Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.

Feature reduction and selection for EMG signal classification

Over the past century, hydrogels have emerged as effective materials for an immense variety of applications. The unique network structure of hydrogels enables very high levels of hydrophilicity and biocompatibility, while at the same time exhibiting the soft physical properties associated with living tissue, making them ideal biomaterials. Stimulus-responsive hydrogels have been especially impactful, allowing for unprecedented levels of control over material properties in response to external cues. This enhanced control has enabled groundbreaking advances in healthcare, allowing for more effective treatment of a vast array of diseases and improved approaches for tissue engineering and wound healing. In this extensive review, we identify and discuss the multitude of response modalities that have been developed, including temperature, pH, chemical, light, electro, and shear-sensitive hydrogels. We discuss the theoretical analysis of hydrogel properties and the mechanisms used to create these responses, highlighting both the pioneering and most recent work in all of these fields. Finally, we review the many current and proposed applications of these hydrogels in medicine and industry.

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Stimulus-responsive hydrogels: Theory, modern advances, and applications

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

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Surface Electromyography Signal Processing and Classification Techniques

The detection of specific proteins as biomarkers of disease, health status, environmental monitoring, food quality, control of fermenters and civil defence purposes means that biosensors for these targets will become increasingly more important. Among the technologies used for building specific recognition properties, molecularly imprinted polymers (MIPs) are attracting much attention. In this critical review we describe many methods used for imprinting recognition for protein targets in polymers and their incorporation with a number of transducer platforms with the aim of identifying the most promising approaches for the preparation of MIP-based protein sensors (277 references).

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The rational development of molecularly imprinted polymer-based sensors for protein detection.

Enzyme inhibition-based biosensors for food safety and environmental monitoring

Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this paper. Generally, WGN is difficult to be removed using typical filtering and solutions to remove WGN are limited. In addition, noise removal is an important step before performing feature extraction, which is used in EMG-based recognition. This research is aimed to present a novel feature that tolerate with WGN. As a result, noise removal algorithm is not needed. Two novel mean and median frequencies (MMNF and MMDF) are presented for robust feature extraction. Sixteen existing features and two novelties are evaluated in a noisy environment. WGN with various signal-to-noise ratios (SNRs), i.e. 20-0 dB, was added to the original EMG signal. The results showed that MMNF performed very well especially in weak EMG signal compared with others. The error of MMNF in weak EMG signal with very high noise, 0 dB SNR, is about 5-10% and closed by MMDF and Histogram, whereas the error of other features is more than 20%. While in strong EMG signal, the error of MMNF is better than those from other features. Moreover, the combination of MMNF, Histrogram of EMG and Willison amplitude is used as feature vector in classification task. The experimental result shows the better recognition result in noisy environment than other success feature candidates. From the above results demonstrate that MMNF can be used for new robust feature extraction. Index Terms—Electromyography (EMG), Feature extraction, Pattern recognition, Robustness, Man-machine interfaces. —————————— a ——————————

A Novel Feature Extraction for Robust EMG Pattern Recognition

Rich useful information can be obtained from the muscles and researchers can use such information in a wide class of clinical and engineering applications by measuring surface electromyography (EMG) signals (Merletti & Parker, 2004). Normally, EMG signals are acquired by surface electrodes that are placed on the skin superimposed on the targeted muscle. In order to use the EMG signal as a diagnosis signal or a control signal, a feature is often extracted before performing analysis or classification stage (Phinyomark et al., 2012a) because a lot of information, both useful information and noise (Phinyomark et al., 2012b), is contained in the raw EMG data. An EMG feature is a distinct characteristic of the signal that can be described or observed quantitatively, such as being large or small, spiky or smooth, and fast or slow. Generally, EMG features can be computed in numerical form from a finite length time interval and can change as a function of time, i.e. a voltage or a frequency. They can be computed in several domains, such as time domain, frequency domain, timefrequency and time-scale representations (Boostani & Moradi, 2003). However, frequencydomain features show the better performance than other-domain features in case of the assessing muscle fatigue (Al-Mulla et al., 2012). Mean frequency (MNF) and median frequency (MDF) are the most useful and popular frequency-domain features (Phinyomark et al., 2009) and frequently used for the assessment of muscle fatigue in surface EMG signals (Cifrek et al., 2009).

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The Usefulness of Mean and Median Frequencies in Electromyography Analysis

Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Objective/Hypothesis: The objectives were to test the hypothesis that synchronous contraction of the thyrohyoid muscle by electrical stimulation during swallowing would improve dysphagia resulting from reduced laryngeal elevation and to evaluate the effectiveness of the synchronous electrical stimulator. Study Design: Prospective study. Methods: Eleven male and 12 female patients (age range, 35–87 y) with moderate to severe degree of dysphagia resulting from reduced laryngeal elevation who did not respond to medical treatment were treated by stimulating synchronous contraction of the thyrohyoid muscle during swallowing with the synchronous electrical stimulator. Treatment was given daily for 4 hours until criteria for improved swallow were fulfilled or other intervention was deemed necessary. Follow-up was performed monthly (range, 3–33 mo). Results: Of the 23 patients, 20 showed marked improvement at the first course of treatment. The duration of stimulation varied from 2 to 4 days (median duration, 2 d) in patients with moderate dysphagia and from 3 to 30 days (median duration, 6 d) in patients with severe dysphagia. Three patients showed failed synchronous electrical stimulator treatment, one owing to device failure and two requiring gastrostomy for feeding. Six patients who achieved improved swallow criteria relapsed at 2 to 9 months after the first treatment course and were successfully treated with an additional course of synchronous electrical stimulation. Conclusions: The study supports the hypothesis that stimulating synchronous contraction of the thyrohyoid muscle by synchronous electrical stimulator during swallowing improves dysphagia resulting from reduced laryngeal elevation. Synchronized electrical stimulation has the advantages of noninvasiveness and actively assisting swallowing and can be used as an alternative treatment. The synchronized feature of the synchronous electrical stimulator helps to restore normal swallowing mechanism and decreases the incidence of nasogastric tube insertion and gastrostomy. Key Words: Dysphagia, stimulation, aspiration, rehabilitation, nutrition. Laryngoscope, 112:2204–2210, 2002

Chusak Limsakul

Papers

Feature reduction and selection for EMG signal classification

A Novel Feature Extraction for Robust EMG Pattern Recognition

The Usefulness of Mean and Median Frequencies in Electromyography Analysis

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Synchronized electrical stimulation in treating pharyngeal dysphagia.