DWT based Identification of Amyotrophic Lateral Sclerosis Using Surface EMG Signal

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Archana Bhaskarrao Sonone
Archana Bhaskarrao Sonone

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DWT based Identification of Amyotrophic Lateral Sclerosis Using Surface EMG Signal

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Abstract

In the process of identification of Amyotrophic Lateral Sclerosis (ALS) which is a motor neuron disorder, extraction of feature is the most important step. In this work normal and ALS class for identification and monitoring have been included. Analysis of surface electromyography (sEMG) signal for ALS identification using discrete wavelet transform is most simple and powerful method being used all over the world. Time domain parameters, like Zero Crossing Rate (ZCR) and Root Mean Square (RMS) and frequency domain parameters like Mean Frequency (MF) and Waveform Length (WL) are considered. Threshold values for the above mentioned parameters are calculated for both the normal and ALS classes. Discrete Wavelet Transform (DWT) parameters are considered and their threshold values are also calculated for both normal and ALS classes. Surface EMG (sEMG) signal database of normal and ALS patients for both male and female is considered.

References

11 Cites in Article
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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.

Data Availability

Not applicable for this article.

How to Cite This Article

Archana Bhaskarrao Sonone. 2017. \u201cDWT based Identification of Amyotrophic Lateral Sclerosis Using Surface EMG Signal\u201d. Global Journal of Medical Research - F: Diseases GJMR-F Volume 17 (GJMR Volume 17 Issue F2): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjmra

Print ISSN 0975-5888

e-ISSN 2249-4618

Keywords
Classification
GJMR-F Classification: NLMC Code: WE 552
Version of record

v1.2

Issue date

August 28, 2017

Language
en
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Published Article

In the process of identification of Amyotrophic Lateral Sclerosis (ALS) which is a motor neuron disorder, extraction of feature is the most important step. In this work normal and ALS class for identification and monitoring have been included. Analysis of surface electromyography (sEMG) signal for ALS identification using discrete wavelet transform is most simple and powerful method being used all over the world. Time domain parameters, like Zero Crossing Rate (ZCR) and Root Mean Square (RMS) and frequency domain parameters like Mean Frequency (MF) and Waveform Length (WL) are considered. Threshold values for the above mentioned parameters are calculated for both the normal and ALS classes. Discrete Wavelet Transform (DWT) parameters are considered and their threshold values are also calculated for both normal and ALS classes. Surface EMG (sEMG) signal database of normal and ALS patients for both male and female is considered.

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DWT based Identification of Amyotrophic Lateral Sclerosis Using Surface EMG Signal

Archana Bhaskarrao Sonone
Archana Bhaskarrao Sonone

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