Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

Physiological signal based detection of driver hypovigilance using higher order spectra

Wearable e-health system, are frequently used for monitoring biomedical signals. These devices need to have advanced and applicable methods of feature selection and classifications for real time applications. Electromyogram (EMG) signal records the movement of the human muscle. EMG signal processing techniques aim to achieve the actual signal and among others, detect the state of signals related to positive and negative emotional expression. In our study, the data collected is from the facial muscle activity that is produced by the emotion of the facial expressions. The key challenge is in finding an accurate classification method of the measured signals. This paper investigates the promising techniques for the detection and classification of EMG signal using machine-learning theory. Here, we demonstrated Support Vector Machine (SVM) is an optimal method for classification of facial surface Electromyogram (sEMG) signal associated to pain dataset. The test results and the methods are able to analyze the patterns recognition of facial EMG signal classification. The result and the findings 99% accuracy with SVM method adds value on the classification algorithms of our EMG signal acquisitions platform.

Electromyogram (EMG) signals are biopotential signals, which occur as the result of electro-chemical circumstances dependent on contraction of muscles. In this study, a new method is presented to determine the motor unit action potentials of EMG signals recorded with surface electrodes. Compared to needle EMG signals, surface EMG signals have more active noise component. Results, which are gained by the process train including wavelet de-noising method for noise anlysis at first step and then ICA algorithms to provide independent components, form four different datas to determine the motor unit action potentials. Performed study, which is aimed to make ineffective the problems that make the analysis of EMG signals difficult and then to reach the desired results, provides simultaneous comparison possibility submitting four results. After the determination of the results, with the feedback between input and output, examination of accuracy ratio of results are possible and this examination eliminates the questions about the accuracy of the study.

Using electromyogram (EMG) signals as a human-machine interface to control the myoelectric prostheses is consummately advanced. Nevertheless, reported studies present high classification performance from the EMG signal captured in laboratory conditions. There is a very challenging suffix regarding untrained condition. Limb position change causes high variation in EMG signals resulting in high misclassification rate in the untrained position. The goal of this work is to indicate the most relevance EMG channel, which is robust to the effect of a limb position change. The EMG signals from four different object shapes with five different object placement positions were examined using linear discriminant analysis. To determine the best EMG channel subset of all possible combination of selective channels, we reduced the number of EMG channels from 12 to 3. The results have shown that the minimum classification error rate (2.09%) across five subjects can be achieved by use of eight optimal EMG channels. Additionally, our results suggest the EMG channel number 3 (Triceps brachii), 7 (Extensor digitorum communis), and 9 (Brachioradialis) as the most three emphasized channels that contain the rich information of hand grasping and generalize limb position change.

The algorithms based on the technique of optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT-based algorithms remains high at the current stage of their development. This stimulates the development of the so-called natural thresholding (NT) algorithm and its variants in this paper. The family of NT algorithms is developed through the first-order approximation of the so-called regularized optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding model, and thus the computational cost for this family of algorithms is significantly lower than that of the OT-based algorithms. The guaranteed performance of NT-type algorithms for signal recovery from noisy measurements is shown under the restricted isometry property and concavity of the objective function of regularized optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding model. Empirical results indicate that the NT-type algorithms are robust and very comparable to several mainstream algorithms for sparse signal recovery.

System level design considerations for high altitude platforms operating in the mm‐wave bands are examined. Propagation effects in these bands are outlined, followed by a brief introduction to different platform scenarios. Ground‐based and platform‐based fixed wireless access scenarios are considered, and it is shown that using a platform, a single base station can supply a much larger coverage area than a terrestrial base station. The effects on performance of platform displacement from its desired location with both fixed and steerable antennas are also examined. It is shown that steerable antennas are of most use when fixed stations are immediately below the platform, with no benefit for fixed stations on the edge of coverage. The bandwidths required to serve several traffic distributions (suburbs and city centre based) are evaluated using the Shannon equation. It is shown that capacity can be constrained when users are located in the city centres, despite longer line of sight paths to users out in the suburbs. The effects of temporal changes in the spatial traffic distribution are investigated. It is shown that bandwith requirements can be reduced if the platform moves to track these changes. Copyright © 2001 John Wiley &amp; Sons, Ltd.

This chapter is a top level treatment of system level considerations concerning high-contrast imaging coronagraphy from various approaches. Considerations include target properties and their effect on fundamental imaging requirements such as resolution, bandpass and contrast. I compare the advantages and disadvantages of observing from the ground, balloons, rockets, and satellites. I outline different methods of starlight suppression presently being explored and compare them based on the crucial performance metrics of spatial and chromatic response. I present components for wavefront control and techniques for wavefront sensing. Finally, a brief overview of efforts that have been undertaken to implement these systems is given.

This two-part paper reviews recent innovations in circuit design that have accelerated the miniaturization of sensor nodes. In this second part of the paper, we focus on key building blocks of miniaturized sensor nodes, such as data transceivers, energy harvesters, power management units, and digital logic circuits. System level design considerations are also discussed to provide guidelines for the design of a miniaturized system. As an example prototype design, a 2.7-cm3 global navigation satellite system (GNSS) logger is proposed. This paper includes a die-stacked sensor platform composed of an ARM cortex M0 processor, energy harvester, power management unit, solar cell, optical receiver, sensor layer, and RF transmitter that exploits the discussed design techniques for ultra-low power operation. The GNSS logger can store GNSS signals of >1 k positions on a single battery charging without additional energy harvesting.

The aim of this study is to analyze Electromyogram (EMG) signals in Rapid Eye Movement (REM) sleep using different techniques to detect the level of normality and abnormality of normal and abnormal (patients with a lack of REM sleep atonia) subjects and predict the development of Parkinson’s disease in abnormal subjects. Quantitative elctromyogram (EMG) signal analysis in the frequency domain using classical power spectrum analysis techniques have been well documented over the past decade. Yet none of these [sic] work have been done on EMG during Rapid Eye Movement (REM) Stage of sleep. In this work three techniques for classifying chin movement via EMG signals during sleep is presented. Three methods (Autoregressive modeling, Cepstrum Analysis and Wavelet Analysis) for extracting features from EMG signal during sleep and a classification algorithm (Linear Discriminant Analysis (LDA)) were analyzed and compared. EMG data are used to detect and describe different disease processes affecting sleep. Rapid Eye Movement Behavior Disorder (RBD) is an example of EMG abnormality in which patients lose their muscle control while in REM stage of sleep resulting in physically acting out their dreams. An adaptive segmentation based on Recursive Least Square (RLS) algorithm was analyzed. This algorithm was used to segment the non-stationary EMG signal into locally stationary components, which were then autoregressive modeled using the Burg-Lattice method. The cepstral measurements described was used and applied to modify the coefficients computed from the autoregressive (AR) model. Yet due to the nature of the EMG, frequency analysis cannot be used to approximate a signal whose properties change over time. To address this problem a time varying feature representation is necessary for analysis to extract useful infomration from the signal. As a consequence Wavelet coefficients were computed using discrete and continuous wavelet transforms. Furthermore, the classification performance of the above three feature sets were investigated for the two classes (Normal and Abnormal). Results showed wavelet analysis compared to AR modeling and cepstrum analysis is a better assessment in finding EMG abnormalities during sleep. However, these methods may be useful in distinguishing EMG patterns that predict the emergence of Parkinson disease in humans.

The electromyogram (EMG) signal has been used as the command input to myoelectric prostheses. A common control scheme is based on classifying the EMG signals from multiple electrodes into one of several distinct classes of user intent/function. In this work, we investigated the use of EMG whitening as a preprocessing step to EMG pattern recognition. Whitening is known to decorrelate the EMG signal, with improved performance shown in the related applications of EMG amplitude estimation and EMG-torque processing. We reanalyzed the EMG signals recorded from 10 electrodes placed circumferentially around the forearm of 10 intact subjects and 5 amputees. The coefficient of variation of two time-domain features--mean absolute value and signal length--was significantly reduced after whitening. Pre-whitened classification models using these features, along with autoregressive power spectrum coefficients, added approximately five percentage points to their classification accuracy. Improvement was best using smaller window durations (<100 ms).

to the editor: The electromyogram (EMG) as an indirect measure of motor unit activity has proved invaluable in motor control studies. Increasingly, the application of frequency domain techniques to EMG signals has provided important insight into the structure and function of the human motor system.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.

A three-electrode human-computer interaction system, based on digital processing of the Electromyogram (EMG) signal, is presented. This system can effectively help disabled individuals paralyzed from the neck down to interact with computers or communicate with people through computers using point-and-click graphic interfaces. The three electrodes are placed on the right frontalis, the left temporalis and the right temporalis muscles in the head, respectively. The signal processing algorithm used translates the EMG signals during five kinds of facial movements (left jaw clenching, right jaw clenching, eyebrows up, eyebrows down, simultaneous left & right jaw clenching) into five corresponding types of cursor movements (left, right, up, down and left-click), to provide basic mouse control. The classification strategy is based on three principles: the EMG energy of one channel is typically larger than the others during one specific muscle contraction; the spectral characteristics of the EMG signals produced by the frontalis and temporalis muscles during different movements are different; the EMG signals from adjacent channels typically have correlated energy profiles. The algorithm is evaluated on 20 pre-recorded EMG signal sets, using Matlab simulations. The results show that this method provides improvements and is more robust than other previous approaches.

Electrocardiogram (ECG) is a vital signal which represents the state of health of astronauts in a manned spaceflight mission and hence must be continuously acquired and transmitted throughout the mission. Telemetry bandwidth is a premium resource in such applications. Traditional method of sampling at Nyquist rate is highly bandwidth inefficient. Hence it is advantageous to use Compressive sensing (CS) technique to optimize data at measurement point itself. Conventional CS techniques employ computationally intensive measurement matrices which are not hardware efficient for both acquisition as well as recovery. In this work, a hardware efficient scheme with use of a sparse binary measurement matrix is proposed. The signal is recovered from the compressive measurement using a constraint function based on inverted Laplace distribution function. Gradient decent method is used to recursively recover the original ECG signal from the compressive measurements in an efficient manner. Apart from this, a projection scheme is also proposed to minimize the recovery error. The proposed scheme was extensively evaluated with different ECG samples with different compression ratios. Finally, the proposed scheme was benchmarked with Approximated L0 norm based method and it is found to perform more efficiently in compressively sensing and recovery of ECG signals.

In recent years, there has been extensive interest in the revelation of different technologies for artificial neural network (ANN) application in various classification of biomedical signals. Several publications have focused on diverse applications of ANN techniques for biomedical signals classification, detection, and processing. This review work gives an overview on the multilayer perceptron (MLP) technique of ANN and how this technology is useful for the classification of EMG signal. This paper will be interesting for those researchers who are studying the classification EMG signal for tremor detection in Parkinson’s disease. Many researchers have worked and also working on Parkinson’s disease tremor by using EMG signal and classifying its feature using ANN. Among those various techniques, MLP technique is highlighted in this work for the classification of EMG signal. The core center of this paper is to review the evolution and research works related to the topic mentioned above.