A Gestation Diabetic Detection Technique using Muscle Energy Derived from Surface EMG

A Gestation Diabetic Detection Technique using Muscle Energy Derived from Surface EMG

Anjaneya L.H

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Mallikarjun S. Holi

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Dr. S. Chandrasekhar

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A Gestation Diabetic Detection Technique using Muscle Energy Derived from Surface EMG

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Abstract

Electromyogram (EMG) is one among the important biopotential signal reflecting the human skeletal muscle activity. EMG signals can be used for many biomedical applications pertaining to diagnosis and therapy of musculoskeletal and rheumatological problems. EMG signals are complex in nature and require advanced techniques for analysis, such as decomposition, detection, processing, and classification. Diabetes mellitus is a chronic metabolic disorder characterized by elevated levels of blood glucose. The musculoskeletal system can be affected by diabetes in a number of ways. The main aim of the paper is to identify the diabetic patient and show the classification performance of the proposed framework. In this paper EMG signal is investigated by feature extraction and are classified into normal and diabetic for comprehension of EMG signal. The primary point of this work is to recognize the diabetes utilizing different elements and to demonstrate the performance of the proposed framework. The obtained results demonstrate that the extracted feature in proposed framework displays better performance for classification the EMG signal contrasted with alternate elements. Based on the impacts of features on the EMG signal classification, different results were obtained through analysis of the SVM Classification. Experimental study shows that the proposed method’s classification accuracy is 98.98%.

References

7 Cites in Article

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K Nishikawa,K Kuribayashi (1991). Neural network application to a discrimination system for EMG-controlled prostheses.
Xiao Hu,; Qun Yu,; Waixi Liu,Jian Qin (2008). Feature Extraction of Surface EMG Signal Based on Wavelet Coefficient Entropy.
S Boostani,R Shabani,S Parsaei,H (2012). A new feature selection method for classification of EMG signals.
Gaoxiang Zhaojieju,; Ouyang,M Wilamowska-Korsak,Honghai,Liu (2013). Surface EMG Based Hand Manipulation Identification Via Nonlinear Feature Extraction and Classification.
N Artug,I Goker,B Bolat,G Tulum,O Osman,M Baslo (2014). Feature extraction and classification of neuromuscular diseases using scanning EMG.
Yong Ning,Xiangjun Zhu,Shanan Zhu,Yingchun Zhang (2015). Surface EMG Decomposition Based on <italic>K</italic>-means Clustering and Convolution Kernel Compensation.
Sivarit Sultornsanee,Ibrahim Zeid,Sagar Kamarthi (2011). Classification of Electromyogram Using Recurrence Quantification Analysis.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Anjaneya L.H. 2016. \u201cA Gestation Diabetic Detection Technique using Muscle Energy Derived from Surface EMG\u201d. Global Journal of Medical Research - K: Interdisciplinary GJMR-K Volume 15 (GJMR Volume 15 Issue K6).

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GJMR Volume 15 Issue K6

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Journal Specifications

Crossref Journal DOI 10.17406/gjmra

Print ISSN 0975-5888

e-ISSN 2249-4618

Keywords

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GJMR-K Classification NLMC Code: QZ 4

Submission ReceivedFebruary 4, 2015
Peer Review Double Blind
Handling Editor
Accepted March 2, 2015
Published March 15, 2015

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v1.2

Issue date

January 12, 2016

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Article Matrices

Total Score: 108

Country: India

Subject: Global Journal of Medical Research - K: Interdisciplinary

Authors: Anjaneya L.H, Mallikarjun S. Holi, Dr. S. Chandrasekhar (PhD/Dr. count: 1)

View Count (all-time): 200

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Total Downloads (simulated): 1992

Publish Date: 2016 01, Tue

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Total Views: 4013

Total Downloads: 1992

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