Class Discriminator-Based EMG Classification Approach for Detection of Neuromuscular Diseases Using Discriminator-Dependent Decision Rule (D3R) Approach

Bhattacharya, Avik; Pahari, Purbanka; Basak, Piyali; Sarkar, Anasua

doi:10.1007/978-981-10-8863-6_6

Cited by 1 publication

(1 citation statement)

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“…Support Vector Machine (SVM) is similar in concept to LDA in that both create a hyperplane separating the labelled data according to their classes. The difference is that the LDA hyperplane maximises the classes mean, whereas the SVM hyperplane maximises the margin between the two classes boundaries with the least amount of error [65]. In this study, two kernels were used, namely linear SVM-L and radial basis function SVM-RBF.…”

Section: Classificationmentioning

confidence: 99%

Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals

Naim¹,

Mustafa²,

Sulaiman³

et al. 2022

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Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This work proposed a dual-layer ranking feature selection algorithm based on statistical formula f EMG signals for driver fatigue detection. In the beginning, in the first layer, 21 filter algorithms were calculated to rank 47 sets of EMG features (25 time-domain and 9 frequency-domain) and applied to six classifiers. Then, in the second layer, all the ranks were re-ranked based on the statistical formula (average, median, mode and variance). The classification performance of all rankings was compared along with the number of features. The highest classification accuracy achieved was 95% for 12 features using the Average Statistical Rank (ASR) and LDA classifier. It is conclusive that a combination of features from the time domain and frequency domain can deliver better performance compared to a single domain feature. Concurrently, the statistical rank ASR performed better than the single filter rank by reducing the number of features. The proposed model can be a benchmark for the enhanced feature selection method for EMG driver fatigue signal.

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Section: Classificationmentioning

confidence: 99%