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

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 pr...

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Published in:Medical & Biological Engineering & Computing Vol. 60; no. 6; pp. 1659 - 1674
Main Authors: Piatkowska, Weronika, Spolaor, Fabiola, Guiotto, Annamaria, Guarneri, Gabriella, Avogaro, Angelo, Sawacha, Zimi
Format: Journal Article
Published: Springer Nature Jun2022
Online Access:View this record in EBSCOhost
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      dt: Jun2022
      vid: 60
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      pub: Springer Nature
      place: New York, New York
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        NLM35428958
        10.1007/s11517-022-02559-3
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        atl: EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach.
      aug:
        au:
          Piatkowska, Weronika
          Spolaor, Fabiola
          Guiotto, Annamaria
          Guarneri, Gabriella
          Avogaro, Angelo
          Sawacha, Zimi
        affil: Department of Information Engineering, University of Padova, Via Gradenigo 6B, 35131, Padova, Italy
      sug:
        subj:
          Diabetes Mellitus
          Walking Physiology
          Electromyography Methods
          Cluster Analysis
          Muscle, Skeletal Physiology
          Gait Physiology
          Scales
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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