Chusak Limsakul

Feature reduction and selection for EMG signal classification

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.

Synchronized Electrical Stimulation in Treating Pharyngeal Dysphagia

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.

Label-free capacitive immunosensor for microcystin-LR using self-assembled thiourea monolayer incorporated with Ag nanoparticles on gold electrode.

A label-free immunosensor based on a modified gold electrode incorporated with silver (Ag) nanoparticles (NPs) to enhance the capacitive response to microcystin-LR (MCLR) has been developed. Anti-microcystin-LR (anti-MCLR) was immobilized on silver nanoparticles bound to a self-assembled thiourea monolayer. Interaction of anti-MCLR and MCLR were directly detected by capacitance measurement. Under optimum conditions, MCLR could be determined with a detection limit of 7.0 pgl(-1) and linearity between 10 pgl(-1) and 1 microgl(-1). The immobilized anti-MCLR on self-assembled thiourea monolayer incorporated with silver nanoparticles was stable and good reproducibility of the signal could be obtained up to 43 times with an R.S.D. of 2.1%. Comparing to the modified electrode without silver nanoparticles it gave 1.7-fold higher sensitivity and lower limit of detection. The developed immunosensor was applied to analyze MCLR in water samples and the results were in good agreement with those obtained by high-performance liquid chromatography (HPLC) (P < 0.05).

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification 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.

Comparison of surface plasmon resonance and capacitive immunosensors for cancer antigen 125 detection in human serum samples.

This paper presents a comparison between surface plasmon resonance (SPR) and capacitive immunosensors for a flow injection label-free detection of cancer antigen 125 (CA 125) in human serum. Anti-CA 125 was immobilized on gold surface through a self-assembled monolayer. Parameters affecting the responses of each system were optimized. Under optimal conditions, SPR provided a detection limit of 0.1 U ml(-1) while 0.05 U ml(-1) was obtained for the capacitive system. Linearity for SPR was between 0.1 and 40 U ml(-1) and 0.05-40 U ml(-1) for capacitive system. These immunosensors were applied to analyze CA 125 concentrations in human serum samples and compared with conventional enzyme linked fluorescent assay (ELFA). Both systems showed good agreement with ELFA (P<0.05). Moreover, these immunosensors were very stable and provided good reproducible responses after regeneration, up to 32 times for SPR and 48 times for capacitive system with relative standard deviation lower than 4%. The SPR immunosensor provided advantages in term of fast response and real-time monitoring while capacitive immunosensor offered a sensitive and cost-effective method for CA 125 detection.

An optimal wavelet function based on wavelet denoising for multifunction myoelectric control

The aim of this study was to investigate and select the wavelet function that is optimum to denoise the surface electromyography (sEMG) signal for multifunction myoelectric control. Wavelet denoising algorithm has been used to find the optimal wavelet function for removing white Gaussian noise (WGN) at various signal-to-noise ratios (SNRs) from sEMG signals. A total of 53 wavelet functions were used in evaluation of the denoised performance. The wavelets are Daubechies, Symlets, Coiflet, BiorSplines, ReverseBior, and Discrete Meyer. Universal thresholding method has been used to estimate threshold value. Soft, hard, hyperbolic, and garrote thresholding are applied. Evaluations of the performance of these algorithms are mean squared error (MSE). The results show that the best wavelet functions for denoising are the first order of Daubechies, BioSplines, and ReverseBior wavelets (db1, bior1.1, rbio1.1). Various families can be used except the third order of decomposition of BiorSplines (bior3.1, bior3.3, bior3.5, bior3.7, bior3.9) and Discrete Meyer (dmey) are not recommended to use in wavelet denoising of sEMG signal. In addition, performance of soft thresholding is better than the others modified thresholding.

The Usefulness of Mean and Median Frequencies in Electromyography Analysis

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).

Label-free capacitive immunosensors for ultra-trace detection based on the increase of immobilized antibodies on silver nanoparticles

Detection of ultra-trace amounts of antigens by label-free capacitive immunosensors was investigated using electrodes modified with silver nanoparticles (AgNPs) that allows for an increase in the amount of immobilized antibodies. The optimal amount of AgNPs that provided the highest immobilization yield was 48 pmol (in 2.0 mL). The performances of immunosensor electrodes for human serum albumin prepared with AgNPs, were compared to electrodes prepared with gold nanoparticles. The two systems provided the same linear range (1.0×10(-18) to 1.0×10(-10) M) and detection limit (1.0×10(-18) M). The system with AgNPs was used to analyze albumin in urine samples and the results agreed well with the immunoturbidimetric assay (P>0.05). Electrodes modified with AgNPs and appropriate antibodies were tested for their performances to detect analytes of different sizes. For a macromolecule (human serum albumin) the incorporation of AgNPs improved the detection limit from 100 to 1 aM. For small molecules, microcystin-LR and penicillin G, the detection limits were lowered from 100 and 10 fM to 10 and 0.7 fM, respectively. The high sensitivity and very low detection limits are potentially useful for the analysis of toxins or residues present in samples at ultra-trace levels and this method could easily be applied to other affinity pairs.

A Comparative Study of Wavelet Denoising for Multifunction Myoelectric Control

The aim of this study was to investigate the application of wavelet denoising in noise reduction for multifunction myoelectric control system. Six upper limb motions including hand open, hand close, wrist extension, wrist flexion, pronation, and supination. For each motion, two channels of electrodes were applied. A comparative study of four classical denoising algorithms including universal thresholding, SURE thresholding, hybrid thresholding, and minimax thresholding have been used to remove white Gaussian noise at various signal-to-noise ratios (SNRs) from EMG signals. Applications of soft and hard thresholding as well as threshold rescaling methods were considered and the whole procedures of noise reduction were applied with different wavelet functions and different decomposition levels. Evaluations of the performance of noise reduction are determined using mean squared error (MSE). The results show that Daubechies wavelet with second orders (db2) provides marginally better performance than other possibilities. Suitable number of decomposition levels is four. Universal and soft thresholding is the best of wavelet denoising algorithms from eight possible denoising processes under investigation. In addition, the threshold using a level-dependent estimation of level noise showed better than others.

A Review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition

Abstract Electric Power Wheelchairs (EPWs) are becoming increasingly important in assistive technology and rehabilitation devices. Normally EPWs are controlled by a joystick. However, this may not be suitable for disabled people who lack full control of their upper-limbs. Recent advances in the control of EPWs based on electromyography (EMG) signals are able to meet the needs of users with restricted limb movement and provide high performance control. Hence, EPWs controlled by EMG signals are highly appropriate for elderly and disabled users. The purpose of this article is to review the state-of-the-art of EMG controlled EPWs and to present the achievements so far in this technology. A study of a variety of methods for EMG-based control in literature was studied here. Two types of control methods for EPWs, pattern recognition and hybrid recognition systems are discussed. Four major criteria are applied to compare the quality of control resulting from the use of these control methods: Accuracy of control, response time or real-time operation, robustness, and intuitiveness of control. Based on these four criteria, the use of the support vector machine classifier using features based on the time domain such as mean absolute value, waveform length, and zero crossing are suggested for the pattern recognition method. Furthermore, a combination of the pattern recognition and non-pattern recognition methods is recommended in order to increase the control commands by use of a small number of muscle positions.