Guest Editorial Introduction to the Special Section on Computational Intelligence in Medical Systems

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<ul><li><p>IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO. 5, SEPTEMBER 2009 667</p><p>Guest EditorialIntroduction to the Special Section on Computational</p><p>Intelligence in Medical Systems</p><p>I. INTRODUCTION</p><p>R ECENT technological advances in medicine have facil-itated the development of complex biomedical systemsincluding sophisticated biomedical signal devices and instru-ments, medical imaging equipment, and computer-aided diag-nosis (CAD) tools enabling the better delivery of healthcareservices. In parallel, computational intelligence, incorporat-ing neural computing, fuzzy systems, evolutionary computing,and more recently, rough sets, and autoimmune systems haveemerged as promising tools for the development, application,and implementation of intelligent systems.</p><p>In the last ten years, there has been a significant effort inthe application of computational intelligent techniques in nu-merous biomedical systems. These cover applications in med-ical decision-making [1], biosignal analysis, and biomedicalengineering at large [2], medical imaging [3][5], bioinformat-ics [6], [7], and others. All these systems underlie the impact ofthese technologies in the biomedical domain.</p><p>The aim of this special issue is to focus on the most recentapplications of computational intelligent systems in medicine.Papers in this special issue cover innovative applications of com-putational intelligence in the following physiological systems:skin, skeletal, muscular, central nervous, peripheral nervous,systems of special senses (eye), cardiovascular, respiratory, andreproductive.</p><p>A total of 45 papers were submitted for this special issuethat were reviewed by at least three reviewers. Following therecommendations of the guest editors and the Editor-in-Chief,19 papers were accepted for publication. The accepted paperswere organized under the topics: General, Computational Biol-ogy, Biosignal Analysis, and Medical Imaging, with two, three,seven, and seven papers in each topic, respectively. Some ofthese papers (10 in total) have been published earlier by mistake,unfortunately in previous issues of the IEEE TRANSACTIONS ONINFORMATION TECHNOLOGY IN BIOMEDICINE. All the acceptedpapers are briefly summarized in the following section.</p><p>It is generally accepted that, nowadays, health services arefacing a number of complex interacting and multifactorialchallenges [8]. To address these issues from the informationand communication technologies (ICTs) perspective, the WorldHealth Assembly (WHA) adopted an eHealth Strategy for theWorld Health Organization (WHO) [9]. The resolution docu-mented that the use of ICT for health is one of the most rapidlygrowing application areas in health today. Moreover, it was pro-posed that automated or semiautomated systems that support</p><p>Digital Object Identifier 10.1109/TITB.2009.2030025</p><p>decision-making in a clinical environment would be very usefulfor the better support of healthcare services.</p><p>In parallel with the WHO activities, the European Commis-sion (EC) in 2004 adopted the eHealth action plan [10], as wellas subsequent directives [11], that cover a wide spectrum ofeHealth services, ranging from cross-border interoperability ofelectronic health record systems, to electronic prescriptions andhealth cards, to new information systems that are targeting toreduce waiting times and errors to facilitate a more harmoniousand complementary European approach to eHealth. Theseinitiatives, as stated in the Prague Declaration of the EUMember States, stress the need to keep the momentum sothat the potential advantages of gradual deployment of ICTin the health sector are not compromised by barriers of legal,technical, economic, or any other nature. At the same time, itis considered crucial that the benefits of eHealth applicationsand services are further enhanced and properly distributedamong all the relevant stakeholders, patients and healthcareprofessionals, society, and the economy.</p><p>To facilitate the provision of better, and more efficient andeffective eHealth services as documented before, sophisticatedand advanced medical systems based on computational intel-ligence have to be developed. Although significant steps havebeen carried out in this direction in the last two decades, theneed still exists that intelligent medical systems be developed,covering a wider spectrum of services, and most importantly, bethoroughly evaluated before their deployment in clinical prac-tice. The papers in this special issue cover a wide range ofapplications, demonstrating the promising potential of compu-tational intelligence in medical systems.</p><p>II. PAPER SUMMARIES</p><p>A. GeneralThe paper by King et al. [12] (appearing in this issue) deals</p><p>with the development of a wireless sensor glove for the assess-ment of surgical skills. The paper presents a wireless sensorplatform for the capture of laparoscopic hand gesture data anda hidden Markov model (HMM) based analysis framework foroptimal sensor selection and placement. Detailed experimentalvalidation is provided to illustrate how the proposed methodcan be used to assess surgical performance improvement overrepeated training.</p><p>The paper by Sampat et al. [13] proposes an empiricalmethodology for the assessment of three-class classifiers. Theassessment of classifier performance is critical for the fair com-parison of methods, including considering alternative models orparameters during system design. For two-class classification</p><p>1089-7771/$26.00 2009 IEEE</p></li><li><p>668 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO. 5, SEPTEMBER 2009</p><p>problems, receiver operating characteristic analysis provides aclear and concise assessment methodology for reporting perfor-mance and comparing competing systems. While several meth-ods have been proposed for assessing the performance of mul-ticlass problems, none has been widely accepted. Based on 4three-class case studies and the three popular classification tech-niques, the following conclusions were derived: 1) the methodproposed by Scurfield provides the most detailed description ofclassifier performance and insight about the sources of error ina given classification task, and 2) the methods proposed by Heand Nakas also have great practical utility as they provide boththe volume under a three-class receiver operating characteristicsurface (VUS) and an estimate of the variance of the VUS. Theseestimates can be used to statistically compare two classificationalgorithms.</p><p>B. Computational BiologyIn their paper, Tang et al. [14] present a new algorithm, Fuzzy</p><p>Granular Support Vector MachineRecursive Feature Elimi-nation (FGSVM-RFE), that was designed for the selection ofmultiple highly informative gene subsets for cancer classifica-tion and diagnosis. Empirical studies on three public datasetsdemonstrated that FGSVM-RFE outperforms state-of-the-artapproaches. Specifically, the independent testing accuracy forthe prostate cancer dataset was significantly improved, usingonly eight genes, which were annotated by Onto-Express to bebiologically meaningful.</p><p>In their review, Oulas et al. [15] present an overview ofexisting computational methods for identifying micro-RNA(miRNA) genes and assessing their expression levels in can-cer. The discovery of thousands of genes that produce noncod-ing RNA (ncRNA) transcripts in the past few years suggestedthat the molecular biology of cancer is much more complex.MicroRNAs (miRNAs), an important group of ncRNAs, haverecently been associated with tumorigenesis by acting eitheras tumor suppressors or oncogenes. Given that the experimentalprediction of miRNA genes is a slow process, because of the dif-ficulties of cloning ncRNAs, experimental approaches providea number of computational tools trained to recognize featuresof the biogenesis of miRNAs that have significantly aided inthe prediction of new miRNA candidates. Computational ap-proaches provide valuable clues as to which are the dominantfeatures that characterize these regulatory units and which genesare their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular sig-natures of miRNA deregulation in human tumors have emerged.</p><p>The paper Computer-aided diagnosis of capillary thyroidcarcinoma using an artificial immune system by Delibasiset al. [16] (appearing in this issue) proposes the utilization of aCAD system based on the artificial immune systems (AISs) toassist the task of thyroid malignancy diagnosis. The proposed al-gorithm is applied on fine-needle aspiration data from 2016 sub-jects with verified diagnosis, and has exhibited average speci-ficity higher than 99%, 90% sensitivity, and 98.5% accuracy.</p><p>C. Biosignal AnalysisThe paper by Lai et al. [17] (appearing in this issue), Com-</p><p>putational intelligence in gait analysis: A perspective on current</p><p>applications and future challenges, surveys current signal pro-cessing and computational intelligence methodologies followedby gait applications ranging from normal gait studies, disorderdetection to artificial gait simulation. In this paper, recent sys-tems focusing on the existing challenges and issues involved inmaking them successful have been described. Moreover, new re-search in sensor technologies for gait is examined, which couldbe combined with these intelligent systems to develop moreeffective healthcare solutions.</p><p>The paper by Apiletti et al. [18] presents a flexible frameworkthat performs real-time analysis of physiological data to moni-tor the subject in his/her daily activities in the hospital environ-ment. The framework supports the ubiquitous monitoring thatcould also be executed on mobile devices for different diseases.The effectiveness and flexibility of the proposed framework hasbeen demonstrated on 64 patients affected by different criticalillnesses in the intensive care unit.</p><p>The use of SVMs for automated recognition of obstructivesleep apnoea syndrome from electrocardiogram recordings isproposed in the paper by Khandoker et al. [19]. A total of 125sets of nocturnal ECG recordings acquired from normal subjects(obstructive sleep apnea (OSAS) ) and subjects with OSAS(OSAS+), each of approximately 8 h in duration, were ana-lyzed. Features extracted from successive wavelet coefficientlevels after wavelet decomposition of signals due to heart ratevariability (HRV) from RR intervals and ECG-derived respira-tion (EDR) from R waves of QRS amplitudes were used as inputsto the SVMs. Independent test results on 42 subjects showed thatSVMs correctly recognized 24 out of 26 OSAS+ subjects and15 out of 16 OSAS subjects (accuracy = 92.85%). The re-sults demonstrate considerable potential in applying SVMs inan ECG-based screening device that can aid a sleep specialist inthe initial assessment of patients with suspected OSAS.</p><p>The paper by Delgado-Trejos et al. [20] present a method-ology for dimensionality reduction oriented toward the featureinterpretation for ischemia detection. The proposed dimensionreduction scheme consists of three levels: projection, interpreta-tion, and visualization. First, a hybrid algorithm is described thatprojects the multidimensional data to a lower dimension space,gathering the features that contribute similarly in the meaningof the covariance reconstruction in order to find information ofclinical relevance over the initial training space. Next, an al-gorithm of variable selection is provided that further reducesthe dimension, taking into account only the variables that offergreater class separability, and finally, the selected feature set isprojected to a 2-D space in order to verify the performance ofthe suggested dimension reduction algorithm in terms of the dis-crimination capability for ischemia detection. The ECG record-ings used in this study are from the European ST-T databaseand from the Universidad Nacional de Colombia database. Inboth cases, over 99% feature reduction was obtained, and clas-sification precision was over 99% using a five nearest neighborclassifier (5-NN).</p><p>Shin et al. [21] present an automatic detection system for theassessment of cough sounds related to the subjects health con-dition, and their classification as symptomatic or asymptomatic.A hybrid model was proposed that consists of an artificial neuralnetwork (ANN) model and an HMM. The ANN model is based</p></li><li><p>IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO. 5. SEPTEMBER 2009 669</p><p>on the energy cepstral coefficient analysis of cough sounds ob-tained by filter banks based on the human auditory characteris-tics, whereas the output of this model and a filtered envelope ofthe signal were the input to an HMM that processed the tem-poral variation of the sound signal. The proposed system whencompared with the conventional HMM using Mel-frequencycepstral coefficients improved recognition rates on low SNRfrom 5 down to 10 dB.</p><p>The heartbeat time series classification with SVMs was in-vestigated by Kampouraki et al. [22]. Statistical methods andsignal analysis techniques are used to extract features fromthe signals. The SVM classifier is favorably compared to otherneural-network-based classification approaches by performingleave-one-out cross validation. The performance of the SVMwith respect to other state-of-the-art classifiers is also confirmedby the classification of signals presenting very low SNR ratio.Finally, the influence of the number of features to the classifi-cation rate was also investigated for two real datasets. The firstdataset consists of long-term ECG recordings of young and el-derly healthy subjects. The second dataset consists of long-termECG recordings of normal subjects and subjects suffering fromcoronary artery disease.</p><p>The detection of recorded epileptic seizure activity in EEGsegments using timefrequency analysis was investigated in thepaper by Tzallas et al. [23] (appearing in this issue). The use oftimefrequency analysis is justified given that seizure evolutionis typically a dynamic and nonstationary process, and the signalsare composed of multiple frequencies, where visual and conven-tional timefrequency (t-f) based methods have proven to be ofrather limited application. In this paper, the short-time Fouriertransform (STFT) and several t-f distributions (TFDs) were usedto calculate the power spectrum density (PSD) of each segment,compute the signal segment fractional energy on specific t-f win-dows, and then classify the EEG segment (existence of epilepticseizure or not) using ANN. The methods were evaluated usingthree classification problems obtained from a benchmark EEGdataset, and qualitative and quantitative results are presented.</p><p>D. Medical ImagingCalhoun and Adal present a computational approach that</p><p>facilitates the feature-based fusion of brain multitask or mul-timodal medical imaging data [24] (appearing in this issue).A multivariate computational approach is proposed using inde-pendent component analysis that attempts to study how functionand structure are related in the same region of the brain utilizing...</p></li></ul>