A novel statistical decimal pattern-based surface electromyogram signal classification method using tunable q-factor wavelet transform

Surface electromyogram sensors have been widely used to acquire hand gestures signals. Many machine learning and artificial intelligence methods have been presented for automated surface electromyogram signals classification. In this method, a novel surface electromyogram signals recognition method is presented using a novel 1D local descriptor. The proposed descriptor is called as statistical decimal pattern and it is utilized as feature extractor in this study and tunable q-factor wavelet transform is used as pooling in this method. By using tunable q-factor wavelet transform and the proposed statistical decimal pattern, a multileveled learning method is constructed. Ten levels are created by using tunable q-factor wavelet transform. Statistical decimal pattern extracts features from tunable q-factor wavelet transform sub-bands of the raw surface electromyogram signal. Then, the generated features are concatenated, and to select distinctive features, ReliefF and neighborhood component analysis are used together. In the classification phase, k-nearest neighbor classifier with city block distance is chosen. To test performance of the proposed tunable q-factor wavelet transform and the proposed statistical decimal pattern-based surface electromyogram classification method, a freely and publicly published dataset was used. In this dataset, 10 hand gestures were defined. Experimental results clearly shown that the proposed tunable q wavelet transform and statistical decimal pattern-based method achieved 98.0%, 99.79% accuracy rates on two datasets and it outcomes other state-of-the-art methods according to these results.

[1]  Anthony Tzes,et al.  Improving EMG based classification of basic hand movements using EMD , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Hakan Temeltas,et al.  Feature extraction of EMG signals, classification with ANN and kNN algorithms , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[3]  Winnie Jensen,et al.  The effect of time on EMG classification of hand motions in able-bodied and transradial amputees. , 2018, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[4]  Abdulhamit Subasi,et al.  Comparison of decision tree algorithms for EMG signal classification using DWT , 2015, Biomed. Signal Process. Control..

[5]  Mykola Pechenizkiy,et al.  ReliefF-MI: An extension of ReliefF to multiple instance learning , 2012, Neurocomputing.

[6]  Andries Petrus Engelbrecht,et al.  A parameter-free particle swarm optimization algorithm using performance classifiers , 2019, Inf. Sci..

[7]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[8]  Yannick Aoustin,et al.  A testing system for a real-time gesture classification using surface EMG , 2017 .

[9]  U. Rajendra Acharya,et al.  A review of automated sleep stage scoring based on physiological signals for the new millennia , 2019, Comput. Methods Programs Biomed..

[10]  Abdulhamit Subasi,et al.  Automated EMG Signal Classification for Diagnosis of Neuromuscular Disorders Using DWT and Bagging , 2018 .

[11]  Pornchai Phukpattaranont,et al.  Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..

[12]  Saichon Jaiyen,et al.  sEMG signal classification using SMO algorithm and singular value decomposition , 2015, 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE).

[13]  Zhizhong Wang,et al.  Classification of surface EMG signal using relative wavelet packet energy , 2005, Comput. Methods Programs Biomed..

[14]  R. Shashikant,et al.  Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter , 2020, Applied Computing and Informatics.

[15]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[16]  Natarajan Sriraam,et al.  Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms , 2018, Expert Syst. Appl..

[17]  Deok-Hwan Kim,et al.  Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals , 2017, Expert Syst. Appl..

[18]  Shaikh Anowarul Fattah,et al.  Hand movement recognition based on singular value decomposition of surface EMG signal , 2017, 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC).

[19]  Abdulhamit Subasi,et al.  Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition , 2020, Biomed. Signal Process. Control..

[20]  Erik Stålberg,et al.  Standards for quantification of EMG and neurography , 2019, Clinical Neurophysiology.

[21]  Ta-Te Lin,et al.  A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions , 2014, Biomed. Signal Process. Control..

[22]  Hiroki Yamazaki,et al.  Deep learning for waveform identification of resting needle electromyography signals , 2019, Clinical Neurophysiology.

[23]  U. Rajendra Acharya,et al.  Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals , 2017, Comput. Biol. Medicine.

[24]  Sengul Dogan,et al.  Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals , 2019, Knowl. Based Syst..

[25]  Xinjun Sheng,et al.  Common spatial-spectral analysis of EMG signals for multiday and multiuser myoelectric interface , 2019, Biomed. Signal Process. Control..

[26]  Fahreddin Sadikoglu,et al.  Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease , 2017 .

[27]  Shih-Tsang Tang,et al.  A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study , 2018 .

[28]  Abdulhamit Subasi,et al.  Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders , 2013, Comput. Biol. Medicine.

[29]  Selahaddin Batuhan Akben,et al.  Low-cost and easy-to-use grasp classification, using a simple 2-channel surface electromyography (sEMG) , 2017 .

[30]  Gianpaolo Francesco Trotta,et al.  A model-free technique based on computer vision and sEMG for classification in Parkinson's disease by using computer-assisted handwriting analysis , 2019, Pattern Recognit. Lett..

[31]  Ram Bilas Pachori,et al.  Classification of cardiac sound signals using constrained tunable-Q wavelet transform , 2014, Expert Syst. Appl..

[32]  Shiru Sharma,et al.  Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals , 2019, Comput. Biol. Medicine.

[33]  Gamini Dissanayake,et al.  Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals , 2012, Expert Syst. Appl..

[34]  Loredana Zollo,et al.  EMG and ENG-envelope pattern recognition for prosthetic hand control , 2019, Journal of Neuroscience Methods.

[35]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[36]  Leif E. Peterson.  K-nearest neighbor , 2009, Scholarpedia.

[37]  Nick Roussopoulos,et al.  K-Nearest Neighbor Search for Moving Query Point , 2001, SSTD.

[38]  Ashish Khanna,et al.  Boosted neural network ensemble classification for lung cancer disease diagnosis , 2019, Appl. Soft Comput..

[39]  Yuanyuan Wang,et al.  Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds , 2019, Comput. Methods Programs Biomed..

[40]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[41]  Wei Yang,et al.  Fast neighborhood component analysis , 2012, Neurocomputing.

[42]  Jamileh Yousefi,et al.  Characterizing EMG data using machine-learning tools , 2014, Comput. Biol. Medicine.

[43]  U. Rajendra Acharya,et al.  Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals , 2019, Future Gener. Comput. Syst..

[44]  Anish C. Turlapaty,et al.  A Machine Learning System for Classification of EMG Signals to Assist Exoskeleton Performance , 2018, 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[45]  Mingwu Jin,et al.  Predication of different stages of Alzheimer’s disease using neighborhood component analysis and ensemble decision tree , 2018, Journal of Neuroscience Methods.

[46]  Thierry Denœux.  A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory , 2008 .

[47]  Mads Jochumsen,et al.  The effect of arm position on classification of hand gestures with intramuscular EMG , 2018, Biomed. Signal Process. Control..

[48]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

[49]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[50]  R. B. Pachori,et al.  Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals , 2017 .

[51]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..