Robust compressive sensing algorithm for wireless surface electromyography applications

Balouchestani, Mohammadreza; Krishnan, Sridhar

doi:10.1016/j.bspc.2015.04.012

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…Due to the scarcity of research into the compression of EMG signals of maternal patients, studies of EMG signals, regardless of disease type, were used for comparison (refer to Section 4.2). The studies by Balouchestani [14], Itiki [15], Norris [16], Berger [17], Filho [18], and Trabuco [7] were selected. Specific summary details of the selected researches are shown in Table 1.…”

Section: Prior Research On Term and Pre-term Emg Signalsmentioning

confidence: 99%

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Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

Cho

Lee

2017

Applied Sciences

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An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive size of the electromyography (EMG) signal impede the practical application of such medical information systems. Therefore, this research proposes an EMG uterine monitoring transmission solution (EUMTS), a lossless efficient real-time EMG transmission solution that solves such problems through efficient EMG data lossless compression. EMG data samples obtained from the Physionet PhysioBank database were used. Solution performance comparisons were conducted using Lempel-Ziv Welch (LZW) and Huffman methods, in addition to related researches. The LZW and Huffman methods showed CRs of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm. This was relatively high compared to related researches, even when considering that those researches were lossy whereas the proposed research was lossless. The results also showed that the proposed algorithm contributes to a reduction in battery consumption by reducing the wake-up time by 1470.6 ms. Therefore, EUMTS will contribute to providing an efficient wireless transmission environment for the prediction of pre-term delivery, enabling immediate interventions by medical professionals. Another novel point of EUMTS is that it is a lossless algorithm, which will prevent any misjudgement by clinicians because the data will not be distorted. Pre-term babies may receive point-of-care immediately after birth, preventing exposure to the development of disabilities.

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Section: Prior Research On Term and Pre-term Emg Signalsmentioning

confidence: 99%

“…Balouchestani [14] Batch processing algorithm based on analog-compressed sensing (CS) for the receiver side of an ultra-low-power wearable and wireless surface EMG (sEMG) sensor.…”

Section: Study Notesmentioning

confidence: 99%

Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

Cho

Lee

2017

Applied Sciences

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“…It is important to mention that biomedical imaging such as MRI was among the first and widely used applications of CS concept based on the advantages of using 2D DFT domain. Still, the applications on other types of biomedical signals are much less frequent in the literature, due to their specific nature [19][20][21].…”

Section: Compressive Sensing Theory-brief Overviewmentioning

confidence: 99%

Multi-base compressive sensing procedure with application to ECG signal reconstruction

Orović

Stanković

Beko

2021

EURASIP J. Adv. Signal Process.

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Standard compressive sensing (CS) scenario assumes a single sparsifying basis used to reconstruct the signals from a small set of incoherent measurements. However, in many cases, the signal cannot be sparsely represented using a single transformation. Particularly, in ECG signal analysis, each signal segment is specific in nature and reflects different physical phenomena. Hence, using the same transformation for all segments may be inappropriate for efficient analysis and reconstruction. Moreover, in the CS scenario, it would be necessary to combine different transforms to achieve compact signal support and to provide successful reconstruction from randomly under-sampled data. This work proposes a hybrid CS reconstruction algorithm that combines different transform basis, based on the concept of orthogonal matching pursuit. The performance of the proposed approach is verified experimentally using the combination of the Fourier and the Hermite transform on the real ECG signals.

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“…Where the input signal vector X of length N is K-sparse, Y is the observation vector of length M and the Gaussian sensing matrix Φ is of size M × N . Compressive sensing is significantly suited for K-sparse signals, where signal vector X of length N is represented by significant K nonzero coefficients [37]. The input signal X is sparse when only a low number of coefficients are nonzero.…”

Section: Compressive Sensingmentioning

confidence: 99%

Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device

Gibson

Amira

Ramzan

et al. 2017

Biomedical Signal Processing and Control

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There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve

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Robust compressive sensing algorithm for wireless surface electromyography applications

Cited by 14 publications

References 22 publications

Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

Multi-base compressive sensing procedure with application to ECG signal reconstruction

Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device

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