editorial biomedical signal processing and modeling...

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2013, Article ID 173469, 2 pageshttp://dx.doi.org/10.1155/2013/173469

EditorialBiomedical Signal Processing and Modeling Complexity ofLiving Systems 2013

Carlo Cattani,1 Radu Badea,2 Sheng-Yong Chen,3 and Maria Crisan4

1 Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano (SA), Italy2 Department of Clinical Imaging Ultrasound, “IuliuHatieganu”University ofMedicine and Pharmacy, 400000 Cluj-Napoca, Romania3 College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China4Department of Histology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania

Correspondence should be addressed to Carlo Cattani; [email protected]

Received 7 November 2013; Accepted 7 November 2013

Copyright © 2013 Carlo Cattani et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Biomedical signal processing aims to provide significantinsights into the analysis of the information flows fromphysiological signals. As such, it can be understood as a spe-cific interdisciplinary scientific discipline. In fact, biomedicalsignals extract information from complex biological modelsthus proposing challenging mathematical problems, whosesolution has to be interpreted from a biological point of view.The focus of this special issue is the mathematical analysisand modeling of time series in living systems and biomedicalsignals. The main steps of the biomedical signals processingare as follows.

(1) Signal processing of biological data implies manydifferent interesting problems dealing with signalacquisition, sampling, and quantization. The noisereduction and similar problems as image enhance-ment are a fundamental step in order to avoid signif-icant errors in the analysis of data. Feature extractionis themost important part of the analysis of biologicalsignals because of the importance which is clinicallygiven to even the smallest singularity of the image(signal).

(2) Information flows from signals imply the modelingand analysis of spatial structures, self-organization,environmental interaction, behavior, and develop-ment. Usually this is related to the complexity analysisin the sense that the information flows come fromcomplex systems so that signals show typical features,such as randomness, nowhere differentiability, fractal

behavior, and self-similarity which characterize com-plex systems. As a consequence typical parametersof complexity such as entropy, power spectrum,randomness, and multifractality play a fundamentalrole, because their values can be used to detect theemergence of clinical pathologies.

(3) Physiological signals usually come as 1D time series or2D images. The most known biosignals are based onsounds (ultrasounds), electromagnetic pulses (ECG,EEG, and MRI), radiation (X-ray and CT), images(microscopy), and many others. The clinical signalunderstanding of them follows from the correct, froma mathematical point of view, interpretation of thesignal.

(4) Physiological signals are detected and measured bymodern biomedical devices. Amongothers, one of themain problems is to optimize both the investigationmethods and the device performances.

The papers selected for this special issue represent agood panel in recent challenges. They represent some of themost recent advances inmany different clinical investigationsdevoted to the analysis of complexity in living systems, like,for example, network science, dynamical systems theory,dynamical complexity, pattern analysis, implementation, andalgorithms. They cannot be exhaustive because of the rapidgrowing both ofmathematical methods of signal analysis andof the technical performances of devices. However they aim

2 Computational and Mathematical Methods in Medicine

to offer a wide introduction on a multidisciplinary disciplineand to give some of themore interesting and original solutionof challenging problems. Among them themost fascinating isto understanding of the biological structure and organization,the intracellular exchange of information, the localization ofinformation in cell nuclei, and in particular the unrevealing ofthe mathematical information (functionally related) contentin DNA.

This special issue contains 23 papers. In the category ofmodeling dynamical complexity, L.-P. Tian et al. make com-plex analysis and parameter estimation of dynamicmetabolicsystems. M. Adib and E. Cretu present wavelet-based artifactidentification and separation technique for EEG signalsduring galvanic vestibular stimulation. X. Wu and N. Wuuse thresholded two-phase test sample representation foroutlier rejection in biological recognition. Z.Ma et al. proposenonlinear Radon transform using Zernike moment for shapeanalysis. C.-Y. Liou et al. study structural complexity of DNAsequence.M. Li et al. investigate heavy-tailed prediction errorin predicting biomedical signals of 1/f noise type. X. Wanget al. propose reliable RANSAC using a novel preprocessingmodel. J. Zheng et al. give fast discriminative stochasticneighbor embedding analysis.

In the category of methods for analysis of dynamicalcomplexity, R. Schiavetti and G. Sannino give in vitro evalu-ation of ferrule effect and depth of post insertion on fractureresistance of fiber posts. G. Sannino and G. Vairo makecomparative evaluation of osseointegrated dental implantsbased on platform-switching concept and find influenceof diameter, length, thread shape, and in-bone positioningdepth on stress-based performance. H.-T. Wu et al. usemultiscale cross-approximate entropy analysis as a measureof complexity among the aged and diabetic. T. Kauppi et al.construct benchmark databases and protocols for medicalimage analysis with diabetic retinopathy. B. Zhu et al. presenta novel automatic detection system for ECG arrhythmiasusing maximum margin clustering with an immune evolu-tionary algorithm. Y.-S. Juang et al. study optimization andimplementation of scaling-free CORDIC-based direct digitalfrequency synthesizer for body care area network systems. Z.Bian et al. find the effect of Pilates training on alpha rhythm.

In the category of biomedical signal analysis, A. F.Badea et al. give fractal analysis of elastographic images forautomatic detection of diffuse diseases of salivary glands. Q.Guan et al. present Bayes clustering and structural supportvectormachines for segmentation of carotid artery plaques inmulticontrastMRI. J. Zhang et al. present self-adaptive imagereconstruction inspired by insect compound eye mechanism.X. Zheng et al. improve spatial adaptivity of nonlocal meansin low-dosed CT imaging using pointwise fractal dimen-sion. N. Wu et al. study three-dimensional identification ofmicroorganisms using a digital holographic microscope. Y.Tang et al. propose a computational approach to seasonalchanges of living leaves. L. Lin et al. study plane-basedsampling for a ray casting algorithm in sequential medicalimages. Y.-S. Juang et al. propose a rate-distortion-basedmerging algorithm for compressed image segmentation.

As already mentioned, the topics and papers are not anexhaustive representation of the area of biomedical signal

processing and modeling complexity of living systems. How-ever we believe that we have succeeded to collect some ofthe most significant papers in this area aiming to improvethe scientific debate in the modern interdisciplinary field ofbiomedical signal processing.

Acknowledgments

We thank the authors for their excellent contributions anddiscussions onmodern topics.The reviewers also deserve ourspecial thanks for their useful comments on the papers thathelped the authors to clarify some crucial points.

Carlo CattaniRadu Badea

Sheng-Yong ChenMaria Crisan

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