ABSTRACT
The electromyography (EMG) signal provides insight into neuromuscular activity which is used in medical and technological fields. Traditional needle electrodes and surface electrodes have several drawbacks making them less suitable for portable and long-term use. In contrast, emerging capacitive electrodes offer promising features over the existing electrodes. Yet, the full potential of capacitive electrodes remains untapped due to the lack of comprehensive design optimization for consistently reliable signal quality. This study highlights the complex interplay of factors influencing correlation in capacitive EMG (cEMG) and wet surface EMG (wet sEMG) signals. The study emphasizes the importance of the surface area of capacitive electrodes, muscle force, preprocessing, and sampling frequency in understanding and improving the correlation between cEMG and wet sEMG signals, providing valuable insights for future research and applications in the field. The study reveals that the electrode area has no significant effect on the correlation. However, the correlation significantly depends on the muscle force. In addition, removing artifacts from the cEMG signal increases the correlation, especially for lower force where artifacts are significant. Again, oversampling the EMG signal above 800 Hz does not have any impact on increasing the correlation but the correlation decreases with higher inter-electrode distance (IED). In this research, the highest correlation of 82.89% (normalized-91.62%) between cEMG and sEMG has been achieved for high muscle force with a plate area of 4 cm2. Therefore, the capacitive electrode can be an alternative for EMG signal acquisition.
ABSTRACT
The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.
Subject(s)
Algorithms , Pattern Recognition, Automated , Electrodes , Electromyography/methods , Movement , Pattern Recognition, Automated/methodsABSTRACT
A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.
ABSTRACT
BACKGROUND: Diarrhoea is a frequent symptom in children with cancer, and occurs due to a composite effect of underlying disease and immunosuppression consequent to therapy, malnutrition, and non-infective aetiologies such as mucositis. In a large proportion of cases, the aetiology of diarrhoea remains unknown but is often attributed to multiple pathogens including parasites. AIM: To identify and describe the pathogens causing diarrhoea in Bangladeshi children with cancer. METHODS: Two cross-sectional pilot studies were conducted involving paediatric oncology patients with diarrhoea. Stool samples were collected from children who were hospitalised with or without being treated with chemotherapy during the study period, and had diarrhoea at any stage during their admission. In the first study, stool samples were tested by conventional microbiological methods and by polymerase chain reaction for parasites, and by immunoassays for Clostridium difficile. In the second study, conventional microbiology was conducted for bacteria and parasites including an enzyme-linked immunosorbent assay for Cryptosporidium antigen, and in a subset, immunoassays for Clostridium difficile. RESULTS: In the first study Giardia lamblia was detected in 68.5% of samples, Entamoeba histolytica in 13%, Cryptosporidium in 5.6%, non-toxigenic C. difficile in 22.4%, and other bacteria in 5.2%. In the second study, E. histolytica was detected in 10% of samples, Cryptosporidium in 4.3%, G. lamblia in 1.4%, C. difficile in 5.1%, and other bacteria in 5.7% of samples. CONCLUSION: These pilot data suggest that parasites are important aetiologies of diarrhoea in Bangladeshi children with malignancy. While molecular diagnostic tools detect an array of stool pathogens with greater sensitivity, conventional diagnostic methods are also useful.
ABSTRACT
INTRODUCTION: Malignancy is one of the leading causes of morbidity and mortality worldwide. According to GLOBOCAN 2012, an estimated 14.1 million new cancer cases and 8.2 million cancer-related deaths occurred in 2012. It is estimated that childhood malignancies are 0.5-4.6% of total malignancies. However, from the point of view of potential year lost due to childhood malignancies, it is more important than adult. MATERIALS AND METHODS: To find out the probable components for the delay in diagnosis and treatment of childhood malignancies in Bangladesh, cross-sectional observational study was done at the National Institute of Cancer Research and Hospital, Dhaka, Bangladesh, from January 2014 to June 2014. RESULTS: A total of 171 patients were included in the study. They were divided into four age groups. The mean age was 8.422 years with standard deviation ± 5.381 years and their age ranged from 2 months to 18 years. In aggregate, about 70% of the cases had to wait for more than 90 days for the treatment. About 15% had to wait for 31-60 days. Negligible percentage of patients got treatment before 30 days. Among the three components of delay, patients delay was influenced by age of the child, economic status of the family, parental education, and awareness of the parents about malignancy. CONCLUSION: More than one-third of the pediatric patients had to wait three months or more for treatment to start for various reasons. By raising awareness among the stake holders this problem can be minimized. Further studies are recommended to explore the other factors which might cause delayed referral.
