A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals

Powar, Omkar S; Chemmangat, Krishnan; Figarado, Sheron

doi:10.1016/j.bspc.2018.02.006

Cited by 25 publications

(6 citation statements)

References 17 publications

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“…SFS-FW is the newest recommended method before 2013, which has similar performance to FVS and is more efficient [12]. More recent works either focus on specific data domains (e.g., [29], [30], [31]) or specific classifiers (e.g., the work in [32] improves the classification performance of KNN, but not other classifiers). Some recent approaches may gain better classification performance but the time cost is generally much higher than SFS-FW [33], [34].…”

Section: Methods To Comparementioning

confidence: 99%

Identify Significant Phenomenon-Specific Variables for Multivariate Time Series

Hao

Cao

Mueen

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Multivariate time series (MTS) are collected for different variables in studying scientific phenomena or monitoring system health where one time series records the values of one variable for a time period. Among the different variables, it is common that only a few variables contribute significantly to a specific phenomenon. Furthermore, the variables contributing significantly to different phenomena are often different. We denote the different variables that contribute to the occurrences of different phenomena as Phenomenon-specific Variables (PVs). In this paper, we formulate a novel problem of identifying significant PVs from MTS datasets. To analyze MTS data, feature extraction techniques have been extensively studied. However, most of them identify important global features for one dataset and do not utilize the temporal order of time series. To solve the newly introduced problem, we propose a solution framework, CNNmts-X, which is a new variant of the Convolutional Neural Networks (CNN) and can embed other feature extraction techniques (as X). Furthermore, we design a CNNmts-LR method that implements a new feature identification approach (LR) as X in the CNNmts-X framework. The LR method leverages both Linear Discriminant Analysis (LDA) and Random Forest (RF).Our extensive experiments on five real datasets show that the CNNmts-LR method has exhibited much better performance than several other baseline methods. Using 30% of the PVs discovered from the CNNmts-LR, classifications can achieve better or similar performance than using all the variables.

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Section: Methods To Comparementioning

confidence: 99%

Identify Significant Phenomenon-Specific Variables for Multivariate Time Series

Hao

Cao

Mueen

et al. 2021

IEEE Trans. Knowl. Data Eng.

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“…Evaluate fitness of chromosomes, F(X) (4) Set Z as the best chromosome (5) for t = 1 to maximum number of generation, T max (6) for i = 1 to number of crossovers 7Select 2 parents using roulette wheel selection (8) Generate 2 children by applying crossover between 2 parents (9) next i (10) for j = 1 to twice number of crossover (11) Mutate the child based on the mutation rate, MR (12) next j (13) Evaluate the fitness of newly generated children (14) Add newly generated children into current population (15) Rank the population and select the best N chromosomes (16) Update Z if there is better chromosome in the population (17) next t…”

Section: Algorithm 2 Genetic Algorithm (1)mentioning

confidence: 99%

“…Begin, initialize the parameters, N, T max , CR 2Initialize a population of vectors, X 3Evaluate fitness of vectors, F(X) (4) Set Z as the best vector (5) for t = 1 to maximum number of generation, T max (6) for i = 1 to number of vectors, N (7)…”

Section: Algorithm 3 Binary Differential Evolutionmentioning

confidence: 99%

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

et al. 2019

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Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

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“…Root mean square (RMS) is one of the famous features that describes the information related to muscle force and activation [21], [22]. RMS can be expressed as: (6) where D i is the coefficient at i frequency band and L is referred to the length of coefficient.…”

Section: F Feature Extractionmentioning

confidence: 99%

A Comparative Analysis of Wavelet Families for the Classification of Finger Motions

Too¹,

Abdullah²,

Saad³

2019

IJACSA

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Wavelet transform (WT) has been widely used in biomedical, rehabilitation and engineering applications. Due to the natural characteristic of WT, its performance is mostly depending on the selection of mother wavelet function. A proper mother wavelet ensures the optimum performance; however, the selection of mother wavelet is mostly empirical and varies according to dataset. Hence, this paper aims to investigate the best mother wavelet of discrete wavelet transform (DWT) and wavelet packet transform (WPT) in the classification of different finger motions. In this study, twelve mother wavelets are evaluated for both DWT and WPT. The electromyography (EMG) data of 12 finger motions are acquired from online database. Four useful features are extracted from each recorded EMG signal via DWT and WPT transformation. Afterward, support vector machine (SVM) and linear discriminate analysis (LDA) are employed for performance evaluation. Our experimental results demonstrate Bior3.3 to be the most suitable mother wavelet in DWT. On the other hand, WPT with Bior2.2 overtakes other mother wavelets in the classification of finger motions. The results obtained suggest that Biorthogonal families are more suitable for accurate EMG signals classification.

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A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals

Cited by 25 publications

References 17 publications

Identify Significant Phenomenon-Specific Variables for Multivariate Time Series

Identify Significant Phenomenon-Specific Variables for Multivariate Time Series

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

A Comparative Analysis of Wavelet Families for the Classification of Finger Motions

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