EEG compression using JPEG2000: How much loss is too much?

Higgins, Garry; Faul, Stephen; McEvoy, Robert P.; McGinley, Brian; Glavin, Martin; Marnane, William P.; Jones, Edward

doi:10.1109/iembs.2010.5628020

Cited by 44 publications

(23 citation statements)

References 11 publications

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“…There has been other work since the introduction of the WDD where authors either apply the measure to evaluate their algorithms [1], propose an alternative to the WDD [7], or consider a similar measure and compression techniques for a di↵erent physiologic signal [5,4]. However, most of the work is focused on compression and signal reconstruction, is not necessarily explored in the context of BSNs, and looks at very general measures.…”

Section: Information Quality Metricsmentioning

confidence: 99%

A methodology for developing quality of information metrics for body sensor design

Armenti

Asare

et al. 2012

Proceedings of the Conference on Wireless Health

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Body sensors networks (BSNs) are emerging technologies that are enabling long-term, continuous, remote monitoring of physiologic and biokinematic information for various medical applications. Because of the varying computational, storage, and communication capabilities of di↵erent components in the BSN, system designers must make design choices that trade o↵ information quality with resource consumption and system battery lifetime. Given these trade-o↵s, there is the possibility that the information presented to the health practitioner at the end point may deviate from what was originally sensed. In some cases, these deviations may cause a practitioner to make a di↵erent decision from what would have been made given the original data. Engineers working on such systems typically resort to traditional measures of data quality like RMSE; however, these metrics have been shown in many cases to not correlate well with the notions of information quality for the particular application. Objective metrics of information distortion and its e↵ects on decision making are therefore necessary to help BSN designers make more informed trade-o↵s between design constraints and information quality and to help practitioners understand the kind of information being produced by BSNs, on which they have to base decisions. In this paper, we present a general methodology for developing such metrics for various BSN applications, illustrate how this methodology can be applied to a real application through a case study, and discuss issues with developing such metrics.

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Section: Information Quality Metricsmentioning

confidence: 99%

A methodology for developing quality of information metrics for body sensor design

Armenti

Asare

et al. 2012

Proceedings of the Conference on Wireless Health

View full text Add to dashboard Cite

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“…Furthermore, the compression factor of MCS is limited by the number of channels, while SHS and BERN can achieve much higher compression rates. However, only SHS yields an acceptable reconstruction performance above 16dB at 32× compression, with 10dB considered as the minimum required performance in order to retain diagnostically relevant information [17]. Table III contains the reconstruction SNR for the second dataset.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Structured sampling and recovery of iEEG signals

Baldassarre

Aprile

Shoaran

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a promising approach to directly acquire compressed signals, allowing to reduce the power consumption associated with data transmission. To this end, we propose an efficient CS scheme which exploits the structure of the intracranial EEG signals, both in sampling and recovery. Our structure-aware approach is conceptually simple to implement in hardware and yields stateof-the-art compression rates up to 32x with high reconstruction quality, as illustrated on two human iEEG datasets.

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“…For instance, root-mean-squared difference (PRD) has been used to evaluate the reconstruction quality of EEG reconstruction in [30]. However, different thresholds have been established based on the targeted application.…”

Section: Cs-based Eeg Compressionmentioning

confidence: 99%

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

Djelouat

Baali

Amira

et al. 2017

Wireless Communications and Mobile Computing

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The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.

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EEG compression using JPEG2000: How much loss is too much?

Cited by 44 publications

References 11 publications

A methodology for developing quality of information metrics for body sensor design

A methodology for developing quality of information metrics for body sensor design

Structured sampling and recovery of iEEG signals

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

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