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Signal processing in medicine and bi...

IEEE Signal Processing in Medicine and Biology Symposium ((2018 :)

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Signal processing in medicine and biology = emerging trends in research and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Signal processing in medicine and biology/ edited by Iyad Obeid, Ivan Selesnick, Joseph Picone.
其他題名:	emerging trends in research and applications /
其他作者:	Obeid, Iyad.
團體作者:	IEEE Signal Processing in Medicine and Biology Symposium
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	vii, 281 p. :ill., digital ;24 cm.
內容註:	Chapter 1. An Analysis of Automated Parkinson's Diagnosis Using Voice: Methodology and Future Directions -- Chapter 2. Noninvasive Vascular Blood Sound Monitoring Through Flexible Microphone -- Chapter 3. The Temple University Hospital Digital Pathology Corpus -- Chapter 4. Transient Artifacts Suppression in Time Series via Convex Analysis -- Chapter 5. The Hurst Exponent - A Novel Approach for Assessing Focus During Trauma Resuscitation -- Chapter 6. Gaussian Smoothing Filter For Improved EMG Signal Modeling -- Chapter 7. Clustering of SCG Events Using Unsupervised Machine Learning -- Chapter 8. Deep Learning Approaches for Automated Seizure Detection from Scalp Electroencephalograms.
Contained By:	Springer eBooks
標題:	Signal processing - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-36844-9
ISBN:	9783030368449

Signal processing in medicine and biology = emerging trends in research and applications /
Signal processing in medicine and biologyemerging trends in research and applications /[electronic resource] :edited by Iyad Obeid, Ivan Selesnick, Joseph Picone. - Cham :Springer International Publishing :2020. - vii, 281 p. :ill., digital ;24 cm.

Chapter 1. An Analysis of Automated Parkinson's Diagnosis Using Voice: Methodology and Future Directions -- Chapter 2. Noninvasive Vascular Blood Sound Monitoring Through Flexible Microphone -- Chapter 3. The Temple University Hospital Digital Pathology Corpus -- Chapter 4. Transient Artifacts Suppression in Time Series via Convex Analysis -- Chapter 5. The Hurst Exponent - A Novel Approach for Assessing Focus During Trauma Resuscitation -- Chapter 6. Gaussian Smoothing Filter For Improved EMG Signal Modeling -- Chapter 7. Clustering of SCG Events Using Unsupervised Machine Learning -- Chapter 8. Deep Learning Approaches for Automated Seizure Detection from Scalp Electroencephalograms.

This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology. Covers traditional signal processing topics within biomedicine Promotes collaboration between healthcare practitioners and signal processing researchers Presents tutorials and examples of successful applications.

ISBN: 9783030368449

Standard No.: 10.1007/978-3-030-36844-9doiSubjects--Topical Terms:

572886
Signal processing
--Congresses.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Signal processing in medicine and biology = emerging trends in research and applications /
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505 0 $a Chapter 1. An Analysis of Automated Parkinson's Diagnosis Using Voice: Methodology and Future Directions -- Chapter 2. Noninvasive Vascular Blood Sound Monitoring Through Flexible Microphone -- Chapter 3. The Temple University Hospital Digital Pathology Corpus -- Chapter 4. Transient Artifacts Suppression in Time Series via Convex Analysis -- Chapter 5. The Hurst Exponent - A Novel Approach for Assessing Focus During Trauma Resuscitation -- Chapter 6. Gaussian Smoothing Filter For Improved EMG Signal Modeling -- Chapter 7. Clustering of SCG Events Using Unsupervised Machine Learning -- Chapter 8. Deep Learning Approaches for Automated Seizure Detection from Scalp Electroencephalograms.
520 $a This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology. Covers traditional signal processing topics within biomedicine Promotes collaboration between healthcare practitioners and signal processing researchers Presents tutorials and examples of successful applications.
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