Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography
Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a contr...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/634 |
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| author | Eira Lotta Spieker Ardit Dvorani Christina Salchow-Hömmen Carolin Otto Klemens Ruprecht Nikolaus Wenger Thomas Schauer |
| author_facet | Eira Lotta Spieker Ardit Dvorani Christina Salchow-Hömmen Carolin Otto Klemens Ruprecht Nikolaus Wenger Thomas Schauer |
| author_sort | Eira Lotta Spieker |
| collection | DOAJ |
| description | Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use. |
| first_indexed | 2024-03-08T09:46:39Z |
| format | Article |
| id | doaj.art-748cd52aeea14cdb8e08332b5ba7b020 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:39Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-748cd52aeea14cdb8e08332b5ba7b0202024-01-29T14:17:30ZengMDPI AGSensors1424-82202024-01-0124263410.3390/s24020634Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on MechanomyographyEira Lotta Spieker0Ardit Dvorani1Christina Salchow-Hömmen2Carolin Otto3Klemens Ruprecht4Nikolaus Wenger5Thomas Schauer6Department of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, GermanyControl Systems Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, GermanyDepartment of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, GermanyDepartment of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, GermanyDepartment of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, GermanyDepartment of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, GermanyControl Systems Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, GermanyTranscutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use.https://www.mdpi.com/1424-8220/24/2/634transcutaneous spinal cord stimulation (tSCS)accelerationmechanomyography (MMG)supervised classificationmultiple sclerosis (MS)spinal cord injury (SCI) |
| spellingShingle | Eira Lotta Spieker Ardit Dvorani Christina Salchow-Hömmen Carolin Otto Klemens Ruprecht Nikolaus Wenger Thomas Schauer Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography Sensors transcutaneous spinal cord stimulation (tSCS) acceleration mechanomyography (MMG) supervised classification multiple sclerosis (MS) spinal cord injury (SCI) |
| title | Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography |
| title_full | Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography |
| title_fullStr | Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography |
| title_full_unstemmed | Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography |
| title_short | Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography |
| title_sort | targeting transcutaneous spinal cord stimulation using a supervised machine learning approach based on mechanomyography |
| topic | transcutaneous spinal cord stimulation (tSCS) acceleration mechanomyography (MMG) supervised classification multiple sclerosis (MS) spinal cord injury (SCI) |
| url | https://www.mdpi.com/1424-8220/24/2/634 |
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