On the choice of an electromyogram data compression method

Prieto‐Guerrero, Alfonso; Mailhes, Corinne

doi:10.1109/iembs.1997.757009

Cited by 31 publications

(25 citation statements)

References 13 publications

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“…In that case, we have to use OMP or other recovery algorithms to find them. However, DCT compacts the energy of most signals in the low-frequency components, particularly for physiological signals like ECG [34] or EMG [35]. The remaining high-frequency components tend to have lower values.…”

Section: ) Validation Of the Proposed Measurement Matrixmentioning

confidence: 99%

Compressed Sensing: A Simple Deterministic Measurement Matrix and a Fast Recovery Algorithm

Ravelomanantsoa

Rabah

Rouane

2015

IEEE Trans. Instrum. Meas.

144

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Compressed sensing (CS) is a technique that is suitable for compressing and recovering signals having sparse representations in certain bases. CS has been widely used to optimize the measurement process of bandwidth and power constrained systems like wireless body sensor network. The central issues with CS are the construction of measurement matrix and the development of recovery algorithm. In this paper, we propose a simple deterministic measurement matrix that facilitates the hardware implementation. To control the sparsity level of the signals, we apply a thresholding approach in the discrete cosine transform domain. We propose a fast and simple recovery algorithm that performs the proposed thresholding approach. We validate the proposed method by compressing and recovering electrocardiogram and electromyogram signals. We implement the proposed measurement matrix in a MSP-EXP430G2 LaunchPad development board. The simulation and experimental results show that the proposed measurement matrix has a better performance in terms of reconstruction quality compared with random matrices. Depending on the compression ratio, it improves the signal-to-noise ratio of the reconstructed signals from 6 to 20 dB. The obtained results also confirm that the proposed recovery algorithm is, respectively, 23 and 12 times faster than the orthogonal matching pursuit (OMP) and stagewise OMP algorithms.Index Terms-Compressed sensing (CS), deterministic measurement matrix, electrocardiogram (ECG), electromyogram (EMG), recovery algorithm.

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Section: ) Validation Of the Proposed Measurement Matrixmentioning

confidence: 99%

Compressed Sensing: A Simple Deterministic Measurement Matrix and a Fast Recovery Algorithm

Ravelomanantsoa

Rabah

Rouane

2015

IEEE Trans. Instrum. Meas.

144

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“…ADPCM was able to reduce the MES from 12 bits per sample to 4 bits per sample, resulting in a compression ratio (CR) of 66.7%. Guerrero and Mailhes [8] compared the performance of three linear predictive coding methods (differential pulse code modulation, multi-pulse coder, and code excited linear predictive coder) and two techniques based on transform coding (discrete cosine transform and discrete wavelet transform). They found that transform coding techniques outperformed the linear predictive coding methods.…”

Section: Introductionmentioning

confidence: 99%

Compression of surface myoelectric signals using MP3 encoding

Chan

2011

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

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The potential of MP3 compression of surface myoelectric signals is explored in this paper. MP3 compression is a perceptual-based encoder scheme, used traditionally to compress audio signals. The ubiquity of MP3 compression (e.g., portable consumer electronics and internet applications) makes it an attractive option for remote monitoring and telemedicine applications. The effects of muscle site and contraction type are examined at different MP3 encoding bitrates. Results demonstrate that MP3 compression is sensitive to the myoelectric signal bandwidth, with larger signal distortion associated with myoelectric signals that have higher bandwidths. Compared to other myoelectric signal compression techniques reported previously (embedded zero-tree wavelet compression and adaptive differential pulse code modulation), MP3 compression demonstrates superior performance (i.e., lower percent residual differences for the same compression ratios).

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“…Norris et al [1] and Chan et al [2] investigated lossy compression of steady-state and transient MES using adaptive differential pulse code modulation (ADPCM), the de facto standard for voice compression. Guerrero et al [3] compared the performance of common compression techniques, such as differential pulse code modulation, multi-pulse coding, and code excited linear prediction to transform-based compression techniques, when applied to intramuscular and the surface MES. Wellig et al [4] investigated intramuscular MES compression using a modified version of Shapiro's [5] embedded zero-tree wavelet (EZW) compression algorithm.…”

Section: Introductionmentioning

confidence: 99%

Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets

Norris

Englehart

Lovely

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Within the field on biomedical engineering, the majority of compression research has focused on encoding medical images, electrocardiograms, and electroencephalograms.Although long-term myoelectric signal (MES) acquisition is important for neuro-muscular system analysis and telemedicine applications, very few studies have been published on MES compression. This research investigates static and dynamic MES compression using the embedded zerotree wavelet (EZW) compression algorithm and compares its performance to a standard wavelet compression technique.

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On the choice of an electromyogram data compression method

Cited by 31 publications

References 13 publications

Compressed Sensing: A Simple Deterministic Measurement Matrix and a Fast Recovery Algorithm

Compressed Sensing: A Simple Deterministic Measurement Matrix and a Fast Recovery Algorithm

Compression of surface myoelectric signals using MP3 encoding

Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets

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