Junquera Godoy, Isabel

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0000-0002-6755-5962
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    Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients
    (MDPI AG, 2024-08) Junquera Godoy, Isabel; Martínez de Juan, José Luís; Gonzalez-Lorente, Gemma; Carot Sierra, José Miguel; Gomis-Tena Dolz, Julio; Saiz Rodríguez, Francisco Javier; Garcia-Blasco, Silvia; Pertusa-Manzón, Isabel; Soler-Climent, Esther; Prats Boluda, Gema; Departamento de Ingeniería Electrónica; Departamento de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Centro de Investigación e Innovación en Bioingeniería; Agència Valenciana de la Innovació; Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana
    [EN] Diabetic peripheral neuropathy (DPN) is a prevalent complication of chronic diabetes mellitus and has a significant impact on quality of life. DPN typically manifests itself as a symmetrical, length-dependent sensorimotor polyneuropathy with severe effects on gait. Surface electromyography (sEMG) is a valuable low-cost tool for assessing muscle activation patterns and precise identification of abnormalities. For the present study, we used information theory methods, such as cross-correlation (CC), normalized mutual information (NMI), conditional granger causality (CG-Causality), and transfer entropy (TE), to evaluate muscle network connectivity in three population groups: 33 controls (healthy volunteers, CT), 10 diabetic patients with a low risk of DPN (LW), and 17 moderate/high risk patients (MH). The results obtained indicated significant alterations in the intermuscular coupling mechanisms due to diabetes and DPN, with the TE group showing the best performance in detecting differences. The data revealed a significant increase in information transfer and muscle connectivity in the LW group over the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings highlight the sEMG coupling metrics¿ potential to reveal neuromuscular mechanisms that could aid the development of targeted rehabilitation strategies and help monitor DPN patients.
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    Publication
    Analysis of surface electromyographic parameters for the assessment of muscle fatigue during moderate exercises
    (Universidad de Valladolid, 2022-11-25) Junquera Godoy, Isabel; Gomis-Tena Dolz, Julio; Martínez de Juan, José Luís; Saiz Rodríguez, Francisco Javier; Prats Boluda, Gema; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Escuela Técnica Superior de Ingeniería Industrial; Centro de Investigación e Innovación en Bioingeniería; Agència Valenciana de la Innovació
    [EN] Muscle fatigue is a neuromuscular phenomenon which prevents the muscles to generate the required force. Although there are multiple studies about detecting this condition with surface electromyography, this method has still some limitations. The objective of this study was to determine the viability of some biomarkers as early indicators of fatigue during moderate exercise. Eight healthy volunteers performed 2 dynamic exercises involving the flexo-extension of the knee lifting 2 and 4kg attached to the ankle. A bipolar electromyographic signal was recorded simultaneously from: Rectus Femoris, Vastus Lateralis and Biceps Femoris. Muscle activations were extracted by supervised automatic segmentation and their root mean square, bandwidth, entropy and spectral moment were calculated. The maximum of the normalized coefficient of the linear cross-correlation between each muscle pair were also computed as well as the slope throughout the execution of the exercise repetitions of all above mentioned parameters. Results from 2kg exercise did not reveal clear signs of fatigue. By contrast those from 4kg showed an increase of the root mean square and spectral moment ratio, and decrease of bandwidth, sample entropy and linear cross correlation coefficient, pointing out fatigue. In this study, it has been confirmed the viability of the cross-correlation technique as a possible new biomarker related to the first signs of muscle fatigue.