Compressed Sensing for Bioelectric Signals: A Review

Craven, Darren; McGinley, Brian; Kilmartin, Liam; Glavin, Martin; Jones, Edward

doi:10.1109/jbhi.2014.2327194

Cited by 160 publications

(116 citation statements)

References 52 publications

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“…The current lossy algorithms, including the compressive sensing algorithms, do not compare favorably with other state-of-the-art lossless compression techniques when considering only CR vs. reconstruction quality 27 . Therefore, the choice of using a lossy algorithm depends on its ability to provide a low-power implementation.…”

Section: Discussionmentioning

confidence: 97%

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Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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Section: Discussionmentioning

confidence: 97%

“…Recent research into the implementation of lossy methods in an ambulatory environment faces many challenges 27 . The current lossy algorithms, including the compressive sensing algorithms, do not compare favorably with other state-of-the-art lossless compression techniques when considering only CR vs. reconstruction quality 27 .…”

Section: Discussionmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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“…However, these two techniques may either interrupt the current task execution or impose secondary tasks on the operator. An alternative method is to use physiological measures, which is featured by continuous-time, non-invasive, and objective assessment (Craven et al, 2015). Since the physiological signals provide real-time information about the cognitive mechanism underlying performance variations, the variations in physiological features can be measured before manifest performance decline.…”

Section: Introductionmentioning

confidence: 99%

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load

2017

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This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

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“…CS techniques have recently become popular for single-detector systems and rapid video recording techniques and have been used in many imaging applications [11][12][13][14][15][16][17][18][19][20]. The fundamental concept of CS involves reconstructing the original information from fewer measurements.…”

Section: Introductionmentioning

confidence: 99%

Simulation study on compressive laminar optical tomography for cardiac action potential propagation

Harada

Tomii

Manago

et al. 2017

Biomed. Opt. Express

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To measure the activity of tissue at the microscopic level, laminar optical tomography (LOT), which is a microscopic form of diffuse optical tomography, has been developed. However, obtaining sufficient recording speed to determine rapidly changing dynamic activity remains major challenges. For a high frame rate of the reconstructed data, we here propose a new LOT method using compressed sensing theory, called compressive laminar optical tomography (CLOT), in which novel digital micromirror device-based illumination and data reduction in a single reconstruction are applied. In the simulation experiments, the reconstructed volumetric images of the action potentials that were acquired from 5 measured images with random pattern featured a wave border at least to a depth of 2.5 mm. Consequently, it was shown that CLOT has potential for over 200 fps required for the cardiac electrophysiological phenomena.

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Compressed Sensing for Bioelectric Signals: A Review

Cited by 160 publications

References 52 publications

Efficient ECG Compression and QRS Detection for E-Health Applications

Efficient ECG Compression and QRS Detection for E-Health Applications

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load

Simulation study on compressive laminar optical tomography for cardiac action potential propagation

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