Sparse signal processing using iterative method with adaptive thresholding (IMAT)

Marvasti, Farokh; Azghani, Masoumeh; Imani, P; Pakrouh, P; Heydari, SJ; Golmohammadi, Azarang; Kazerouni, Abbas; Khalili, Mohsen

doi:10.1109/ictel.2012.6221328

Cited by 38 publications

(27 citation statements)

References 17 publications

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“…1, this step consists of two substeps of High Resolution Spectrum Estimation using the Iterative Method with Adaptive Thresholding (IMAT) and Peak Selection. IMAT is a fast and efficient algorithm for sparse signal reconstruction [19], [20] that proved to outperform some other spectrum estimation techniques regarding reconstruction performance and complexity [21]. This algorithm is used to provide a higher resolution and denoised spectrum of the input signal prior to Peak Selection.…”

Section: A Ma Cancellationmentioning

confidence: 99%

Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry

Mashhadi¹,

Asadi²,

Eskandari³

et al. 2016

IEEE Signal Process. Lett.

116

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Abstract-This paper considers the problem of casual heart rate tracking during intensive physical exercise using simultaneous 2 channel photoplethysmographic (PPG) and 3 dimensional (3D) acceleration signals recorded from wrist. This is a challenging problem because the PPG signals recorded from wrist during exercise are contaminated by strong Motion Artifacts (MAs). In this work, a novel algorithm is proposed which consists of two main steps of MA Cancellation and Spectral Analysis. The MA cancellation step cleanses the MA-contaminated PPG signals utilizing the acceleration data and the spectral analysis step estimates a higher resolution spectrum of the signal and selects the spectral peaks corresponding to HR. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the proposed algorithm achieves an average absolute error of 1.25 beat per minute (BPM). These experimental results also confirm that the proposed algorithm keeps high estimation accuracies even in strong MA conditions.

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Section: A Ma Cancellationmentioning

confidence: 99%

Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry

Mashhadi¹,

Asadi²,

Eskandari³

et al. 2016

IEEE Signal Process. Lett.

116

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“…The task of further spectrum sparsification can be performed by the Iterative Method with Adaptive Thresholding (IMAT) algorithm [26]; given by eq. (4).…”

Section: A Spectral Sparsificationmentioning

confidence: 99%

“…A final contribution of this work is composed of three techniques, devised to improve performance of any arbitrary HR tracking system. First, the justified quality increase of PPG spectrum by sparsifying it using the Iterative Method with Adaptive Thresholding (IMAT) [26]. Second, achieving a more accurate (though slightly delayed) estimation of HR by applying a small order median filter on the sequence of HR estimates.…”

Section: Introductionmentioning

confidence: 99%

RealTime Heart Rate Monitoring Using Photoplethysmographic (PPG) Signals During Intensive Physical Exercises

Farhadi

Essalat

et al. 2016

Preprint

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Heart Rate (HR) is a fundamental vital sign, monitoring which provides essential information for automated healthcare systems. The emerging technology of Photoplethysmograph (PPG) is shown as a feasible candidate for such applications; however, Motion Artifacts (MA) hinder efficient HR estimation using PPG, especially in situations involving physical activities. It is previously shown that even in the presence of sever MA, HR is still traceable with the help of simultaneous acceleration data although at high computational expenses. In this paper, we propose a novel framework, that not only improves the accuracy in HR estimation, but also achieves realtime performance by significantly reducing the complexity of system; mainly due to alleviation of the need for computationally demanding MA cancellation methods. Utilizing an spectrum estimation model (autoregressive) that suits well to the inherent PPG generation process, and benefiting from further intrinsic properties of the environment (e.g., the venous pulsation phenomenon); our framework achieves realtime and delayed (post-processed) Average Absolute Errors (AAE) of 1.19 and 0.99 Beats Per Minute (BPM) respectively, on the 12 benchmark recordings in which subjects run at speeds of up to 15 km/h maximum. Moreover, the system makes standalone implementation feasible by processing input frames (2 channel PPG and 3D ACC) in < 0.004 times of the frame duration, operating on a 3.2 GHz processor. This study provides wearable healthcare technologies with a robust framework for accurate HR monitoring; at considerably low computational costs.

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“…When some new rows are added to the measurement matrix (new samples of the image are taken), it is impossible to use the recovered samples of the previous stage and the whole of the recovery procedure should be repeated. The iterative methods such as Iterative K MAX Thresholding (IKMAX) 1 and Iterative Method with Adaptive Thresholding (IMAT) [12][13][14] are suitable for progressive Sparse Signal recovery. Whenever a new subset of samples is derived from the image, the reconstructed image in the previous stage is enhanced by adding the effect of the new samples to it.…”

Section: Introductionmentioning

confidence: 99%

Progressive sparse image sensing using Iterative Methods

Azghani

Marvasti

2012

6th International Symposium on Telecommunications (IST)

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Progressive image transmission enables the receivers to reconstruct a transmitted image at various bit rates. Most of the works in this field are based on the conventional Shannon-Nyquist sampling theory. In the present work, progressive image transmission is investigated using sparse recovery of random samples. The sparse recovery methods such as Iterative Method with Adaptive Thresholding (IMAT) and Iterative IKMAX Thresholding (IKMAX) are exploited in this framework since they have the ability for successive reconstruction. The simulation results indicate that the proposed method performs well in progressive recovery. The IKMAX has better final reconstruction than IMAT at the cost of requiring sparse signal with extra information about the sparsity number. However, the IMAT has the ability to recover the compressible signals. Furthermore, two sampling strategies, random sampling and uniform sampling are exploited. The simulations show that IMAT is a better choice for random sampling and IKMAX behaves better than IMAT in the case of uniform sampling.

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Sparse signal processing using iterative method with adaptive thresholding (IMAT)

Cited by 38 publications

References 17 publications

Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry

Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry

RealTime Heart Rate Monitoring Using Photoplethysmographic (PPG) Signals During Intensive Physical Exercises

Progressive sparse image sensing using Iterative Methods

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