Prediction of Myoelectric Biomarkers in Post-Stroke Gait
Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired mus...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/16/5334 |
| _version_ | 1797522103994417152 |
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| author | Iqram Hussain Se-Jin Park |
| author_facet | Iqram Hussain Se-Jin Park |
| author_sort | Iqram Hussain |
| collection | DOAJ |
| description | Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation. |
| first_indexed | 2024-03-10T08:24:46Z |
| format | Article |
| id | doaj.art-cbdd709170774c3a88a7db39fa4095d4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:24:46Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cbdd709170774c3a88a7db39fa4095d42023-11-22T09:37:57ZengMDPI AGSensors1424-82202021-08-012116533410.3390/s21165334Prediction of Myoelectric Biomarkers in Post-Stroke GaitIqram Hussain0Se-Jin Park1Center for Medical Convergence Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, KoreaCenter for Medical Convergence Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, KoreaElectromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.https://www.mdpi.com/1424-8220/21/16/5334electromyographyphysiological biomarkergaitstrokemachine learning |
| spellingShingle | Iqram Hussain Se-Jin Park Prediction of Myoelectric Biomarkers in Post-Stroke Gait Sensors electromyography physiological biomarker gait stroke machine learning |
| title | Prediction of Myoelectric Biomarkers in Post-Stroke Gait |
| title_full | Prediction of Myoelectric Biomarkers in Post-Stroke Gait |
| title_fullStr | Prediction of Myoelectric Biomarkers in Post-Stroke Gait |
| title_full_unstemmed | Prediction of Myoelectric Biomarkers in Post-Stroke Gait |
| title_short | Prediction of Myoelectric Biomarkers in Post-Stroke Gait |
| title_sort | prediction of myoelectric biomarkers in post stroke gait |
| topic | electromyography physiological biomarker gait stroke machine learning |
| url | https://www.mdpi.com/1424-8220/21/16/5334 |
| work_keys_str_mv | AT iqramhussain predictionofmyoelectricbiomarkersinpoststrokegait AT sejinpark predictionofmyoelectricbiomarkersinpoststrokegait |