An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification. (February 2023) - Sahu, Padmini; Singh, Bikesh Kumar; Nirala, Neelamshobha
Highlights: An improved ABC (GGABC) is proposed and validated on benchmark functions. A novel wrapper FS technique (BGGABC) for EMG signal classification is presented. Discrete Wavelet Transform (DWT) is used for feature extraction. The performance of BGGABC is evaluated and compared with other competitors. Abstract: Electromyography (EMG) measures muscle relaxes or contractions during muscular activity through EMG signal. It plays a vital role in identifying muscle-related problems for clinical diagnosis. This paper presents an efficient EMG feature selection technique for classifying 17 different prosthetic hand movements recorded from 11 subjects. Two variants of the Artificial Bee Colony (ABC) algorithm, namely: i) Global Best Guided ABC (GbestABC) and ii) Gaussian ABC (GABC), are employed to propose an Improved Artificial Bee Colony (ABC) algorithm called Global Best Guided Gaussian ABC (GGABC) for solving global optimization problems. GbestABC performs better in the exploitation phase, whereas GABC performs better in the exploration phase in searching for the optimal solutions. The proposed GGABC takes advantage of GbestABC and GABC to counterbalance basic ABC's exploitation and exploration capability. Further, a binary version of GGABC known as binary GGABC (BGGABC) is developed to solve binary optimization problems and select optimal EMG signal classification features. Extensive experiments are carried out in three phases: i) GGABC for global optimization problems ii) BGGABC for EMG feature selection problems with other meta-heuristic-based competitors iii) BGGABC for EMG feature selection problems with well-known filter based techniques. K-nearest neighbor (KNN) classifier is used in experiments to validate and investigate the effectiveness of the proposed algorithm. Experimental result shows that the BGGABC-based EMG feature selection achieved 94.13% average classification accuracy and 97.06% best classification accuracy. Obtained results confirm that the proposed algorithm outperforms or is competitive with state-of-the-art algorithms in EMG feature selection and classification.