Constrained ECG compression algorithm using the block-based discrete cosine transform

Benzid, Redha; Messaoudi, Abdelhamid; Boussaad, A.

doi:10.1016/j.dsp.2007.08.003

Cited by 52 publications

(19 citation statements)

References 24 publications

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“…In our comparisons, we use a DCT-based compression algorithm where DCT coefficients with magnitude smaller than a threshold are discarded. We choose this method mainly because it exhibits a better rate-distortion trade-off than using a wavelet transformation [13] and has practical implementation benefits as well [14]. Fig.…”

Section: Performance Evaluation Of Tans Schemes On Real Datasetsmentioning

confidence: 99%

“…Signal reconstruction may be performed through an optimization problem which can be formulated as: (12) where is a matrix representing the synthesis operator of an orthonormal basis where the signal has a sparse representation. The initial signal is recovered by multiplying the solution of the optimization problem with the inverse basis matrix: (13) Alternatives to this basis pursuit (BP) formulation [17] include lasso [18], the Dantzig selector [19], greedy methods [20], [21] and iterative methods related to belief propagation [22]. Exact asymptotic performance under Bayesian formulations can be computed using the methods of [23].…”

Section: Performance Evaluation Of Tans Schemes On Real Datasetsmentioning

confidence: 99%

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Backward Adaptation for Power Efficient Sampling

Feizi

Goyal

Médard

2014

IEEE Trans. Signal Process.

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Advances in sampling and coding theory have contributed significantly towards lowering power consumption of resource-constrained devices, e.g. battery-operated sensor nodes, enabling them to operate for extended periods of time. In this paper, rate and energy efficiency of a recently proposed adaptive nonuniform sampling framework by Feizi et al., called Time-Stampless Adaptive Nonuniform Sampling (TANS), is examined and compared against state-of-the-art methods. TANS addresses one of the main limitations of nonuniform sampling schemes: sampling times do not need to be stored/transmitted since they can be computed using a function of previously taken samples. The sampling rate is adapted continuously with the aim of reducing the rate and therefore the energy consumption of the sampling process when the signal is varying slowly. Three TANS methods are proposed for different signal models and sampling requirements: i) TANS by polynomial extrapolation, which only assumes the third derivative of the signal is bounded but requires no other specific knowledge of the signal; ii) TANS by incremental variation, where the sampling time intervals are chosen from a lattice; and iii) TANS constrained to a finite set of sampling rates. Practical implementation details of TANS are discussed, and its rate and energy performance are compared with uniform sampling followed by a transformation-based compression, nonuniform sampling, and compressed sensing. Our results demonstrate that TANS provides significant improvements in terms of both the rate-distortion performance and the energy consumption compared against the other approaches.Index Terms-Nonuniform sampling, energy efficient acquisition schemes, compressed sensing, adaptive sampling for biomedical applications.

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Section: Performance Evaluation Of Tans Schemes On Real Datasetsmentioning

confidence: 99%

Section: Performance Evaluation Of Tans Schemes On Real Datasetsmentioning

confidence: 99%

Backward Adaptation for Power Efficient Sampling

Feizi

Goyal

Médard

2014

IEEE Trans. Signal Process.

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“…In the literature, ECG data compression has been extensively studied (see, e.g., [5], [6], [7], [8], [9], [10]). However, WBAN applications not only require small reconstruction error (distortion) and high compression ratio (CR), but also require these to come at low complexity.…”

Section: Ecg Data Compression For Wbanmentioning

confidence: 99%

ECG Monitoring over Bluetooth: Data Compression and Transmission

Yang

Chong³

2010

2010 IEEE Wireless Communication and Networking Conference

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Remote health monitoring by exploiting wireless communications technologies is an emerging area receiving increasing interests from academia, research labs and industry. This issue has also been brought up in the standardization process of IEEE 802.15 Task Group 6 Wireless Body Area Networks (WBAN). The challenge is to find the appropriate combination of medical data processing techniques and the wireless technology in order to meet the particularly stringent error, latency and power consumption requirements of healthcare applications. In this paper, we present the results of a case study on wireless electrocardiogram (ECG) monitoring over Bluetooth. Based on the special considerations of (processing and transmission) power consumption at wireless ECG sensors, we first propose a low complexity ECG compression method. Comparisons with existing approaches confirm the superior performance of our method. We then study the data reconstruction performance at the Bluetooth receiver and find that: i) the uncompressed ECG data transmission is not necessarily better than the compressed transmission; and ii) there exists an optimum ECG data compression ratio for the wireless link.

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“…Benzid vd. [5], Ayrık Kosinüs Dönüşüm (AKD) tekniğini EKG verilerini sıkıştırmak için kullanmıştır. Shinde ve Kanjalkar [6], EKG verilerinin sıkıştırılması için FTD, AKD ve Dalgacık Tabanlı Dönüşüm (DTD) tekniklerinin karşılaştırılmalı bir çalışmasını gerçekleştirmiştir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

EKG verilerinin destek vektör regresyon yöntemiyle sıkıştırılması

Karal¹

2018

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract Purpose: Electrocardiogram (ECG) signals must be continuously recorded and monitored to effectively detect diseases caused by fast or slow heartbeat, that is, rhythm disorders. However, long monitoring periods generates large amount of data that are difficult to store and transmit. Moreover, these records may be subject to noise due to the environment. For this reason, an effective and reliable data compression technique is needed for ECG data compression without losing the clinical information content. In order to address the aforementioned problems, in this paper, SVR based compression algorithm is introduced. Theory and Methods: SVR provides a better generalization capability because it tries to minimize both the empirical risk minimization and the structural risk minimization principle. Therefore, SVR has been widely used in many scientific areas. The success of SVR in application is dependent on the ε-insensitive Laplace (Vapnik) loss function that ignores the errors lower than the user-defined ε value. Small noisy training samples falling into the ε-insensitive zone are not included in the solution representation. So, SVR yields a sparse (compressed) model in the obtained solution. The compressed signal is expressed as the weighted sum of basis functions. Different from the other transformation-based compression methods, the number, position, and shape of these functions are automatically determined by the SVR algorithm based on the solution of the quadratic optimization problem. Results:From the experimental results, the user defined ε parameter allows us to check the selection of the samples (support vectors) related to the direct compression. If the value of the ε parameter increases, the Compression Ratio (CR) also increases, and at the same time increases the Percent Root Mean Square Difference(PRD) and Root Mean Square Error (RMSE) values, which leads to distortions in the compressed signal. Also, when the value of ε increases, the SVR algorithm also increases in generalization ability (lowers w). Conclusion:Computer simulation results show that the performance of the proposed SVR-based compression algorithm is better than other transformation-based compression techniques such as the commonly used Fourier Transform (FT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT). As can be seen from the table, at the same compression ratio (SO = 6.38), the SVR technique reached minimum PRD = 17.30 and RMSE = 0.021. In addition, the proposed method has important features such as being independent of the sampling conditions, being a single minimum in the global sense, and not requiring additional algorithms (preprocessing of the ECG signal). As a result, SVR based compression algorithm is an attractive candidate for compressing ECG signals.

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Constrained ECG compression algorithm using the block-based discrete cosine transform

Cited by 52 publications

References 24 publications

Backward Adaptation for Power Efficient Sampling

Backward Adaptation for Power Efficient Sampling

ECG Monitoring over Bluetooth: Data Compression and Transmission

EKG verilerinin destek vektör regresyon yöntemiyle sıkıştırılması

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