Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition

Chae, Daniel H.; Alem, Yibeltal F.; Durrani, Salman; Kennedy, Rodney A.

doi:10.1109/icassp.2013.6637862

Cited by 29 publications

(11 citation statements)

References 12 publications

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“…This represents a 6.5x more sample size reduction (engergy saving) than the reference framework [20]. Table 1 compares the proposed framework with existing CS frameworks [1,5,6,7] that adopt pre-determined basis in signal recovery. In general, our framework is able to further improve the CR by 2-4x for achieving an average PRD of 9%.…”

Section: Experiments Settings and Resultsmentioning

confidence: 99%

“…Experimental results based on MIT-BIH database show that our framework is able to achieve an average PRD of 9% at a CR of 10x. This indicates that our framework can achieve 2-4x additional energy saving on sensor nodes (for the same reconstruction quality) compared to the reference designs [1,5,6,7]. Due to the training and personalization of the dictionary, the proposed framework has the potential to be generally applied to a wide range of physiological signals.…”

Section: Introductionmentioning

confidence: 85%

“…However, the compression ratio (CR) demonstrated by existing frameworks is limited given a signal recovery quality required for diagonosis purposes. In [5,6] percent root-mean-square difference (PRD) of 8.5% and 9% is reported at a CR of 5x and 2.5x for ECG signals, respectively. These frameworks all deal with the sparsity of physiological signals on pre-determined bases and fail to take into account the individual variability in signals that is critical to exact signal recovery.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring

Xu¹,

Li²,

Ren³

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL) together. The learned dictionary carries individual characteristics of the original signal, under which the signal has an even sparser representation compared to pre-determined dictionaries. As a consequence, the compression ratio is effectively improved by 2-4x comparing to prior works. Besides, the proposed framework offloads pre-processing from sensor nodes to the server node prior to dictionary learning, providing further reduction in hardware costs. As it is data driven, the proposed framework has the potential to be used with a wide range of physiological signals.Index Terms-Compressive sensing, online dictionary learning, wireless sensor nodes (WSNs), wireless health.

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Section: Experiments Settings and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring

Xu¹,

Li²,

Ren³

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…The results produced by this study suggest that DNN-based arrhythmia detection from ambulatory ECG images can be undertaken without substantial loss of accuracy compared with raw signal analysis. This is despite the fact that (i) ambulatory ECG data generally contains more noise and movement artefact than recordings in a controlled environment, 23 (ii)…”

Section: Discussionmentioning

confidence: 99%

Deep learning to automatically interpret images of the electrocardiogram: Do we need the raw samples?

Brisk

Bond

Banks

et al. 2019

Journal of Electrocardiology

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“…Thus, obviously, as a first reflection, the compression of the ECG signal might be an adequate solution. However, it has been shown in Chae et al (2013) that applying compressive sampling, also known as compressed sensing, is not an adequate solution. To reduce the volume of transmitted data, we advocate the approach of on-sensor extraction of the relevant features from the ECG signal, analysing them for AF detection and sending notification to the server with eventually the extracted features for further classification.…”

Section: Introductionmentioning

confidence: 99%

Design of embedded atrial fibrillation detection scheme for wireless body area networks

AlMusallam

Soudani

2019

IJES

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Wireless body sensor networks (WBSNs) and wearable technologies are the new trends in healthcare applications. This technology can provide real-time monitoring of the patient's biosignals and health condition. In this context, the analysis of ECG signals, reflecting the heart activity, is considered as the key tool in diagnosing cardiac disorders such as atrial fibrillation (AF) that can lead to strokes and heart failure. Classical approaches for sensor-based AF detection require continuous transmission of ECG signals to a remote server, which can rapidly exhaust the sensor energy and shorten the lifetime of the application. In this paper, we propose a new low-power scheme for AF episodes detection in ECG signal that is intended for implementation in WBSN. The paper details the design of this scheme and demonstrates its high accuracy for AF detection and shows that it saves 93% of energy.

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Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition

Cited by 29 publications

References 12 publications

An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring

An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring

Deep learning to automatically interpret images of the electrocardiogram: Do we need the raw samples?

Design of embedded atrial fibrillation detection scheme for wireless body area networks

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