Multilayer machine learning algorithm to classify diabetic type on knee dataset

被引：0

作者：

Anjaneya, L. H. ^{[1
]}

Holi, Mallikarjun S. ^{[2
]}

机构：

[1] Bapuji Inst Engn & Technol, Dept Biomed Engn, Davangere, Karnataka, India

[2] UBDT Coll Engn, Dept Elect & Instrumentat Engn, Davangere, Karnataka, India

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ELECTRONICS, INFORMATION & COMMUNICATION TECHNOLOGY (RTEICT) | 2016年

关键词：

Diabetes; EMG signal; time domain feature; frequency domain; classification; neural network; NEUROPATHY; EMG;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

since last decade, the diabetes risks are increasing in children and adults. Various approaches have been proposed for early detection of the diabetes and prevention on it. Some methods use EMG signals for diabetes classification, due to motion artifacts in the EMG signals during acquisition of signal, these approaches are not able to classify the signal efficiently. To overcome this we propose anew method by considering time domain and frequency domain features of the EMG signals and to perform the classification we use neural network. This method is executed using MATLAB tool and simulation study shows the accuracy of proposed approach is 97.05%.

引用

页码：584 / 587

页数：4

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