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Academic Press Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG)

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Description

Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) presents a new and innovative method of COPD diagnosis using EMG to analyze sternomastoid muscle activity using features extraction and classification. The book describes the methodology of EMG analysis, the slope-based onset detection algorithm and SEMG analysis in time, frequency and time frequency domain analyses. It also explores the identification of frequencies for single frequency Continuous Wavelet Transform (CWT) analysis and feature extraction and selection for successful classification COPD into its severity grades. The book provides a compilation of all techniques used in the literatures and emphasizes newly proposed techniques for the early detection of COPD. Fully comprehensive, the book includes discussion of limitations of existing methods for COPD diagnosis and introduces new efficient methods for COPD identification, classification and early diagnosis. Review An overview of a new, alternative and innovative method to diagnosis early COPD by analyzing respiratory muscle activity using EMG From the Back Cover <p>COPD is a respiratory disease, commonly diagnosed using spirometry. Spirometry is time consuming and difficult to perform on certain patients. <i>Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) </i>presents a new and innovative method of COPD diagnosis by using EMG to analyze sternomastoid muscle activity using features extraction and classification. It describes the methodology of EMG analysis, slope-based onset detection algorithm and SEMG analysis in time, frequency and time frequency domain analysis. It also explores the identification of frequencies for single frequency Continuous Wavelet Transform (CWT) analysis and feature extraction and selection for successful classification COPD into its severity grades. It provides a compilation of all techniques used in the literatures and emphasizes newly proposed techniques for early detection of COPD. Fully comprehensive, the book includes discussion of limitations of existing methods for COPD diagnosis and introduces new efficient methods for COPD identification, classification and early diagnosis.</p> About the Author Dr. Archana Bajirao Kanwade has completed M.E. in Electronic-Communication in 2009 from Shivaji University. University of Pune has awarded her a PhD degree in Electronics and Telecommunication Engineering in 2020. She has 17 years of teaching experience and 09 years of research experience. She has filed a patent on COPD diagnosis using EMG analysis. She has published more than 35 technical papers, out of which 28 papers are in International journals. She has 4 papers indexed in Scopus Journals and 2 papers in SCI indexed journals. She is reviewer for three Scientific Journals. She has also been invited as resources person for invited talk. She is a member of IEEE SIGHT. Currently she is associated with Sinhgad Institute of Technology and Science, (Affiliated college to S P Pune University), India as Assistant Professor in Electronics and Telecommunication Engineering. She is recognized PG Guide in Electronics and Telecommunication Engineering of Savitribai Phule Pune University. Dr. Vinayak K. Bairagi, is a recognized PhD guide in Savitribai Phule Pune University. He is working as Professor at Department of E electronics and Telecommunication Engg. and actively working as Chairman, IEEE Signal Processing Society Pune Chapter. He has teaching experience of 14 years and research experience of 10 years. He has filed 12 patents and 5 copyrights in technical field. He has published more than 70 papers. He has received IEI national level Young Engineer Award (2014) and ISTE national level Young Researcher Award (2015) for his excellence in the field of engineering. He also has 5 books and 6 book chapters on his credits. His area of interest is Biomedical Signal Proc

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
24 January 2022
Listed Since
28 June 2021

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