The
recent advances of modern techniques of data fusion allow the
complementarities of different biosignal analyses to be exploited. Thus,
more accurate, structured, and enriched information is possible to be
obtained considering the approach of mixing data from heterogeneous
sensors or different signal modalities. There are several challenging
issues that are increasingly studied from this framework that are still
open and need for research in multidisciplinary areas such as signal
processing, image processing, parallel computing, and clinical
diagnosis. The potential of efficient combination of outstanding
characteristics from different clinical analyses is enormous. Therefore,
theoretical and applicative advances for improvement the know-how in
biosignal data fusion pursuing the solution of real clinical problems
are highly appreciated. This will have potential important benefits for
medical diagnosis and health of the society in general.
The
evolution of signal processing coming together with information
theoretic learning and the progressive increase in computational power
allow integrated solutions for multimodal biosignal analysis using
heterogeneous sensors to be implemented. This context enables to relief
design constraints of theoretically well-defined solutions bearing in
mind that they have to work in real-life. Thus, there are many feasible
options to analyze and combine simultaneously data from different
sources based on signal processing, machine learning, and pattern
recognition methods. In addition, current clinical procedures might be
improved from significant findings and demonstrations of application of
the biosignal fusion methods in clinical cases of study. In summary, the
proposed Special Issue is interesting from theoretical and practical
standpoints providing great opportunities of contribution in a booming
field of research for academicians and practitioners.
Aims and Scope:
Today
data acquisition and biosignal processing are paving the way to the
optimal integration or fusion of complementary data modalities in a wide
variety of clinical settings, including electrocardiography (ECG),
electroencephalography (EEG), electrocorticography (ECoG), magnetic
resonance imaging (MRI), functional MRI (fMRI), positron emission
tomography (PET), diffusion tensor imaging (DTI), etc. Integration can
be performed by exploiting the analyses sequentially or simultaneously,
depending on issues related with synchronization, physical
compatibilities, and standard clinical procedures. Fusion approaches aim
at integrated analyses of data from different modalities, establishing
synergic relationships for improved clinical hypothesis testing and
medical diagnosis.
The heterogeneous nature of data sources from
different clinical analyses and acquisition modalities presents big
challenges such as: (i) extraction of conspicuous features from raw data
in different domains and mapping into a normalized work space; (ii) to
define a processing model for stationary or non-stationary joint
analysis of the biosignals; (iii) possible decoding procedure from the
model parameters to spatial and/or time coordinates; (iv) decision
methods for detection, classification, segmentation, etc.; and (v)
methods for levels of fusion, e.g., early and late fusion. The main
objective of data fusion is to exploit complementary properties of
several single-modality methods in order to improve on each of them
considered separately. In addition, fusion can enable or enhance the
approximation to more complex structured results, e.g., hierarchical
trees and topological networks.
This broad field of research has
been named in different ways, for instance, sensor data fusion, decision
fusion, multimodal fusion, heterogeneous sensor fusion, mixture of
experts, classifier combiners, multi-way signal processing, etc. A
classic example is the simultaneous analysis of EEG and fMRI that
pursues to take advantage of EEG high temporal resolution and fMRI high
spatial resolution. Fused analysis can help to highlight and enhance
singular portions of image frames and EEG traces in dynamic analyses for
different applications such as epileptic seizure focus detection and
localization, cognitive networks estimation, etc. Another example is
hybrid brain computer interfaces that consider the possibility to create
a communication channel between the brain and a computer by using
combination of different biosignals.
Topics of interest in the special issue include (but are not limited to):
- Multimodal fusion: algorithms and clinical experiences.
- Signal processing on graphs for fusion methods.
- Hierarchical approaches: e.g., brain levels of activation: fusion at different cortex depths.
- Computational issues in fusion methods for real-time biosignal analysis.
- Heterogeneous sensor fusion in big data context.
- Tensor methods and constraint techniques for multi-way signal processing.
- Hybrid brain computer interfaces (Multimodal BCI systems).
It
is important to emphasize that this special issue is open to works
dealing with any kind of biomedical data. Furthermore, the fusion
methods do not exclude the possibility to combine more information
coming from the same biosignal, for example EEG rhythms and ERPs, or HR
and HRV from ECG, and so on.
Important Dates:
– Manuscript submission due: Friday, 27 January 2017
– First Round of Reviews: Friday, 21 April 2017
– Publication date: Friday, 16 June 2017
The
Call for Papers of the Special Issue is posted on the journal\’s
website: http://www.hindawi.com/journals/CIN/si/831731/cfp/. Prospective
authors should visit: http://www.hindawi.com/journals/cin/ for
information on paper submission.
Please feel free to forward this message to colleagues interested in submitting papers to the Special Issue.
Guest Editors:
–
Addisson Salazar (Lead Guest Editor), Universitat Politècnica de
València, Institute of Telecommunications and Multimedia Applications,
Valencia, Spain; asalazar@dcom.upv.es
– Vicente Zarzoso, Université
Côte d\’Azur, CNRS, I3S Laboratory, CS 40121, 06903 Sophia Antipolis
Cedex, France; zarzoso@i3s.unice.fr
– Manuel Rosa Zurera, Universidad
de Alcalá, Polytechnic School, Campus Universitario s/n, 28805, Alcalá
de Henares, Spain; manuel.rosa@uah.es
– Luis Vergara, Universitat Politècnica de València, Department of Communications, Valencia, Spain; lvergara@dcom.upv.es