A Longitudinal Monitoring of Muscle Weakness in ALS Using Portable sEMG and Data Mining
DOI:
https://doi.org/10.61467/2007.1558.2027.v18i1.1515Keywords:
Surface electromyography, Amyotrophic Lateral Sclerosis, data mining, clustering, decision trees, supervised classification, Electromiografía de superficie, esclerosis lateral amiotrófica, minería de datos, árboles de decisiónAbstract
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease characterized by the degeneration of upper and lower motor neurons, causing a gradual loss of muscle strength, functional limitations and a reduction in quality of life. Due to the progressive nature of the disease, it is essential to have continuous monitoring mechanisms capable of identifying functional changes at an early stage and in an objective manner. In this context, surface electromyography (sEMG) represents a non-invasive alternative for recording muscular electrical activity and analyzing patterns associated with muscle weakness. This article proposes a data-mining-based analytical model for the longitudinal monitoring of patients with ALS using sEMG signals obtained through an eight-channel Myo Armband device. The methodology integrates multichannel acquisition, temporal segmentation, feature extraction, exploratory clustering and supervised classification through decision trees. The Root Mean Square (RMS), Mean Absolute Value (MAV) and Waveform Length (WL) indicators are mathematically formalized, as are the partition criteria employed by the ID3, C4.5 and CART algorithms. The research demonstrates that CART constitutes the most suitable alternative for this context due to its capacity to work with continuous variables, perform cost-complexity pruning and generate interpretable rules for clinical specialists. The results show that the progressive decrease in amplitude and muscular energy can be used as a complementary indicator for monitoring the functional evolution associated with ALS. Likewise, the rules obtained make it possible to identify patterns of muscle weakness and potential anomalous events observable in the electromyographic record.
Spanish-language metadata / Metadatos en español
Título en español:
Monitorización longitudinal de la debilidad muscular en la ELA mediante sEMG portátil y minería de datos
Resumen:
La esclerosis lateral amiotrófica (ELA) es una enfermedad neurodegenerativa progresiva caracterizada por la degeneración de las neuronas motoras superiores e inferiores, que provoca una pérdida gradual de la fuerza muscular, limitaciones funcionales y una reducción de la calidad de vida. Debido al carácter progresivo de la enfermedad, es esencial disponer de mecanismos de monitorización continua capaces de identificar cambios funcionales de forma temprana y objetiva. En este contexto, la electromiografía de superficie (sEMG) representa una alternativa no invasiva para registrar la actividad eléctrica muscular y analizar patrones asociados con la debilidad muscular.
Este artículo propone un modelo analítico basado en minería de datos para la monitorización longitudinal de pacientes con ELA mediante señales sEMG obtenidas a través de un dispositivo Myo Armband de ocho canales. La metodología integra adquisición multicanal, segmentación temporal, extracción de características, agrupamiento exploratorio y clasificación supervisada mediante árboles de decisión. Se formalizan matemáticamente los indicadores de valor cuadrático medio (RMS), valor absoluto medio (MAV) y longitud de forma de onda (WL), así como los criterios de partición empleados por los algoritmos ID3, C4.5 y CART.
La investigación demuestra que CART constituye la alternativa más adecuada para este contexto debido a su capacidad para trabajar con variables continuas, realizar poda por complejidad de coste y generar reglas interpretables para especialistas clínicos. Los resultados muestran que la disminución progresiva de la amplitud y de la energía muscular puede utilizarse como indicador complementario para monitorizar la evolución funcional asociada con la ELA. Asimismo, las reglas obtenidas permiten identificar patrones de debilidad muscular y posibles eventos anómalos observables en el registro electromiográfico.
Palabras Claves:
Electromiografía de superficie, esclerosis lateral amiotrófica, minería de datos, agrupamiento, árboles de decisión, clasificación supervisada.
Smart citations:
SciteAI.
Dimensions.
Open Alex.
References
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth International Group.
De Luca, C. J. (1997). The use of surface electromyography in biomechanics. Journal of Applied Biomechanics, 13(2), 135–163. https://doi.org/10.1123/jab.13.2.135
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://doi.org/10.1016/C2009-0-61819-5
Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000). Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology, 10(5), 361–374. https://doi.org/10.1016/S1050-6411(00)00027-4
Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39(4), 261–283. https://doi.org/10.1007/s10462-011-9272-4
Medical Research Council. Nerve Injuries Committee. (1943). Aids to the investigation of peripheral nerve injuries (2nd rev. ed.; War Memorandum No. 7). H.M. Stationery Office.
Merletti, R., & Parker, P. A. (Eds.). (2004). Electromyography: Physiology, engineering, and noninvasive applications. Wiley-IEEE Press. https://doi.org/10.1002/0471678384
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420–7431. https://doi.org/10.1016/j.eswa.2012.01.102
Phinyomark, A., Khushaba, R. N., & Scheme, E. (2018). Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors, 18(5), Article 1615. https://doi.org/10.3390/s18051615
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann. https://doi.org/10.1016/C2009-0-27846-9
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann. https://doi.org/10.1016/C2015-0-02071-8
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.
Zhang, Z., Yang, K., Qian, J., & Zhang, L. (2019). Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network. Sensors, 19(14), Article 3170. https://doi.org/10.3390/s19143170
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
