Computational Intelligence Methods on Biomedical Signal Analysis
COMPUTATIONAL INTELLIGENCE METHODS ON BIOMEDICAL SIGNAL ANALYSIS AND DATA MINING IN MEDICAL RECORDS
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This book is centered around the development andapplication of computationally effectivesolutions based on artificial neural networks (ANN)for biomedical signal analysis and datamining on medical records. The description of someANN types is followed by the description of a newmodel design for pattern classification. A detaileddescription of the wavelets theory is includedtogether with an example of it''s practical use for feature pre-processing. In the field of BiomedicalEngineering, the goal of a researcher is to providethe clinician with the best possibleinformation needed to make an accurat...
This book is centered around the development and
application of computationally effective
solutions based on artificial neural networks (ANN)
for biomedical signal analysis and data
mining on medical records. The description of some
ANN types is followed by the description of a new
model design for pattern classification. A detailed
description of the wavelets theory is included
together with an example of it''s practical use for
feature pre-processing. In the field of Biomedical
Engineering, the goal of a researcher is to provide
the clinician with the best possible
information needed to make an accurate diagnosis.
The new learning paradigms proposed, based on
semi-supervised learning are imposing themselves as
an emergent powerful modeling methodology to be
applied not only on unidimensional medical signals
(like ECG or EEG signals) but also in image analysis
and many other engineering fields.
application of computationally effective
solutions based on artificial neural networks (ANN)
for biomedical signal analysis and data
mining on medical records. The description of some
ANN types is followed by the description of a new
model design for pattern classification. A detailed
description of the wavelets theory is included
together with an example of it''s practical use for
feature pre-processing. In the field of Biomedical
Engineering, the goal of a researcher is to provide
the clinician with the best possible
information needed to make an accurate diagnosis.
The new learning paradigms proposed, based on
semi-supervised learning are imposing themselves as
an emergent powerful modeling methodology to be
applied not only on unidimensional medical signals
(like ECG or EEG signals) but also in image analysis
and many other engineering fields.
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