Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. "Juan de Dios Bátiz" s/n esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Ciudad de México C.P. 07738, Mexico.
Departamento de Ciencias e Ingenierías, Universidad Iberoamericana Puebla, Blvrd del Niño Poblano 2901, Reserva Territorial Atlixcáyotl, Centro Comercial Puebla, San Andrés Cholula 72810, Puebla, Mexico.
Sensors (Basel). 2019 Jan 24;19(3):475. doi: 10.3390/s19030475.
Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
肌电图信号分类的应用范围很广，从不同肌肉疾病的临床诊断到生物医学工程，肌电图信号作为控制假肢设备的输入已经成为研究的热点。分类这些信号的挑战在于所提出的算法的准确性和在硬件中实现的可能性。本文考虑了肌电图信号分类的问题，通过提出的信号处理和特征提取阶段来解决，重点在于信号模型和时域特性，以获得更好的分类准确性。该提案考虑了一种简单的预处理技术，该技术产生适合特征提取和 Burg 反射系数的学习和分类模式的信号。与使用的时域特征相比，这些系数产生了具有竞争力的分类率。有时，肌电图信号的特征提取表明，该过程可以忽略对机器学习模型不太有用的特征。使用特征选择算法可以用尽可能少的特征提供更高的分类性能。这些算法在低模式维数下实现了高达 100%的高分类率，同时对手部运动识别的其他种类的不相关属性也具有很高的分类率。
