Classification and Feature Extraction of Different Hand Movements from EMG Signal using Machine Leaning based Algorithms

Extraction of potential electromyography (EMG) features has become one of the important roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced wavelength (EWL) and enhanced mean absolute value (EMAV) are proposed. The EWL and EMAV are the modified version of wavelength (WL) and mean absolute value (MAV), which aims to enhance the prediction accuracy for the classification of hand movements. Initially, the proposed features are extracted from the EMG signals via discrete wavelet transform (DWT). The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. To examine the effectiveness of EWL and EMAV, several conventional EMG features are used in performance comparison. In addition, the efficacy of EWL and EMAV when combine with other features are also investigated. Based on the results obtained, the combination of EWL and EMAV with other features can improve the classification performance. Thus, EWL and EMAV can be considered as valuable tools for rehabilitation and clinical applications.

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques

Objective: Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Methods: Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis. Results: In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion: Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

People with disabilities correspond to about 25% of the Brazilian population. A great part of these people have physical impairments that difficult the use computer peripherals. This article presents the development of a system for detection of hand movements through the acquisition and classification of electromyographic (EMG) signals using machine learning techniques. The purpose of the proposed system is to be used by people with disabilities to control an adapted text editor. The signals are capture by surface EMG electrodes and used to the detect 4 different hand movements. In addition, a database with 3200 EMG signals generated by the hand movements was created, made by one user diagnosed with cerebral palsy and another user without diagnosed motor disabilities. Several tests were carried out, showing the good accuracy of the proposed system, with a success classification rate of 96% to 98%.

Electromyography (EMG) signals hold critical significance in biomedical research, capturing muscle electrical activity during rest and contraction in the upper limb. Their versatility in applications, particularly in human-assisting robotic tools, drives ongoing exploration and research. This paper presents an original study focusing on leveraging machine learning techniques to classify EMG datasets and efficiently control a robotic arm based on predicted gestures. Data acquisition involves strategically placing an EMG muscle sensor on the forearm to ensure precise measurement of signals associated with hand gestures and movements. Diverse classifiers including random forest, support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naïve Bayes, gated recurrent unit (GRU), long short-term memory (LSTM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), and vision transformer (ViT) are employed. Performance results are meticulously analyzed and presented in tabular format, showcasing the ViT classifier as the most successful, achieving an impressive 97.7% accuracy in robotic arm control. Notably, ANN, RNN, and CNN also exhibit high accuracy exceeding 90%. Furthermore, this work is comprehensively compared with existing literature, laying the groundwork for future advancements in human-robot interaction and cutting-edge assistive technologies that markedly enhance the quality of life for individuals with motor impairments or disabilities. The findings carry significant implications for designing and implementing intuitive, responsive robotic systems based on EMG signals.

Postherpetic neuralgia (PHN), a complication of herpes zoster, significantly impacts the quality of life of affected patients. Research indicates that early intervention for pain can reduce the occurrence or severity of PHN. This study aims to develop a predictive model and scoring table to identify patients at risk of developing PHN following acute herpetic neuralgia, facilitating informed clinical decision-making. We conducted a retrospective review of 524 hospitalized patients with herpes zoster at The First Affiliated Hospital of Zhejiang Chinese Medical University from December 2020 to December 2023 and classified them according to whether they had PHN, collecting a comprehensive set of 30 patient characteristics and disease-related indicators, 5 comorbidity indicators, 2 disease score values, and 10 serological indicators. Relevant features associated with PHN were identified using the least absolute shrinkage and selection operator (LASSO). Then, the patients were divided into a training set and a test set in a 4:1 ratio, with comparability tested using univariate analysis. Six models were established in the training set using machine learning methods: support vector machines, logistic regression, random forest, k-nearest neighbor, gradient boosting, and neural network. The performance of these models was evaluated in the test set, and a nomogram based on logistic regression was used to create a PHN prediction score table. Eight non-zero characteristic variables selected from the LASSO regression results were included in the model, including age [area under the curve (AUC)=0.812, p<0.001], Numerical Rating Scale (NRS) (AUC=0.792, p<0.001), receiving treatment time (AUC=0.612, p<0.001), rash recovery time (AUC=0.680, p<0.001), history of malignant tumor (AUC=0.539, p<0.001), history of diabetes (AUC=0.638, p<0.001), varicella-zoster virus immunoglobulin M (AUC=0.620, p<0.001), and serum nerve-specific enolase (AUC=0.659, p<0,001). The gradient boosting model outperformed other classifier models on the test set with an AUC of 0.931, 95% confidence interval (CI) (0.882-0.980), accuracy of 0.886 (95% CI 0.809-0.940). In the test set, our predictive scoring table achieved an AUC of 0.820 (95% CI 0.869-0.970) with accuracy of 0.790 (95% CI 0.700-0.864). This study presents a methodology for predicting the development of postherpetic neuralgia in shingles patients by analyzing historical case data, employing various machine learning techniques, and selecting the optimal model through comparative analysis. In addition, a logistic regression model has been used to create a scoring table for predicting the postherpetic neuralgia.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Electromyography (EMG) signals are commonly used in prosthetic limb studies. We have proposed a system to detect six basic hand movements using unsupervised and supervised classification algorithms. In this study, two-channel EMG recordings belonging to six different hand movements are analyzed and the performance of the wavelet-based features for hand movement clustering and classification are examined for six subjects (three females and three males). The approximation and detail components are obtained by four-level symmetric wavelet transform. The energy, mean, standard deviation, and entropy values of the wavelet components are calculated and the feature sets are generated. After feature extraction, feature set dimensionality is reduced using principal component analysis, and then the k-nearest neighbor method and k-means clustering are applied for classification and clustering, respectively. The analyses are performed subject-specifically and gender-specifically. Thus, it is possible to evaluate the gender effect on classification performances. Subject-specific hand movements were detected with accuracy in the range of 86.33–100%. Gender-specific hand movements were detected with an accuracy of 96.67% for males and 92.78% for females. The classification and clustering results support each other. It was observed that the samples of hand movements that were classified incorrectly were concentrated in the same clusters. Similarly, it was found that the hand movements that were easily detected were homogeneously clustered.

Osteoporotic fractures (OPF) pose a public health issue, imposing significant burdens on families and societies worldwide. Currently, there is a lack of comprehensive and validated risk assessment models for OPF. This study aims to develop a model to assess and predict the risk of OPF in Qingdao City, China. From January 2021 to January 2023, we recruited 84 osteoporotic patients diagnosed with fractures from Qingdao University Affiliated Hospital, Qingdao Municipal Hospital, Qingdao Hiser Hospital Affiliated of Qingdao University, and Qingdao Central Hospital as the experimental group. In addition, 112 osteoporotic patients without fractures were recruited as the control group. In this study, we employed seven machine learning models, namely Adaboost, random forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Gradient Boosting Decision Trees (GBDT), to analyze the risk factors influencing the occurrence of OPF. Next, we plotted receiver operating characteristic (ROC), Precision-Recall (PR), and calibration curves to evaluate the predictive values of the different risk assessment models for OPF. Among the seven models built based on the training set data, the Adaboost model showed area under the curve (AUC), sensitivity, and specificity values close to 1, indicating the best classification performance. In the test set, the AUC values for the RF, SVM, LR, KNN, NB, AdaBoost, and GBDT models were 0.936, 0.905, 0.88, 0.93, 0.862, 0.939, and 0.859, respectively (p < 0.001). All sensitivity and specificity values for these models were higher than 0.8, with sensitivity and specificity values of the Adaboost model closest to 1. Additionally, six models had an area under the Precision-Recall curve (prAUC) values higher than 0.9, except KNN at 0.284 (p < 0.001). The calibration curves of the seven models did not significantly deviate from the ideal curve, indicating acceptable discriminative ability and predictive performance of the predictive model. All results showed that trabecular bone score (TBS) was the most important variable affecting the model, followed by osteocalcin (OST) and hunchback. Given the various clinical data from patients with OPF, we assessed and demonstrated the good predictive performance of our risk predictive models. This model will enable us to take timely intervention measures to reduce the incidence of OPF and improve patient prognosis.

Background Malaria remains a major cause of illness and death among children under five in Nigeria, despite efforts to control transmission. Accurate and reliable prediction of malaria outbreaks is crucial for health authorities to take timely measures. This study aims to identify the most robust machine learning classification algorithms for predicting the status of malaria in children under five (0-59 months). Methods The 2021 Nigeria Malaria Indicator Survey (NMIS) included 10,655 children under five who were tested for malaria using the Rapid Diagnostic Test (RDT). Various machine learning models were explored, including Decision Trees, K-Nearest Neighbor, Naïve Bayes, Random Forest, Support Vector Machines, and Survey Logistic Regression, and their performance was evaluated through metrics such as accuracy, AUC, balanced accuracy, F1-Score, negative predictive value, precision, sensitivity, and specificity. Results Random Forest (RF) is the most robust and balanced classification model due to its superior accuracy (79%), precision (77%), recall (62%), F1-score (69%), and AUC (80%). Support Vector Machine (SVM) also demonstrated strong performance, particularly in accuracy (74%) and AUC (80%). Survey Logistic Regression (SLR) and Decision Tree (DT) offered moderate results but fell short compared to RF and SVM, indicating the need for further optimization. Naive Bayes (NB) and K-Nearest Neighbors (KNN) had limitations, making them less reliable for this task. Conclusion In conclusion, the study reveals that RF and SVM are the best classification models for predicting malaria status in children under five years old. RF is reliable and balanced, while SVM is preferred for recall. SLR and DT show potential but require optimization. NB and KNN have significant performance gaps, making them less suitable. These findings will help policymakers and malaria intervention programs address key factors, enabling more targeted public health interventions to reduce the malaria burden on young children and improve the well-being of vulnerable populations in Nigeria.

Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer

Single-cell RNA-seq overcomes the shortcomings of conventional transcriptome sequencing technology and could provide a powerful tool for distinguishing the transcriptome characteristics of various cell types in biological tissues, and comprehensively revealing the heterogeneity of gene expression between cells. Many Intelligent Computing methods have been presented to infer gene regulatory network (GRN) with single-cell RNA-seq data. In this paper, we investigate the performances of seven classifiers including support vector machine (SVM), random forest (RF), Naive Bayesian (NB), GBDT, logical regression (LR), decision tree (DT) and K-Nearest Neighbor (KNN) for solving the binary classification problems of GRN inference with single-cell RNA-seq data (Single_cell_GRN). In SVM, three different kernel functions (linear, polynomial and radial basis function) are utilized, respectively. Three real single-cell RNA-seq datasets from mouse and human are utilized. The experiment results prove that in most cases supervised learning methods (SVM, RF, NB, GBDT, LR, DT and KNN) perform better than unsupervised learning method (GENIE3) in terms of AUC. SVM, RF and KNN have the better performances than other four classifiers. In SVM, linear and polynomial kernels are more fit to model single-cell RNA-seq data.

Fatigue is important in aviation because the risks associated with fatigue pose a threat to many people. The purpose of this study is to investigate the possibility of using a wearable ECG device to detect mental fatigue in pilots. 22 healthy individuals were recruited from Beihang University and each subject wore a Polar H10 heart rate band with a Polar IGNITE sports watch to record heart rate variability (HRV) data. To stimulate the fatigue state, each subject performed a simulated flight experiment of 1h and the number of blinks was recorded by the face recognition method as a label for fatigue. HRV data and blink frequency were collected every 5 minutes throughout the experiment. After feature selection, four HRV indicators SDNN, LF_HF, LF and VLF were extracted for subsequent analysis. Five machine learning algorithms: support vector machine (SVM), Random Forest, AdaBoost, K-nearest neighbor (KNN), Naïve Bayes (NB) were used to build a classifier for automatic detection of pilot fatigue. Naïve Bayes (NB) had the best performance and had an average CV F1 score of 0.9065 on the training set and 0.9250 on the test set.