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