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Article

Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait

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Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
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Neuromuscular Division, Hamad General Hospital, Doha 3050, Qatar
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Department of Neurology, Al khor Hospital, Doha 3050, Qatar
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Department of Physics and Electronics, Dr. Ram Manohar Lohia Avadh University, Faizabad, Uttar Pradesh 224001, India
*
Authors to whom correspondence should be addressed.
Academic Editor: Susanna Spinsante
Sensors 2022, 22(9), 3507; https://doi.org/10.3390/s22093507
Received: 8 March 2022 / Revised: 31 March 2022 / Accepted: 2 April 2022 / Published: 5 May 2022
(This article belongs to the Section Intelligent Sensors)
Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals. View Full-Text
Keywords: diabetic neuropathy; diabetic foot ulceration; biomechanical parameters; electromyography; EMG; gait; ground reaction force; machine learning; feature selection diabetic neuropathy; diabetic foot ulceration; biomechanical parameters; electromyography; EMG; gait; ground reaction force; machine learning; feature selection
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MDPI and ACS Style

Haque, F.; Reaz, M.B.I.; Chowdhury, M.E.H.; Ezeddin, M.; Kiranyaz, S.; Alhatou, M.; Ali, S.H.M.; Bakar, A.A.A.; Srivastava, G. Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait. Sensors 2022, 22, 3507. https://doi.org/10.3390/s22093507

AMA Style

Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, Alhatou M, Ali SHM, Bakar AAA, Srivastava G. Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait. Sensors. 2022; 22(9):3507. https://doi.org/10.3390/s22093507

Chicago/Turabian Style

Haque, Fahmida, Mamun Bin Ibne Reaz, Muhammad Enamul Hoque Chowdhury, Maymouna Ezeddin, Serkan Kiranyaz, Mohammed Alhatou, Sawal Hamid Md Ali, Ahmad Ashrif A Bakar, and Geetika Srivastava. 2022. "Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait" Sensors 22, no. 9: 3507. https://doi.org/10.3390/s22093507

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