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Çalışkan SG, Bilgin MD. Nonlinear surface EMG analysis to detect the neuroprotective effect of citicoline in rat sciatic nerve crush injury. Med Biol Eng Comput 2022; 60:2865-2875. [DOI: 10.1007/s11517-022-02639-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 07/28/2022] [Indexed: 12/01/2022]
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2
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Zhang H, Wang X, Zhang Y, Cao G, Xia C. Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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3
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Estimation of Time-Frequency Muscle Synergy in Wrist Movements. ENTROPY 2022; 24:e24050707. [PMID: 35626589 PMCID: PMC9140749 DOI: 10.3390/e24050707] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/09/2022] [Accepted: 04/25/2022] [Indexed: 02/05/2023]
Abstract
Muscle synergy analysis is a kind of modularized decomposition of muscles during exercise controlled by the central nervous system (CNS). It can not only extract the synergistic muscles in exercise, but also obtain the activation states of muscles to reflect the coordination and control relationship between muscles. However, previous studies have mainly focused on the time-domain synergy without considering the frequency-specific characteristics within synergy structures. Therefore, this study proposes a novel method, named time-frequency non-negative matrix factorization (TF-NMF), to explore the time-varying regularity of muscle synergy characteristics of multi-channel surface electromyogram (sEMG) signals at different frequency bands. In this method, the wavelet packet transform (WPT) is used to transform the time-scale signals into time-frequency dimension. Then, the NMF method is calculated in each time-frequency window to extract the synergy modules. Finally, this method is used to analyze the sEMG signals recorded from 8 muscles during the conversion between wrist flexion (WF stage) and wrist extension (WE stage) movements in 12 healthy people. The experimental results show that the number of synergy modules in wrist flexion transmission to wrist extension (Motion Conversion, MC stage) is more than that in the WF stage and WE stage. Furthermore, the number of flexor and extensor muscle synergies in the frequency band of 0–125 Hz during the MC stage is more than that in the frequency band of 125–250 Hz. Further analysis shows that the flexion muscle synergies mostly exist in the frequency band of 140.625–156.25 Hz during the WF stage, and the extension muscle synergies appear in the frequency band of 125–156.25 Hz during the WE stage. These results can help to better understand the time-frequency features of muscle synergy, and expand study perspective related to motor control in nervous system.
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Sayilgan E, Yuce Y, Isler Y. Investigating the Effect of Flickering Frequency Pair and Mother Wavelet Selection in Steady-State Visually-Evoked Potentials on Two-Command Brain-Computer Interfaces. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.04.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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5
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A novel statistical decimal pattern-based surface electromyogram signal classification method using tunable q-factor wavelet transform. Soft comput 2021. [DOI: 10.1007/s00500-020-05205-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Campbell E, Phinyomark A, Scheme E. Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1613. [PMID: 32183215 PMCID: PMC7146367 DOI: 10.3390/s20061613] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/08/2020] [Accepted: 03/09/2020] [Indexed: 11/17/2022]
Abstract
This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.The intrinsic physiological mechanisms limiting practical implementations of myoelectric deviceswere explored: the limb position effect and the contraction intensity effect. The degradationof electromyography (EMG) pattern recognition in the presence of these factors was demonstratedon six datasets, where classification performance was 13% and 20% lower than the controlledsetting for the limb position and contraction intensity effect, respectively. The experimental designsof limb position and contraction intensity literature were surveyed. Current state-of-the-art trainingstrategies and robust algorithms for both effects were compiled and presented. Recommendationsfor future limb position effect studies include: the collection protocol providing exemplars of at least 6positions (four limb positions and three forearm orientations), three-dimensional space experimentaldesigns, transfer learning approaches, and multi-modal sensor configurations. Recommendationsfor future contraction intensity effect studies include: the collection of dynamic contractions, nonlinearcomplexity features, and proportional control.
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Affiliation(s)
- Evan Campbell
- Department of Electrical and Computer Engineering, University of New Brunswick, Canada
- Institute of Biomedical Engineering, University of New Brunswick, Canada
| | - Angkoon Phinyomark
- Institute of Biomedical Engineering, University of New Brunswick, Canada
| | - Erik Scheme
- Department of Electrical and Computer Engineering, University of New Brunswick, Canada
- Institute of Biomedical Engineering, University of New Brunswick, Canada
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Peng J, Hao D, Yang L, Du M, Song X, Jiang H, Zhang Y, Zheng D. Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest. Biocybern Biomed Eng 2020; 40:352-362. [PMID: 32308250 PMCID: PMC7153772 DOI: 10.1016/j.bbe.2019.12.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.
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Key Words
- ACC, accuracy
- ADASYN, adaptive synthetic sampling approach
- ANN, artificial neural network
- AR, auto-regressive model
- AUC, the area under the curve
- CorrDim, correlation dimension
- DT, decision tree
- EHG, electrohysterogram
- Electrohysterogram (EHG)
- Feature extraction
- Gestational week
- IUPC, intrauterine pressure catheter
- K-NN, K-nearest
- LDA, linear discriminant analysis
- LE, Lyapunov exponent
- MDF, median frequency
- MNF, mean frequency
- PE, preterm delivery before the 26th week of gestation
- PF, peak frequency
- PL, preterm delivery after the 26th week of gestation
- Preterm delivery
- QDA, quadratic discriminant analysis
- RF, random forest
- RMS, root mean square
- ROC, the receiver operating characteristic curve
- Random forest (RF).
- SD, standard deviation
- SE, energy values in signal
- SM, maximum values in signal
- SS, singular values in signal
- SV, variance values in signal
- SVM, support vector machine
- SampEn, sample entropy
- TE, term delivery before the 26th week of gestation
- TL, term delivery after the 26th week of gestation
- TOCO, tocodynamometer
- TPEHG, term-preterm electrohysterogram
- Tr, time reversibility
- τz, zero-crossing
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Affiliation(s)
- Jin Peng
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Lin Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Mengqing Du
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Xiaoxiao Song
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Hongqing Jiang
- Beijing Haidian Maternal and Children Health Hospital, Beijing, China
| | - Yunhan Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Dingchang Zheng
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, UK
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Masood F, Abdullah HA, Seth N, Simmons H, Brunner K, Sejdic E, Schalk DR, Graham WA, Hoggatt AF, Rosene DL, Sledge JB, Nesathurai S. Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes. SENSORS 2019; 19:s19153303. [PMID: 31357572 PMCID: PMC6695770 DOI: 10.3390/s19153303] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/25/2019] [Accepted: 07/25/2019] [Indexed: 12/11/2022]
Abstract
This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).
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Affiliation(s)
- Farah Masood
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.
- The Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad 47146, Iraq.
| | | | - Nitin Seth
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Heather Simmons
- The Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Kevin Brunner
- The Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Ervin Sejdic
- The Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Dane R Schalk
- The Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - William A Graham
- The Division of Physical Medicine and Rehabilitation, Department of Medicine, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Amber F Hoggatt
- The Center of Comparative Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Douglas L Rosene
- The Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - John B Sledge
- The Lafayette Bone and Joint Clinic, Lafayette, LA 70508, USA
| | - Shanker Nesathurai
- The Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI 53715, USA
- The Division of Physical Medicine and Rehabilitation, Department of Medicine, McMaster University, Hamilton, ON L8S 4K1, Canada
- The Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, St Joseph's Hamilton Healthcare, Hamilton, ON L9C 0E3, Canada
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9
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Göksu H. EEG based epileptiform pattern recognition inside and outside the seizure states. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.03.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Artameeyanant P, Sultornsanee S, Chamnongthai K. An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection. SPRINGERPLUS 2017; 5:2101. [PMID: 28053831 PMCID: PMC5174015 DOI: 10.1186/s40064-016-3772-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 11/30/2016] [Indexed: 04/03/2024]
Abstract
Background Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses. Methods This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such as k-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases. Results Performance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods. Conclusions An EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses.
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Affiliation(s)
- Patcharin Artameeyanant
- Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmod, Thungkhru, Bangkok, 10140 Thailand
| | - Sivarit Sultornsanee
- School of Business, University of the Thai Chamber of Commerce, 126/1 Vibhavadi Rd., Dindang, Bangkok, 10400 Thailand
| | - Kosin Chamnongthai
- Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmod, Thungkhru, Bangkok, 10140 Thailand
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LIU HAOTING, XU FENGGANG, ZHOU QIANXIANG, LIU ZHIZHEN, LI FAN, WANG CHUNHUI, CHEN SHANGUANG. HEALTHY STATE MONITOR OF UPPER LIMB FOR SPACE FLIGHT TASK BASED ON SIGNAL ANALYSES OF MULTIPLE MUSCLE FORCES. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519417500610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A novel healthy state monitor method of upper limb for space flight task is proposed. Without taking other complex diagnosis equipment in orbit, this method only uses the ordinary exercise instruments to collect and analyze the multiple muscle forces of astronauts, and deduces where the serious muscle atrophy occurs in their muscle groups of upper limb. First, the typical multiple muscle forces data of upper limb are accumulated. A 45-day 6-degree head-down tilt bed rest experiment together with a multiple muscle forces test experiment are carried out to collect the corresponding data. These data include both the muscle force data of healthy state and the related data of unhealthy state. Second, the Wavelet Packet Transform (WPT) and the Empirical Mode Decomposition (EMD) methods are used to compute the signal features of these data above. Third, a Support Vector Machine (SVM) classifier is trained by the related signal features. Finally, the trained SVM can be utilized to evaluate the healthy state of upper limb in orbit for astronaut. If the output of SVM is negative, the C-means method and the Euclidean distance can be used to locate the abnormal muscle forces and muscle groups. The concept of typical muscle group health state evaluation for upper limb is emphasized in this paper. The comparisons among the traditional diagnosis-based method, the electromyogram (EMG)-based muscle forces analysis method, and the proposed method are made. Many experiment results on ground have verified the effectiveness of proposed method.
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Affiliation(s)
- HAOTING LIU
- School of Biological Science & Medical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, P. R. China
| | - FENGGANG XU
- School of Biological Science & Medical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, P. R. China
| | - QIANXIANG ZHOU
- School of Biological Science & Medical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, P. R. China
| | - ZHIZHEN LIU
- National Laboratory of Human Factor, Astronaut Research and Training Center of China, No.1 Yuanmingyuan West Road, Beijing 100094, P. R. China
| | - FAN LI
- National Laboratory of Human Factor, Astronaut Research and Training Center of China, No.1 Yuanmingyuan West Road, Beijing 100094, P. R. China
| | - CHUNHUI WANG
- National Laboratory of Human Factor, Astronaut Research and Training Center of China, No.1 Yuanmingyuan West Road, Beijing 100094, P. R. China
| | - SHANGUANG CHEN
- National Laboratory of Human Factor, Astronaut Research and Training Center of China, No.1 Yuanmingyuan West Road, Beijing 100094, P. R. China
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12
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Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis. ENTROPY 2016. [DOI: 10.3390/e18020041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Wu Q, Mao J, Wei C, Fu S, Law R, Ding L, Yu B, Jia B, Yang C. Hybrid BF–PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Ertuğrul ÖF, Kaya Y, Tekin R. A novel approach for SEMG signal classification with adaptive local binary patterns. Med Biol Eng Comput 2015; 54:1137-46. [PMID: 26718556 DOI: 10.1007/s11517-015-1443-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 12/14/2015] [Indexed: 10/22/2022]
Abstract
Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.
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Affiliation(s)
- Ömer Faruk Ertuğrul
- Department of Electrical and Electronic Engineering, Batman University, 72060, Batman, Turkey.
| | - Yılmaz Kaya
- Department of Computer Engineering, Siirt University, 56100, Siirt, Turkey
| | - Ramazan Tekin
- Department of Computer Engineering, Batman University, 72060, Batman, Turkey
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15
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16
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Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-1953-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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17
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Spasić S, Kesić S, Stojadinović G, Petković B, Todorović D. Effects of the static and ELF magnetic fields on the neuronal population activity in Morimus funereus (Coleoptera, Cerambycidae) antennal lobe revealed by wavelet analysis. Comp Biochem Physiol A Mol Integr Physiol 2015; 181:27-35. [DOI: 10.1016/j.cbpa.2014.11.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Revised: 11/19/2014] [Accepted: 11/20/2014] [Indexed: 12/21/2022]
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18
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Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y. Feature extraction of the first difference of EMG time series for EMG pattern recognition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:247-256. [PMID: 25023536 DOI: 10.1016/j.cmpb.2014.06.013] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Revised: 06/13/2014] [Accepted: 06/21/2014] [Indexed: 06/03/2023]
Abstract
This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2-8%.
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Affiliation(s)
- Angkoon Phinyomark
- GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France; LIG Laboratory, CNRS UMR 5217, University of Grenoble, Grenoble, France.
| | - Franck Quaine
- GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France.
| | - Sylvie Charbonnier
- GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France.
| | - Christine Serviere
- GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France.
| | | | - Yann Laurillau
- LIG Laboratory, CNRS UMR 5217, University of Grenoble, Grenoble, France.
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19
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Muscle activity detection in electromyograms recorded during periodic movements. Comput Biol Med 2014; 47:93-103. [DOI: 10.1016/j.compbiomed.2014.01.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Revised: 01/27/2014] [Accepted: 01/28/2014] [Indexed: 11/23/2022]
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20
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Fontana JM, Chiu AW. Analysis of electrode shift effects on wavelet features embedded in a myoelectric pattern recognition system. Assist Technol 2014; 26:71-80. [PMID: 25112051 PMCID: PMC4134107 DOI: 10.1080/10400435.2013.827138] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
Myoelectric pattern recognition systems can translate muscle contractions into prosthesis commands; however, the lack of long-term robustness of such systems has resulted in low acceptability. Specifically, socket misalignment may cause disturbances related to electrodes shifting from their original recording location, which affects the myoelectric signals (MES) and produce degradation of the classification performance. In this work, the impact of such disturbances on wavelet features extracted from MES was evaluated in terms of classification accuracy. Additionally, two principal component analysis frameworks were studied to reduce the wavelet feature set. MES from seven able-body subjects and one subject with congenital transradial limb loss were studied. The electrode shifts were artificially introduced by recording signals during six sessions for each subject. A small drop in classification accuracy from 93.8% (no disturbances) to 88.3% (with disturbances) indicated that wavelet features were able to adapt to the variability introduced by electrode shift disturbances. The classification performance of the reduced feature set was significantly lower than the performance of the full wavelet feature set. The results observed in this study suggest that the effect of electrode shift disturbances on the MES can potentially be mitigated by using wavelet features embedded in a pattern recognition system.
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Affiliation(s)
- Juan M. Fontana
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, United States
- Biomedical Engineering Department, Louisiana Tech University, Ruston, LA, United States
| | - Alan W.L. Chiu
- Biomedical Engineering Department, Louisiana Tech University, Ruston, LA, United States
- Applied Biology and Biomedical Engineering, Rose-Hulman Institute of Technology, Terre Haute, IN, 47803, United States
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Shourie N, Firoozabadi SMP, Badie K. A Comparative Investigation of Wavelet Families for Analysis of EEG Signals Related to Artists and Nonartists During Visual Perception, Mental Imagery, and Rest. ACTA ACUST UNITED AC 2013. [DOI: 10.1080/10874208.2013.847606] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Subasi A. Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.03.035] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Huang H, Xie HB, Guo JY, Chen HJ. Ant colony optimization-based feature selection method for surface electromyography signals classification. Comput Biol Med 2011; 42:30-8. [PMID: 22074763 DOI: 10.1016/j.compbiomed.2011.10.004] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2011] [Revised: 09/08/2011] [Accepted: 10/16/2011] [Indexed: 11/17/2022]
Abstract
This paper presented a new ant colony optimization (ACO) feature selection method to classify hand motion surface electromyography (sEMG) signals. The multiple channels of sEMG recordings make the dimensionality of sEMG feature grow dramatically. It is known that the informative feature subset with small size is a precondition for the accurate and computationally efficient classification strategy. Therefore, this study proposed an ACO based feature selection scheme using the heuristic information measured by the minimum redundancy maximum relevance criterion (ACO-mRMR). The experiments were conducted on ten subjects with eight upper limb motions. Two feature sets, i.e., time domain features combined with autoregressive model coefficients (TDAR) and wavelet transform (WT) features, were extracted from the recorded sEMG signals. The average classification accuracies of using ACO reduced TDAR and WT features were 95.45±2.2% and 96.08±3.3%, respectively. The principal component analysis (PCA) was also conducted on the same data sets for comparison. The average classification accuracies of using PCA reduced TDAR and WT features were 91.51±4.9% and 89.87±4.4%, respectively. The results demonstrated that the proposed ACO-mRMR based feature selection method can achieve considerably high classification rates in sEMG motion classification task and be applicable to other biomedical signals pattern analysis.
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Affiliation(s)
- Hu Huang
- School of Electronic and Information Engineering, Jiangsu University, Xuefu Road 301#, Zhenjiang, PR China
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Motion classification of EMG signals based on wavelet packet transform and LS-SVMs ensemble. ACTA ACUST UNITED AC 2009. [DOI: 10.1007/s12209-009-0053-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Sekiyama K, Ito M, Fukuda T, Suzuki T, Yamashita K. An Adaptive Muscular Force Generation Mechanism Based on Prior Information of Handling Object. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2009. [DOI: 10.20965/jaciii.2009.p0222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Evaluating the influences of human-machine interface (HMI) visual information is vital to developing the user-oriented and human-friendly equipment, robots, etc. We define HMI visual information as prior information, such as size, color, shape, etc. The relationship between prior information and the Kansei feeling is evaluated by surface electromyogram (sEMG). This study deals with object-grasping motion and measures sEMG signals during the motion. Prediction on object-grasping motion is predicted from sEMG signals and defined as Force Prediction (FP). Differences between prediction of HMI operation and actual results are assumed to influence on Kansei feeling concerning the operation. Subjects given different prior information calculate FP about plastic bottles when grasping them. Experimental results show that the FP differs even though the plastic bottles have the same weight. The influence of prior information on FP is visually plotted in a three-dimensional map which is called Size-Color-iEMG map, and its application is to HMI design.
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Kilby J, Hosseini HG. Extracting effective features of SEMG using continuous wavelet transform. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2008; 2006:1704-7. [PMID: 17946475 DOI: 10.1109/iembs.2006.260064] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting accurate patterns of the SEMG signals. We used the scalogram and frequency-time based spectrum to plot the power of the wavelet transform and enhance the diagnosis features of the signal. As a result, clinical interpretation of SEMG can be improved by extracting time-based information as well as scales, which can be converted to frequencies. Using the extracted features of the dominant frequencies of the wavelet transform and the related scales, we were able to train and validate an artificial neural network (ANN) for SEMG classification.
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Wang G, Yan Z, Hu X, Xie H, Wang Z. Classification of surface EMG signals using harmonic wavelet packet transform. Physiol Meas 2006; 27:1255-67. [PMID: 17135698 DOI: 10.1088/0967-3334/27/12/001] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In this paper, an efficient method based on the discrete harmonic wavelet packet transform (DHWPT) is presented to classify surface electromyographic (SEMG) signals. After the relative energy of SEMG signals in each frequency band had been extracted by the DHWPT, a genetic algorithm was utilized to select appropriate features in order to reduce the feature dimensionality. Then, the selected features were used as the input vectors to a neural network classifier to discriminate four types of prosthesis movements. Compared with other classification methods, the proposed method provided high classification accuracy in experimental research. In addition, this method could also save a lot of computational time because the DHWPT has a fast algorithm based on the fast Fourier transform for numerical implementation.
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Affiliation(s)
- Gang Wang
- Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
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Wang G, Wang Z, Chen W, Zhuang J. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med Biol Eng Comput 2006; 44:865-72. [PMID: 16951931 DOI: 10.1007/s11517-006-0100-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2005] [Accepted: 07/25/2006] [Indexed: 10/24/2022]
Abstract
In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).
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Affiliation(s)
- Gang Wang
- Department of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, People's Republic of China
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Abstract
Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.
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Affiliation(s)
- Xiao Hu
- Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China.
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