Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

Dixon, Anna M. R.; Allstot, Emily G.; Gangopadhyay, Daibashish; Allstot, D.J.

doi:10.1109/tbcas.2012.2193668

Cited by 279 publications

(170 citation statements)

References 29 publications

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“…The output of SI is then sampled 4 Includes AFE, ADC, DBE (while executing feature extraction), and bias power consumption, with power down mode disabled. 5 Off-chip LED driver. LED power consumption is subject to the SNR, skin tone of the subject and the efficiency of the LED used in the setup NA-Not applicable, NR-Not reported.…”

Section: Measurement Resultsmentioning

confidence: 99%

“…CS-based acquisition, however, suffers from the drawback of requiring a computationally intensive convex optimization process to recover the signal. In conventional CS-based acquisition systems, acquired data are transmitted over a wireline/wireless link to a base station, where the reconstruction is performed [5]. This approach however has the following drawbacks.…”

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confidence: 99%

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A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data

Pamula

Valero-Sarmiento

Yan

et al. 2017

IEEE Trans. Biomed. Circuits Syst.

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Abstract-A compressive sampling (CS) photoplethysmographic (PPG) readout with embedded feature extraction to estimate heart rate (HR) directly from compressively sampled data is presented. It integrates a low-power analog front end together with a digital back end to perform feature extraction to estimate the average HR over a 4 s interval directly from compressively sampled PPG data. The application-specified integrated circuit (ASIC) supports uniform sampling mode (1x compression) as well as CS modes with compression ratios of 8x, 10x, and 30x. CS is performed through nonuniformly subsampling the PPG signal, while feature extraction is performed using least square spectral fitting through LombScargle periodogram. The ASIC consumes 172 µW of power from a 1.2 V supply while reducing the relative LED driver power consumption by up to 30 times without significant loss of relevant information for accurate HR estimation.Index Terms-Compressive sampling (CS), heart rate (HR), Lomb-Scargle periodogram (LSP), low power, photoplethysmography.

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Section: Measurement Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data

Pamula

Valero-Sarmiento

Yan

et al. 2017

IEEE Trans. Biomed. Circuits Syst.

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“…A large part of CS theory deals with the optimum choice of undersampling rate and choice of incoherent basis functions. In some applications, feasibility of achieving up to 1 order of magnitude sampling rate reductions have been demonstrated in imager, radar, spectrum sensing, and biomedical systems [7,8,9,10]. While full signal reconstruction comes with a very large computational load, interesting emerging work involves the direct extraction of features in the digital domain from the compressed signal without prior full signal reconstruction.…”

Section: Alternative Sampling Techniques a Beyond Nyquist Througmentioning

confidence: 99%

Where Analog Meets Digital: Analog?to?Information Conversion and Beyond

Verhelst

Bahai

2015

IEEE Solid-State Circuits Mag.

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Energy efficiency, long battery life and low latency are some of the key attributes of many emerging ultralow power sensing and monitoring systems. Applications such as always-on reactive sensor systems for natural human-device interfaces and IoT for consumer and industrial applications require ultra-low power designs beyond the promises of state of the art data converters.These devices demand for a new approach to analog-digital system partitioning with the goal of significant overall reduction in energy consumption. Many IoT applications, unlike most multimedia systems, require signal information extraction or signature extraction, rather than full reconstruction of the original sensed waveforms. Under these conditions, Nyquist rate sampling may no longer offer the optimal digitization scheme. Recent work on alternative sensor digitization strategies target drastic sampling rate reduction in the ADC, while preserving the valuable relevant information (knowledge) present in the sensed signal. This paper aims to give an overview of this emerging field of analog-to-information conversion in light of various sub-Nyquist sampling techniques recently appearing in literature, as well as highlight new opportunities, challenges and applications emerging by such converters. I.Nyquist rate vs. Information rate: Over the last several decades, a growing number of signal processing architects have embraced intensive digital signal processing preceded by a standard analog frontend and analog-to-digital converter. This trend has been exacerbated by the exponential rate of miniaturization in silicon, growing complexity of signal processing algorithms, and more systematic digital design and technology porting compared to analog design in deep submicron technology nodes. The interface between analog and digital signals has as such generally been governed by sampling at or above the Nyquist sampling rate of the analog waveforms. The dimensionality of a bandlimited signal f(t) with a physical bandwidth W over a period of T is 2WT, indicating the number of samples sufficient for perfect digital signal reconstruction. Such sampling at the Nyquist rate of 2W ensures the integrity of the signal represented by samples which are Fourier series coefficients in a Fourier series expansion of function F(w) over fundamental interval [-W W] [1]. The original signal can subsequently be reconstructed by superimposing a set of orthogonal basis functions (sinc functions) weighted by the samples f(nT). Sampling the incoming signal at this rate hence guarantees that no information about the incoming signal is lost without taking into account any heuristic or a priori side information about the signal or its information content other than the physical bandwidth. While sampling at or above Nyquist rate offers a classic and straightforward approach, it can compromise overall power efficiency.

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“…Much literature exists on CS applied to biomedical signal processing, such as magnetic resonance image (MRI), electromyography (EMG), electroencephalography (EEG), electrocardiography (ECG) [2] [3] [13] [14] [15] [16] [17]. However, most papers only exploit the sparsity in one signal domain, while many biomedical signals are sparse in more than one domain.…”

Section: Introductionmentioning

confidence: 99%

“…Even more generally, some biomedical signals have structural features other than sparsity. For example, some EMG signals are sparse in both time and frequency domains [16] [17]; Multi-channel EMG signals are highly-correlated with each other [18], which can lead to a low-rank structure in the data matrix; MRI data have both a piecewise smooth structure and a low rank structure [2] [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-structural Signal Recovery for Biomedical Compressive Sensing

Liu

Vos

Gligorijević

et al. 2013

IEEE Trans. Biomed. Eng.

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Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from subNyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in different domains, piecewise smoothness, low rank, etc.We propose a framework to exploit all the available structure information. A new convex programming problem is JOURNAL NAME, VOL. X, NO. X, MONTH YEAR 2 generated with multiple convex structure-inducing constraints and the linear measurement fitting constraint. With additional a priori information for solving the underdetermined system, the signal recovery performance can be improved. In numerical experiments, we compare the proposed method with classical methods. Both simulated data and real-life biomedical data are used. Results show that the newly proposed method achieves better reconstruction accuracy performance in term of both L1 and L2 errors.

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Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

Cited by 279 publications

References 29 publications

A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data

A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data

Where Analog Meets Digital: Analog?to?Information Conversion and Beyond

Multi-structural Signal Recovery for Biomedical Compressive Sensing

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