Improved Spontaneous Activity Maps of Resting Skeletal Musculature by surface 
EMG-based Contraction Pattern Classification

Schwartz, M; Steidle, G; Martirosian, P; Erb, M; Yang, B; Scheffler, K; Schick, F

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Improved Spontaneous Activity Maps of Resting Skeletal Musculature by surface EMG-based Contraction Pattern Classification

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Scheffler, K
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Schwartz, M., Steidle, G., Martirosian, P., Erb, M., Yang, B., Scheffler, K., et al. (2018). Improved Spontaneous Activity Maps of Resting Skeletal Musculature by surface EMG-based Contraction Pattern Classification. Poster presented at Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, France.

Zitierlink: https://hdl.handle.net/21.11116/0000-0001-7DC4-6

Zusammenfassung

Reliable assessment and analysis of spontaneous mechanical activities in musculature (SMAM) visible in repetitive DWI is a relatively new technique for non-invasive characterization of skeletal musculature. To correct for data corrupted by intentional contractions, a surface electromyography-based contraction state analysis was investigated to reject undesired DWI data. It is demonstrated that the presented method enables a more reliable quantification of SMAMs and improved spontaneous activity maps.