacy is evaluated using a set of real-life datasets. L 2 C achieves compression ratios of up to 10.4:1 for a single dataset and on average about 4:1, while introducing no more than 0.4% error.

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Section: Link Compressionmentioning

confidence: 99%

L ² C: Combining Lossy and Lossless Compression on Memory and I/O

Eldstål-Ahrens

Arelakis²,

Sourdis

2022

ACM Trans. Embed. Comput. Syst.

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“…This calculation has more memory necessities and depends on a basic and effective encoding conspire, which can be actualized with basic checking operations. Hence, it can be effectively executed in asset obliged microcontrollers, as those are commonly utilized in a few low-cost ECG observing frameworks [23]. Another method presents the lossless ECG compression strategy.…”

Section: The Approach Of the Compressed Sensingmentioning

confidence: 99%

Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform

Hassan¹,

Alzaidi²,

Ghoneim³

et al. 2022

Computers, Materials &Amp; Continua

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This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper. A small number of wavelet components can describe the ECG signal as related work to obtain a sparse ECG signal. A sensing technique for ECG signal compression, which is a novel area of research, is proposed. ECG signals are introduced randomly between any successive beats of the heart. MIT-BIH database can be represented as the experimental database in this domain of research. The MIT-BIH database consists of various ECG signals involving a patient and standard ECG signals. MATLAB can be considered as the simulation tool used in this work. The proposed method's uniqueness was inspired by the compression ratio (CR) and achieved by MDCT. The performance measurement of the recovered signal was done by calculating the percentage root mean difference (PRD), mean square error (MSE), and peak signal to noise ratio (PSNR) besides the calculation of CR. Finally, the simulation results indicated that this work is one of the most important works in ECG signal compression.

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“…Parameter extraction uses modeling and entropy coding to extract crucial data from the ECG signal to recover the characteristics of the signal. To elicit the features from the samples different techniques such as adaptive linear prediction (ALP) with Golomb-rice code encoding scheme [18], EDLZW [19], zero-order predictor [20], set partitioning in hierarchical trees (SPIHT) [21,22] and principal component analysis [23,24] were studied in the literature. A transform-domain method is the most focused and popular method among the other lossy compression technique.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Sharma

Sunkaria

2023

Biomed. Phys. Eng. Express

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Background:In telecardiology, the bio-signal acquisition processing and communication for clinical purposes occupies larger storage and significant bandwidth over a communication channel. Electrocardiograph (ECG) compression with effective reproductivity is highly desired. In the present work, a compression technique for ECG signals with less distortion by using a non-decimated stationary wavelet with a run-length encoding scheme has been proposed.Method:In the present work non-decimated stationary wavelet transform (NSWT) method has been developed to compress the ECG signals. The signal is subdivided into N levels with different thresholding values. The wavelet coefficients having values larger than the threshold are evaluated and the remaining are suppressed. In the presented technique, the biorthogonal wavelet is employed as it improves the compression ratio as well percentage root means square ratio when compared to the existing method and exhibits better results. Then the coefficients are pre-processed by using the Savitzky-Golay filter which discards the corrupted signals. The wavelet coefficients are quantized using dead-zone quantization which removes the value close to zeros. To encode these values run length encoding scheme is applied and compressed ECG signals are obtained.Results:The presented methodology has been evaluated on the MIT-BIH arrhythmias database which contains 4800 ECG fragments from forty-eight clinical records. An average compression ratio = 33.12, PRD =1.99, NPRD =2.53, and QS = 16.57 is obtained in the proposed technique.Conclusion:The proposed technique exhibits a high compression ratio and reduces distortion compared to the existing method.

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An Efficient Lossless Compression Algorithm for Electrocardiogram Signals

Cited by 16 publications

References 27 publications

L ² C: Combining Lossy and Lossless Compression on Memory and I/O

L ² C: Combining Lossy and Lossless Compression on Memory and I/O

Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

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An Efficient Lossless Compression Algorithm for Electrocardiogram Signals

Cited by 16 publications

References 27 publications

L 2 C: Combining Lossy and Lossless Compression on Memory and I/O

L 2 C: Combining Lossy and Lossless Compression on Memory and I/O

Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

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L ² C: Combining Lossy and Lossless Compression on Memory and I/O

L ² C: Combining Lossy and Lossless Compression on Memory and I/O