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Vijayvargiya A, Sinha A, Gehlot N, Jena A, Kumar R, Moran K. S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality. PLoS One 2024; 19:e0301263. [PMID: 38820390 PMCID: PMC11142505 DOI: 10.1371/journal.pone.0301263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.
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Affiliation(s)
- Ankit Vijayvargiya
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
- Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India
| | - Aparna Sinha
- Department of Information Technology, Bansthali Vidyapeeth, Radha Kishnpura, Rajasthan, India
| | - Naveen Gehlot
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Ashutosh Jena
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Rajesh Kumar
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Kieran Moran
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
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Khader A, Zyout A, Al Fahoum A. Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis. PLoS One 2024; 19:e0302707. [PMID: 38713653 DOI: 10.1371/journal.pone.0302707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/09/2024] [Indexed: 05/09/2024] Open
Abstract
Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding the knee joint were examined and recorded. The principal components of the proposed method were preprocessing, high-order spectral analysis (HOSA), and diagnosis/recognition through deep learning. EMG signals from individuals with normal and OA knees while walking were extracted from a publicly available database. This examination focused on the quadriceps femoris, the medial gastrocnemius, the rectus femoris, the semitendinosus, and the vastus medialis. Filtration and rectification were utilized beforehand to eradicate noise and smooth EMG signals. Signals' higher-order spectra were analyzed with HOSA to obtain information about nonlinear interactions and phase coupling. Initially, the bicoherence representation of EMG signals was devised. The resulting images were fed into a deep-learning system for identification and analysis. A deep learning algorithm using adapted ResNet101 CNN model examined the images to determine whether the EMG signals were conventional or indicative of knee osteoarthritis. The validated test results demonstrated high accuracy and robust metrics, indicating that the proposed method is effective. The medial gastrocnemius (MG) muscle was able to distinguish Knee osteoarthritis (KOA) patients from normal with 96.3±1.7% accuracy and 0.994±0.008 AUC. MG has the highest prediction accuracy of KOA and can be used as the muscle of interest in future analysis. Despite the proposed method's superiority, some limitations still require special consideration and will be addressed in future research.
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Affiliation(s)
- Ateka Khader
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, Jordan
| | - Ala'a Zyout
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, Jordan
| | - Amjed Al Fahoum
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, Jordan
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Hatamzadeh M, Sharifnezhad A, Hassannejad R, Zory R. Discriminative sEMG-based features to assess damping ability and interpret activation patterns in lower-limb muscles of ACLR athletes. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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An Efficient Gait Abnormality Detection Method Based on Classification. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2022. [DOI: 10.3390/jsan11030031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In the study of human mobility, gait analysis is a well-recognized assessment methodology. Despite its widespread use, doubts exist about its clinical utility, i.e., its potential to influence the diagnostic-therapeutic practice. Gait analysis evaluates the walking pattern (normal/abnormal) based on the gait cycle. Based on the analysis obtained, various applications can be developed in the medical, security, sports, and fitness domain to improve overall outcomes. Wearable sensors provide a convenient, efficient, and low-cost approach to gather data, while machine learning methods provide high accuracy gait feature extraction for analysis. The problem is to identify gait abnormalities and if present, subsequently identify the locations of impairments that lead to the change in gait pattern of the individual. Proper physiotherapy treatment can be provided once the location/landmark of the impairment is known correctly. In this paper, classification of multiple anatomical regions and their combination on a large scale highly imbalanced dataset is carried out. We focus on identifying 27 different locations of injury and formulate it as a multi-class classification approach. The advantage of this method is the convenience and simplicity as compared to previous methods. In our work, a benchmark is set to identify the gait disorders caused by accidental impairments at multiple anatomical regions using the GaitRec dataset. In our work, machine learning models are trained and tested on the GaitRec dataset, which provides Ground Reaction Force (GRF) data, to analyze an individual’s gait and further classify the gait abnormality (if present) at the specific lower-region portion of the body. The design and implementation of machine learning models are carried out to detect and classify the gait patterns between healthy controls and gait disorders. Finally, the efficacy of the proposed approach is showcased using various qualitative accuracy metrics. The achieved test accuracy is 96% and an F1 score of 95% is obtained in classifying various gait disorders on unseen test samples. The paper concludes by stating how machine learning models can help to detect gait abnormalities along with directions of future work.
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Vijayvargiya A, Singh B, Kumar R, Tavares JMRS. Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview. Biomed Eng Lett 2022; 12:343-358. [DOI: 10.1007/s13534-022-00236-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 05/17/2022] [Accepted: 06/06/2022] [Indexed: 12/16/2022] Open
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Awan MJ, Mohd Rahim MS, Salim N, Rehman A, Nobanee H. Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2550120. [PMID: 35444781 PMCID: PMC9015864 DOI: 10.1155/2022/2550120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/02/2022] [Accepted: 03/21/2022] [Indexed: 12/14/2022]
Abstract
In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.
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Affiliation(s)
- Mazhar Javed Awan
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
- Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
| | - Mohd Shafry Mohd Rahim
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
| | - Naomie Salim
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
| | - Amjad Rehman
- Artificial Intelligence and Data Analytics Laboratory, College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Haitham Nobanee
- College of Business, Abu Dhabi University, P.O. Box 59911, Abu Dhabi, UAE
- Oxford Centre for Islamic Studies, University of Oxford, Oxford OX1 2J, UK
- School of Histories Languages and Cultures, The University of Liverpool, Liverpool L69 3BX, UK
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A Deep Learning-Based Upper Limb Rehabilitation Exercise Status Identification System. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06702-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Li W, Liu K, Sun Z, Li C, Chai Y, Gu J. A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103115] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Çalıkuşu İ, Uzunhisarcıklı E, Fidan U, Çetinkaya MB. Analysing the effect of robotic gait on lower extremity muscles and classification by using deep learning. Comput Methods Biomech Biomed Engin 2021; 25:1350-1369. [PMID: 34874210 DOI: 10.1080/10255842.2021.2012655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Robotic gait training helps the nervous system recover and strengthen weak muscle groups. Many studies in the literature show that applying robotic gait rehabilitation to patients with neurological disorders such as Multiple Sclerosis (MS), Stroke and Spinal Cord Injection (SCI) effectively restores gait ability. In contrast to the studies in the literature that included only healthy individuals, both the control and patient groups were formed and detailed analyses were carried out for both groups. In this study, EMG signals in GMA, GME, ILP, BF, VM, MG, TA muscles were recorded simultaneously with a different electrode placement during robotic gait for the first time in literature and then a location that prevents a phase shift was presented. The classification performance has also been increased by removing 26 different attribute parameters like time, frequency and statistics from the signals instead of gait studies with a maximum of 12-16 traits extraction. The extracted features were classified with the approaches Multilayer Perceptron Neural Networks (MLP), Support Vector Machines (SVM), K-Nearest Neighbourhood algorithm (KNN), Random Forest Classification Algorithm (RF) and Deep Learning and then a detailed performance comparison have been realized. Among the approaches compared the Stochastic Gradient Optimization Algorithm-based deep learning structure produced the best performance with 98.5714% accuracy. It was also seen that it is essential to plan the exoskeleton and the robotic gait pattern suitable for patients' disease state and muscle activation.
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Affiliation(s)
- İsmail Çalıkuşu
- Biomedical Device Technology, Nevsehir Haci Bektas Veli Universitesi, Nevsehir, Turkey
| | - Esma Uzunhisarcıklı
- Kayseri Vocational High School Biomedical Device Technology, Kayseri University, Talas, Turkey
| | - Uğur Fidan
- Engineering Faculty Biomedical Engineering, Afyon Kocatepe University, Afyon, Turkey
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