利用高效统计特征实时识别表面肌电信号中受污染的噪声类型

IF 1.7 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Pornchai Phukpattaranont , Nantarika Thiamchoo , Paramin Neranon
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引用次数: 0

摘要

表面肌电图(EMG)信号中不同类型的噪声可能会降低识别性能。要去除噪声,首先必须识别噪声的类型。在本文中,我们提出了一种实时高效的系统,用于识别干净的肌电信号和受到以下三种噪声中任何一种噪声污染的肌电信号:心电图干扰、尖峰噪声和电源线干扰。峰度和偏度这两个统计描述符被用作级联二次判别分析分类器的输入特征。提出了一种有效的峰度和偏度计算简化方法,可以减少计算时间和内存存储。基于 ATmega 2560 微控制器的实时系统的实验结果表明,峰度和倾斜度值的均方根误差在传统技术和所提出的高效技术之间分别为 0.08 和 0.09。二次判别分析分类器的五倍交叉验证识别准确率为 96.00%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

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来源期刊
Medical Engineering & Physics
Medical Engineering & Physics 工程技术-工程:生物医学
CiteScore
4.30
自引率
4.50%
发文量
172
审稿时长
3.0 months
期刊介绍: Medical Engineering & Physics provides a forum for the publication of the latest developments in biomedical engineering, and reflects the essential multidisciplinary nature of the subject. The journal publishes in-depth critical reviews, scientific papers and technical notes. Our focus encompasses the application of the basic principles of physics and engineering to the development of medical devices and technology, with the ultimate aim of producing improvements in the quality of health care.Topics covered include biomechanics, biomaterials, mechanobiology, rehabilitation engineering, biomedical signal processing and medical device development. Medical Engineering & Physics aims to keep both engineers and clinicians abreast of the latest applications of technology to health care.
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