Advances in biomedical signal and image processing â A systematic review
Biomedical signal and image processing establish a dynamic area of specialization in both academic as well as research aspects of biomedical engineering. The concepts of signal and image processing have been widely used for extracting the physiological information in implementing many clinical proce...
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doaj-1743f4f664da4577bd12444b6dad71e52020-11-24T21:19:13ZengElsevierInformatics in Medicine Unlocked2352-91482017-01-0181319Advances in biomedical signal and image processing â A systematic reviewJ. Rajeswari0M. Jagannath1School of Electronics Engineering, VIT University Chennai, Tamilnadu, IndiaCorresponding author:; School of Electronics Engineering, VIT University Chennai, Tamilnadu, IndiaBiomedical signal and image processing establish a dynamic area of specialization in both academic as well as research aspects of biomedical engineering. The concepts of signal and image processing have been widely used for extracting the physiological information in implementing many clinical procedures for sophisticated medical practices and applications. In this paper, the relationship between electrophysiological signals, i.e., electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and functional image processing and their derived interactions have been discussed. Examples have been investigated in various case studies such as neurosciences, functional imaging, and cardiovascular system, by using different algorithms and methods. The interaction between the extracted information obtained from multiple signals and modalities seems to be very promising. The advanced algorithms and methods in the area of information retrieval based on time-frequency representation have been investigated. Finally, some examples of algorithms have been discussed in which the electrophysiological signals and functional images have been properly extracted and have a significant impact on various biomedical applications. Keywords: Biomedical signals and images, Processing, Analysishttp://www.sciencedirect.com/science/article/pii/S2352914817300242 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. Rajeswari M. Jagannath |
spellingShingle |
J. Rajeswari M. Jagannath Advances in biomedical signal and image processing â A systematic review Informatics in Medicine Unlocked |
author_facet |
J. Rajeswari M. Jagannath |
author_sort |
J. Rajeswari |
title |
Advances in biomedical signal and image processing â A systematic review |
title_short |
Advances in biomedical signal and image processing â A systematic review |
title_full |
Advances in biomedical signal and image processing â A systematic review |
title_fullStr |
Advances in biomedical signal and image processing â A systematic review |
title_full_unstemmed |
Advances in biomedical signal and image processing â A systematic review |
title_sort |
advances in biomedical signal and image processing â a systematic review |
publisher |
Elsevier |
series |
Informatics in Medicine Unlocked |
issn |
2352-9148 |
publishDate |
2017-01-01 |
description |
Biomedical signal and image processing establish a dynamic area of specialization in both academic as well as research aspects of biomedical engineering. The concepts of signal and image processing have been widely used for extracting the physiological information in implementing many clinical procedures for sophisticated medical practices and applications. In this paper, the relationship between electrophysiological signals, i.e., electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and functional image processing and their derived interactions have been discussed. Examples have been investigated in various case studies such as neurosciences, functional imaging, and cardiovascular system, by using different algorithms and methods. The interaction between the extracted information obtained from multiple signals and modalities seems to be very promising. The advanced algorithms and methods in the area of information retrieval based on time-frequency representation have been investigated. Finally, some examples of algorithms have been discussed in which the electrophysiological signals and functional images have been properly extracted and have a significant impact on various biomedical applications. Keywords: Biomedical signals and images, Processing, Analysis |
url |
http://www.sciencedirect.com/science/article/pii/S2352914817300242 |
work_keys_str_mv |
AT jrajeswari advancesinbiomedicalsignalandimageprocessingaasystematicreview AT mjagannath advancesinbiomedicalsignalandimageprocessingaasystematicreview |
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