EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach

• Published in: Medical & biological engineering & computing Vol. 60; no. 6; pp. 1659 - 1673
• Main Authors: Piatkowska, Weronika, Spolaor, Fabiola, Guiotto, Annamaria, Guarneri, Gabriella, Avogaro, Angelo, Sawacha, Zimi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022; Springer Nature B.V
• Subjects: Algorithms; Ankle; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cardiovascular disease; Climbing; Cluster analysis; Clustering; Computer Applications; Deactivation; Diabetes; Diabetes mellitus; Diabetic neuropathy; Electromyography; Gait; Human Physiology; Hypotheses; Imaging; Metabolic disorders; Muscle function; Muscles; Original; Original Article; Parameters; Radiology; Rehabilitation; Ulcers; Diabetic neuropathies; Stair climbing; Electromyography; Clustering; Diabetes mellitus; Gait analysis
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-022-02559-3

The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments. Graphical abstract