sEMG feature selection and classification using SVM-RFE

Tosin, Maurício Cagliari; Majolo, Mariano; Chedid, Raissan; Balbinot, Alexandre

doi:10.1109/embc.2017.8036844

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…The optimal features should be extracted by some criteria. Tosin et al [ 42 ] demonstrated that RFE was a powerful feature selection algorithm. However, the output was a list of ranks in separability without detailed values.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, embedded methods rely on criteria that are generated during the classifier training process. Examples of embedded techniques are the support vector machine (SVM)-based Recursive Feature Elimination (RFE) [ 42 ] and the linear discriminant analysis (LDA)-based Fisher’s Discriminant (FD) function [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…A critical problem is the functionality requirement for accurate control. (1) The recognition for more motions or gestures can increase degree-of-freedom [ 37 , 42 ]. (2) A state-machine based control and a proportional control could meet the requirement [ 48 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Sun

Zhang

Zhao

et al. 2018

Sensors

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The novel human-computer interface (HCI) using bioelectrical signals as input is a valuable tool to improve the lives of people with disabilities. In this paper, surface electromyography (sEMG) signals induced by four classes of wrist movements were acquired from four sites on the lower arm with our designed system. Forty-two features were extracted from the time, frequency and time-frequency domains. Optimal channels were determined from single-channel classification performance rank. The optimal-feature selection was according to a modified entropy criteria (EC) and Fisher discrimination (FD) criteria. The feature selection results were evaluated by four different classifiers, and compared with other conventional feature subsets. In online tests, the wearable system acquired real-time sEMG signals. The selected features and trained classifier model were used to control a telecar through four different paradigms in a designed environment with simple obstacles. Performance was evaluated based on travel time (TT) and recognition rate (RR). The results of hardware evaluation verified the feasibility of our acquisition systems, and ensured signal quality. Single-channel analysis results indicated that the channel located on the extensor carpi ulnaris (ECU) performed best with mean classification accuracy of 97.45% for all movement’s pairs. Channels placed on ECU and the extensor carpi radialis (ECR) were selected according to the accuracy rank. Experimental results showed that the proposed FD method was better than other feature selection methods and single-type features. The combination of FD and random forest (RF) performed best in offline analysis, with 96.77% multi-class RR. Online results illustrated that the state-machine paradigm with a 125 ms window had the highest maneuverability and was closest to real-life control. Subjects could accomplish online sessions by three sEMG-based paradigms, with average times of 46.02, 49.06 and 48.08 s, respectively. These experiments validate the feasibility of proposed real-time wearable HCI system and algorithms, providing a potential assistive device interface for persons with disabilities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Sun

Zhang

Zhao

et al. 2018

Sensors

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“…The accuracy of an SVM classifier is sensitive to the choice of features, which has been reported in the past . This motivated us to perform feature optimization for this particular study.…”

Section: Methodsmentioning

confidence: 92%

“…The accuracy of an SVM classifier is sensitive to the choice of features, which has been reported in the past. 32,33 This motivated us to perform feature optimization for this particular study. At first, the 12-fold CV SVM classification error was obtained for individual features, for both multiclass as well as binary classification tasks.…”

Section: Statistical Analysis Of Features From Selectedmentioning

confidence: 99%

Glioma grading using a machine‐learning framework based on optimized features obtained from T₁ perfusion MRI and volumes of tumor components

Sengupta

Ramaniharan

Gupta

et al. 2019

Magnetic Resonance Imaging

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Background Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed. Purpose To develop an artificial intelligence‐based methodology for glioma grading using T1 perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients. Study Type Retrospective. Population The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients. Field Strength/Sequence Conventional MRI images (2D T1‐W, dual PD‐T2‐W, and 3D FLAIR) and 3D T1 perfusion MRI data obtained at 3 T. Assessment Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T1 perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T1 perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest‐based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading. Statistical Tests Mean ± SD, analysis of variance (ANOVA) followed by the Tukey–Kramer test, ROC analysis. Results Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90th percentile value of T1 perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier. Data Conclusion It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. Level of Evidence: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295–1306.

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Individual Finger Movement Recognition Based on sEMG and Classification Techniques

Vega-Escobar

Castro-Ospina

Duque-Muñoz

2018

Progress in Artificial Intelligence and Pattern Recognition

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sEMG feature selection and classification using SVM-RFE

Cited by 19 publications

References 26 publications

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Glioma grading using a machine‐learning framework based on optimized features obtained from T₁ perfusion MRI and volumes of tumor components

Individual Finger Movement Recognition Based on sEMG and Classification Techniques

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sEMG feature selection and classification using SVM-RFE

Cited by 19 publications

References 26 publications

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Glioma grading using a machine‐learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components

Individual Finger Movement Recognition Based on sEMG and Classification Techniques

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Glioma grading using a machine‐learning framework based on optimized features obtained from T₁ perfusion MRI and volumes of tumor components