Diagnosis of Severe Obstructive Sleep Apnea with Model Designed Using Genetic Algorithm and Ensemble Support Vector Machine

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1 Appl. Math. Inf. Sc. 7 o. 1S pp. 37S-336S (013) Appled Mathematcs & Informaton Scences An Internatonal 01 SP atural Scences Publshng Cor. Dagnoss of Severe Obstructve Sleep Apnea th Model Desgned Usng Genetc Algorthm and Ensemble Support Vector Machne Lang-Wen Hang 1,ǂ, Hsuan-Hung Ln,ǂ, John. Y. Chang 3, Hsang-Lng Wang 4 and Yung-Fu Chen 5,6,7,* 1 Sleep Medcne Center, Department of Internal Medcne, Chna Medcal Unversty Hosptal, 4040 achung, aan Dept. of Management Info. Systems, Central aan Unversty of Scence and echnology, achung, aan 3 Dept. of Computer Scence and Engneerng, atonal Sun Yat-sen Unversty, 8044 Kaohsung, aan 4 Dept. of Beauty Scence, atonal achung Unversty of Scence and echnology, achung, aan 5 Dept. of Healthcare Admnstratons and 6 Insttute of Bomedcal Engneerng and Materal Scence, Central aan Unversty of Scence and echnology, achung, aan 7 Dept. of Health Servces Admnstratons, Chna Medcal Unversty, 4040 achung, aan * Correspondng author: Emal: yfchen@ctust.edu.t. ǂ Authors contrbuted equally to ths ork. Receved ov. 5, 011; Revsed Jan. 8, 01; Accepted Jan. 31, 01 Publshed onlne: 01 Abstract: Obstructve sleep apnea (OSA) s a general sleep dsorder and s a sgnfcant cause of motor vehcle crashes and chronc dseases. he severty of the respratory events s measured by the frequency and duraton of apneas and hypopneas per hour of sleep, namely apnea-hypopnea ndex (AHI), usng polysomnography (PSG). Suspected patents can be classfed as normal (AHI<5), mld (5AHI<15), moderate (15AHI<30), and severe (AHI30). Although PSG s treated as the gold standard for the dagnoss of OSA, ts shortcomng ncludes techncal expertse s requred and tmely access s restrcted. hus, home pulse oxmetry has been proposed as a valuable and effectve tool for screenng patents th OSA. Support vector machne (SVM) s beleved to be more effcent than neural netork and tradtonal statstcal-based classfers. onetheless, t s crtcal to determne sutable parameters to ncrease classfcaton performance. Furthermore, an ensemble of SVM classfers use multple models to obtan better predctve accuracy and are more stable than models consst of a sngle model. Genetc algorthm (GA), on the other hand, s able to fnd optmal soluton thn an acceptable tme, and s faster than dynamc programmng th exhaustve searchng strategy. By takng the advantage of GA n quckly selectng the salent features and adjustng SVM parameters, t as combned th ensemble SVM to desgn a clncal decson support system (CDSS) for the dagnoss of patents th severe OSA, and then folloed by PSG to further dscrmnate normal, mld and moderate patents. he results sho that ensemble SVM classfers demonstrate better dagnosng performance than models consstng of a sngle SVM model and logstc regresson analyss. Addtonally, the oxmetry/psg dagnostc scheme as shon to have hgher costeffectveness n the dagnoss of OSA patents th an average cost rato of 0.66 and an average atng tme rato of 0.40 compared to the tradtonal scheme th PSG examnaton only. Keyords: Obstructve Sleep Apnea, Polysomnography, Oxmetry, Ensemble Classfer, Cost-Effectveness Analyss 1 Introducton Obstructve sleep apnea (OSA) s a general sleep dsorder and s commonly seen n 4% of men and 9% of omen [1]. Among them, up to 93% of omen and 8% of men have not been

2 38 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea dagnosed []. It s a sgnfcant cause of motor vehcle crashes resultng n an ncreased rsk of -7 folds [3] and causes of several chronc dseases, such as metabolc syndrome [4], chronc hyperventlaton syndrome and upper chest breathng pattern dsorders [5], bronchal nflammaton [6], personalty change and ntellectual mparment [7], and erectle dysfuncton [8]. OSA as also reported to be related to cogntve defcts, vglance alteraton, and attentonal declne [9], as ell as obstructve pulmonary dsease, neuromuscular dsease, polomyelts, obesty, cardovascular dsease, and crano-facal anomales [5,10]. he severty of the respratory events s measured by the frequency and duraton of apneas and hypopneas per hour of sleep, namely apneahypopnea ndex (AHI), usng polysomnography (PSG). Suspected patents th AHI smaller than 5 are consdered as normal, hle those th AHI>5 can be further classfed nto mld (AHI15), moderate (15<AHI30), and severe (AHI>30). PSG s treated as the gold standard for the dagnoss of OSA; hoever, t has several lmtatons, such as techncal expertse s requred and tmely access s restrcted [11]. hus, home pulse oxmetry has been proposed as a valuable and effectve tool for screenng patents th OSA; nonetheless, t s effcacy n OSA dagnoss has been debated for several years [1]. Recently, a comprehensve evaluaton of representatve oxyhemoglobn ndces for predctng severty of OSA as reported [13]. It shoed that ODI had a better dagnostc performance than the tme doman and frequency doman ndces n dagnosng severty of OSA th senstvty/specfcty achevng 84.0%/84.3% n AHI>15/h and 87.8%/96.6% n AHI>30/h, respectvely [13]. In ths study, not only the ODI, parameters obtaned from questonnare and anthropometrc ere also adopted for desgnng a clncal decson support system th genetc algorthm (GA) and ensemble support vector machne (SVM) used to predctve and dagnose severty of OSA patents. Clncal decson support system (CDSS) provdes doman knoledge and relevant supportve nformaton to enhance dagnostc performance and to mprove healthcare qualty n clncal settng. hree key ponts ere dentfed and proposed to acheve the goal of enhancng healthcare qualty: best knoledge avalable hen needed, hgh adopton and effectve use, and contnuous mprovement of knoledge and CDS methods [14]. Several CDSSs have been developed for clncal applcatons n the past to decades. Garg et al., [15] reported that 64% of the 97 proposed CDSS applcatons, ncludng 10 dagnostc systems, 1 remnder systems, 37 dsease management systems, and 9 drug-dosng or prescrbng systems, demonstrated mproved outcomes n medcal practtoner performance. Recently, t as shon that CDSSs have been effectvely appled n the dagnoses of loer back pan [16], otologcal dsease [17], cardovascular dsease [18], and cancer usng endoscopc mages [19]; managements and cares of chronc heart falure [0] and chronc kdney falure [1]; management of operatonal rsk n hemodalyss []; and care of patents ho receved mechancal ventlaton [3, 4], predcton of successful ventlaton eanng [5], and outcome predcton of dabetc control of ICU patents [6]. An approprate CDSS can hghly ncrease patent safety, mprove healthcare qualty, and reduce cost. In ths study, a CDSS ntegratng genetc algorthm and ensemble support vector machne as desgned to select salent features and to construct the model for the dagnoss of patents th severe OSA. Wth ts great senstvty, most of the severe OSA patents can be dagnosed th the CDSS. More expensve PSG examnatons ere then used to dagnose the undetermned non-severe patents nto normal, mld, and moderate. he strategy of applyng oxmetry test folloed by PSG examnaton (oxmetry/psg scheme) demonstrates to have better cost-effectveness than the tradtonal PSG scheme. Ensemble Classfers he support vector machne (SVM) s a supervsed learnng method dely used for classfcaton. It s a poerful methodology for solvng problems n nonlnear classfcaton, functon estmaton, and densty estmaton, leadng to many applcatons ncludng mage nterpretaton, data mnng, bometrc authentcaton, botechnologcal nvestgaton, and clncal dagnoss. [7-30]. In general, SVM has better performance hen competed th other exstng methods, such as neural netorks and decson trees [31-33]. he goal of SVM s to separate multple clusters th a set of unque hyperplanes havng greatest margns

3 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea 39 to the boundary, conssted of support vectors, of each cluster. In contrast, each hyperplane hch separates to clusters s not unque for other lnear classfers. Gven a to-class lnearly separable problem, the hyperplane separatng to classes leavng the maxmum margn from both classes s represented as [34]: g( x) x 0 0 (1) n hch ndcates the eghts of the nput vector x and 0 s a bas term of the hyperplane. he tranng data of to classes can be represented as (x, y ) th x R n and y {+1,-1} for =1,,, n hch sample x s an -dmensonal nput vector and y s ts correspondng label ndcatng the class of x. By scalng the orthogonal vector and bas 0 n Eq. (1) to make the values of g(x) at the nearest ponts n class 1 and class equal to 1 and - 1, respectvely, the problem of obtanng the optmal hyperplane becomes a nonlnear quadratc optmzaton problem, as expressed n the follong equaton: Mn, Subject to y ( x 0 ) 1,, 0 1,,..., () he problem can be solved by consderng Lagrangan dualty and stated equvalently by ts Wolfe dual representaton form th the constrants satsfyng the Karush-Kuhn-ucker (KK) condtons,.e. L (, 0, λ) / 0, L(, 0, λ) / 0 0, [ y ( x 0 ) 1] 0, and 0 for 1,..., as ndcated n the follong equaton. Max L(, 0, λ) [ y ( x 1 0) -1] (3a) Subject to y x, 1 (3b) y 1 0, and λ 0 for 1,..., here L(, 0,λ) s a Lagrangan functon and λ=[λ 1,λ, λ] s the vector of Lagrangan multplers correspondng to the constrant n Eq.(). In contrast to Eq. (), the frst to constrants n Eq. (3b) become equalty constrants and make the problem easer to handle. By substtutng the frst to constrants n (3b) nto (3a), the problem s formulated as: 1 Max( y y j j j j ), 1 x x λ, 1 (4) Subject to y 0 th 0, 1,... 1 As soon as the Lagrangan multplers have been obtaned by maxmzng the above equaton, the optmal hyperplane can be obtaned 1 from x shon n Eq. (3b). And then, y classfcaton of a sample s performed based on the sgn of the follong equaton: s ( f x) sgn( x ) sgn( y x x ) (5) here s s the number of support vectors. For a nonlnear classfcaton problem, the optmzaton problem shon n Eq. () s changed to Eq. (6) th a penalty term beng added: Mn ( C ), Subject to, 1 0 y ( ( x ) ) 1-, and 0, 1,,..., 0 (6) here C s a postve penalty parameter, varables ξ are used to eght the cost of msclassfed samples, and (x ) s a functon appled to map the tranng sample x to a hgher dmensonal space. For a vector xr n n the orgnal feature space, t s assumed that there exsts a functon for mappng xr n to (x)r k th k > n. hen, the class of a sample can be determned from the follong equaton: s f x) sgn[ ( x) ] sgn[ y ( x) ( x ) ] (7) ( n hch (x) (x ) s the nner product needed for calculaton, hch s performed by a kernel functon K(x,z)= (x) (z) hch s a symmetrc functon satsfyng the follong condton: K( x, z) g( z) dxdz 0, and g( x) dx (8) Fnally, the optmzaton problem n Eq. (4) s reformulated as: j j y y jk( x 1, 1 x j )), λ (9) Max( Subject to 1 y 0 th 0 λ C 1 Varous kernels ncludng polynomal, radal bass functon, and hyperbolc tangent can be used for mappng the orgnal sample space nto a ne Eucldan space th Mercer s condtons are satsfed for desgnng a nonlnear classfer. he lnear classfer can then be desgned for classfcaton. Among them, radal bass functon, as shon n the follong equaton, s the most dely used functon and ll be appled n ths study for feature mappng.

4 330 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea K ( x, z) exp( x z ) (10) Ensemble Classfers In machne learnng, ensemble methods use multple models to obtan better predctve accuracy and are more stable than models consst of a sngle model [35-37]. Detterch [38] shoed that the outcome of the ensemble classfer generally outperformed the sngle model hen multple eak models ere combned. he reasons causng such an mproved performance mght be: (1) f there are several dfferent optmal hypotheses exsted and the ensemble methods can reduce the rsk of choosng a rong hypothess; () a sngle machne learnng algorthm may end up n local optma, by contrast, the ensemble may obtan a better performance; and (3) the desred functon cannot be represented or acheved by a sngle model. Bron et al. [39] gave a general survey of ensemble learnng and a theoretcal descrpton of hy ensemble learnng may outperform the sngle model. hey dvded the methods nto three categores for achevng dversty: (1) startng pont n hypothess space: vary the startng ponts thn the hypothess space by creatng dfferent ntal settngs; () set of accessble hypotheses: vary the tranng sets that are accessble by the ensemble method employed (e.g., baggng [40], boostng [41] random subspace [4]); and (3) hypothess space traversal. Feature Selecton Feature selecton takes the advantages of reducng the number of features and the sze of storage requrements, decreasng tranng and computatonal tme, facltatng data vsualzaton and understandng, and mprovng predctve performance [43,44]. he algorthms of feature selecton can often be classfed nto 3 approaches,.e. flter, rapper, and embedded methods [43]. he flter method s a preprocessng procedure hch selects a subset of features based on statstc measures ndependent of the desgned classfers. In contrast, the rapper method assesses ndvdual subsets of features n a recursve ay by consderng ther predctve effcency to a gven classfer. It s more computatonally ntensve than the flter method, but s beleved to be able to provde more effcent outcome. he subset th a smallest number of features achevng the hghest predctve accuracy s used for classfer constructon. An alternatve rapper method hch combned genetc algorthm th a classfer as also proposed for feature selecton [45,46]. hs strategy can also be used for adjustng cost value and kernel parameter of an SVM model, together th feature selecton hen desgnng the classfer [47]. On the other hand, embedded method selects features durng the process of model constructon by consderng the cost functon of a model [48], for example the functon shon n Eq. (6) for SVM model. In ths study, a rapper method combnng genetc algorthm and an ensemble of SVM classfers as adopted to construct the CDSS for dagnosng severe OSA patents. Generally, the rapper method assesses ndvdual subsets of features n a recursve ay by consderng ther predctve effcency to a gven classfer. For a vector space th n features, recursve feature elmnaton (RFE) algorthm removes unmportant features based on backard sequental selecton by teratvely deletng one feature at a tme, resultng n a sub-optmal combnaton of r (r<n) features th best predctve performance [43]. For SVM-RFE, t starts th all features by deletng a feature repeatedly untl r features are left, hch leads to a largest margn separatng to classes. Weght magntude hch s nverse proportonal to the margn s generally used as the rankng crteron n determnng mportance of ndvdual features. he elmnated feature p s the one hch mnmzes the varaton of eght: p j j y y jk( x, 0 x j ) (11) In addton to eght or margn, other measures such as generalzaton error [48], gradent of eght [49], and Fscher s rato [50] ere also proposed for feature rankng. In ths study, classfcaton accuracy as used as ftness functon for determnng the optmal soluton n each teraton. 3 Data Recordng Retrospectve data of 699 suspected OSA patents tested usng PSG equpment for overnght attendng recordng at the Sleep Center of a Unversty Hosptal from Jan. 005 to Dec. 006 ere collected. Data of 48 subjects th ages less than 0 or more than 85 years old, as ell as the data acqured from 85 subjects th sleepng tme less than 4 hours ere excluded [4]. Hence only data of 566 patents ere used for nvestgaton. Alce 4 PSG recorder as used to montor and record PSG durng sleep. he recorded physologc varables nclude: (1) EEG for detectng bran electrcal actvty and sleep stages, () EOG and submental EMG for detectng eye and ja muscle movement, (3) tba EMG for montorng leg muscle movement, (4) arflo for detectng breath

5 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea 331 nterrupton, (5) nductance plethysmorgraphy for estmatng respratory effort, (6) ECG for measurng heart rate, and (7) arteral oxygen saturaton for nspectng blood oxygen. Anthropometrc (eght, heght, BMI, ast, neck and hp crcumferences, etc.), demographc (age, gender, etc.), and symptomatc (dabetes, hypertenson, asthma, smokng, alcohol consumpton, observed apnea, nocturnal chokng, mornng headache, ake refresh, day sleepness, etc.) data, ere measured and retreved from the patent records, hle questonnares ncludng Eporth scalng score (ESS), the sleepng dsorders questonnare, the Beck depresson nventory (BDI), and the medcal outcome study 36-tem short form health survey (SF-36) ere flled by the subjects before PSG recordng. 4 CDSS Desgned th GA and Ensemble SVM Regardng SVM performance, t s crtcal to determne sutable combnaton of SVM parameters (log C and log γ). Genetc algorthm can fnd optmal soluton thn an acceptable tme, hch s faster than dynamc programmng usng exhaustve searchng strategy. By takng the advantage of GA n quckly searchng the optmal features and parameters, a nonlnear hyperplane th a maxmum margn can be obtaned by usng SVM to classfy to clusters. Classfcaton of multple clusters can be easly expanded. he freeare LIBSVM [51], a lbrary for SVM, as adopted to be ntegrated th the GA program desgned by our team to acheve best performance. he values of SVM parameters,.e. regularzaton parameter (C) and kernel parameter (γ), are crtcal n optmzng classfcaton performance. radtonally, regular grd search strategy as used to perform model selecton, hch s tme-consumng th regards to computatonal complexty. Addtonally, dfferent from a prevous nvestgaton that GA as used for feature selecton folloed by SVM for classfcaton [5], the combned GA and ensemble SVM method proposed n ths study adjusted SVM parameters and selected features at the same teraton. It can converge to a sub-optmal soluton n a reasonable tme. o ensemble SVM classfers ere desgn: multple-kernel and sngle-kernel. As shon n Fg. 1, an ensemble of 3 SVM classfers embedded th dfferent kernels (polynomal, RBF, and Sgmod) ere desgned. Fgure 1(a) llustrates the consttuents of the chromosome of GA ncludng eghts and parameters of 3 ndvdual SVM models and clncal features. On the other hand, Fg. shos an ensemble of 10 sngle-kernel SVM classfers constructed by 10 tranng subsets obtaned from the tranng set by boostng method. Compared to Fg. 1(a), as ndcated n Fg. (a), only a set of SVM parameters s needed for ths model. Fgure (b) depcts the model hch combnes GA and an ensemble SVM classfer for feature selecton and CDSS constructon. he SVM kernel adopted s radal bass functon (RBF) and the ftness functon s defned as the accuracy of classfcaton. In order to prevent over-tranng th cross valdaton, data of 565 samples ere randomly dvded nto 3 sets,.e. tranng (=188), valdatng (M=188), and testng (P=189) sets. A total of 10 tranng datasets, each contanng 188 samples, ere obtaned usng the boostng method by randomly samplng the tranng dataset to create an ensemble of 10 SVM classfers. he expermental procedure s summarzed as follos: ranng phase (1) Generate an ntal populaton of chromosomes consstng of SVM parameters and features. () Randomly dvde the data nto tranng, valdatng, and testng sets. (3) Obtan 10 sub-tranng datasets by randomly samplng from the tranng set usng boostng method. (4) Construct an ensemble of 10 SVM classfers based on the 10 sub-tranng datasets by usng the valdatng set for valdaton. A valdatng sample s classfed as sever OSA f the mean probablty of the ensemble SVM classfers s greater than 0.5. (5) Generate a ne populaton of SVM parameters and features and repeat Step 4 to get optmal SVM parameters and features. estng phase (1) Execute Steps and 3 of the tranng phase to generate testng set and 10 subtranng sets. () Apply the SVM parameters and features obtans from the tranng phase to construct an ensemble of 10 SVM classfers based on the 10 sub-tranng sets, and then use the testng set for testng. A testng sample s classfed as severe OSA f the mean probablty of the ensemble SVM classfers s greater than 0.5. (3) Repeat Steps 1 and for 10 teratons to obtan mean accuracy, senstvty, and specfcty and ther standard devatons. W 1,W, & W 3 C 1,γ 1; C,γ ; C 3γ 3 X 11...X 1n; X 1 X n; X 31 X 3n Weght of each classfer SVM parameters (a) If the feature s selected X j=1, otherse X j=0

6 33 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea C γ X 1 X... X n SVM Paramet Features (X =1 f selected, otherse X =0 (a) (b) Fgure 1: (a) Chromosome and (b) flochart of ntegrated GA and SVM algorthm for desgnng multple-kernel ensemble classfers. 5 Expermental Results In one experment, the subjects ere dvded nto to groups th AHI=15 used as the threshold to dscrmnate severty of OSA by classfyng the subjects nto normal and mld (AHI<15) as ell as moderate and severe (AHI>=15) groups; hle n the other experment, the subjects ere dvded nto non-severe (AHI<30) and severe (AHI>=30) groups usng AHI=30 as the threshold. ables 1 and sho the results of detectng severe OSA patents th thresholds based on AHI=15 and AHI=30, respectvely, usng an ensemble of 10 SVM classfers th a sngle RBF kernel. he accuracy, senstvty, and specfcty are 89.6±1.43, 89.34±1.68, and 90.15±.07, respectvely, for AHI>15, as ell as 90.37±0.71, 90.11±1.78, and 90.58±1.78, respectvely, for AHI>30. As shon n ables 3, the accuracy, senstvty, and specfcty for an ensemble of 3 SVM classfers are 88.58±1.40, 87.60±.36, and 90.40±3.34, respectvely, for AHI>15, as ell as 89.±1.4, 87.93±1.95, and 91.63±4.0, respectvely, for AHI>30. (b) Fgure : (a) Chromosome and (b) flochart of ntegrated GA and SVM algorthm for desgnng sngle-kernel ensemble classfers. 6 Dscussons and Conclusons he average senstvtes for the sngle-kernel ensemble SVM classfer acheve 89.34% and 90.11%, respectvely for AHI=15 and AHI=30 as the thresholds, hch s hgher than the multplekernel ensemble SVM classfer (87.60% and 87.93%, respectvely) and the classfers constructed th a sngle kernel (AHI=15/AHI=30: 85.65%/86.95%, 86.30%/87.39%, and 86.5%/86.41% for polynomal, RBF, and sgmod kernels, respectvely). Recently, a comprehensve evaluaton of representatve oxyhemoglobn ndces for predctng severty of OSA as nvestgated by Ln et al. [13]. he results shoed that ODI had a better dagnostc performance than the tme doman and frequency doman ndces n dagnosng severty of

7 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea 333 OSA th senstvty/specfcty achevng 84.0%/84.3% n AHI>15/h and 87.8%/96.6% n AHI>30/h, respectvely. he proposed ensemble sngle-kernel SVM classfer th 3 selected features (ODI, ESS, or BMI) acheves a better dagnosng performance th senstvty/specfcty of 89.34%/90.15% n AHI>15/h and 90.11%/90.58% n AHI>30/h. On the other hand, the senstvty/specfcty s 87.60%/90.40% n AHI>15/h and smlar dagnosng performance of 87.93%/91.63% n AHI>30/h for ensemble multple-kernel SVM classfer. he classfcaton performances of both ensemble SVM classfers are better than the non-ensemble SVM classfers and the classfcaton reported n [13]. able 1: Detectng severe patents th AHI>15 Iteraton Accuracy Senstvty Specfcty Mean SD able : Detectng severe patents th AHI>30 Iteraton Accuracy Senstvty Specfcty Mean SD Cost-effectveness as conducted based on to schemes: oxmetry/psg and PSG. For the former scheme, the physcans are suggested to order a take-home oxmetry examnaton to dagnose severe OSA patents folloed by an addtonal PSG examnaton for detectng non-severe patents. In contrast, all the suspected OSA patents take PSG examnatons to detect OSA severty for the latter scheme. Currently, Bureau of atonal Health Insurance (BHI) of aan pays $380 and $4500 for take-home oxmetry and PSG examnatons, respectvely. Although, except oxmetry, the non-severe patents ll receve an addtonal PSG examnaton, hch mght cost more, by consderng the cost saved by severe OSA patents ho take only cheap oxmetry examnaton, the total dagnosng cost s expected to be reduced, as detaled belo. he average cost (AC) per case of oxmetry/psg scheme n OSA dagnoss can be calculated accordng to the follong equaton: AC P 100% P [1 S /(1 F / )] (1) ox psg here P ox ($380) and P psg ($4500) ndcates the costs of conductng an oxmetry test and a PSG examnaton, respectvely; S s the senstvty; and and F represent the percentages of RUE (severe) and FALSE (non-severe) cases, respectvely. he frst term at the rght sde of Eq. (1) ndcates that all the suspected OSA patents have to take oxmetry tests, hle only those ho are not detected as rue-postve usng oxmetry need to take an addtonal PSG examnaton. In our dataset, there are 309 and 56 cases of severe and nonsevere patents. Hence, the average cost per case pad by BHI for oxmetry/psg scheme s: [ / (309 / 56)] = $150, compared to $4500 per case for PSG scheme. It can save the BHI as much as $980 per case. able 3: Comparsons of an ensemble multple-kernerl SVM classfer and 3 ndvdual SVM classfers th sngle kernel AHI>= 15/h AHI>= 30/h OSA Severty SVM Classfer Ensemble Polynomal RBF Sgmod Accuracy(%) 88.58± ± ± ±1.54 Senstvty(%) 87.60± ± ± ±.13 Specfcty(%) 90.40± ± ± ±3.70 Accuracy(%) 89.± ± ± ±1.98 Senstvty(%) 87.93± ± ± ±3.81 Specfcty(%) 91.63± ± ± ±7.54 Regardng the tme needed to confrm a dagnoss s 1 and 7 days, for oxmetry and PSG, respectvely, under currently outpatent settng n a unversty hosptal stuated n mddle aan. Hence, the average tme of affrmatve dagnoss can be calculated as: [ /(309/56)]=.77 days. In contrast, the average tme of the PSG scheme for affrmatve dagnoss s as long as 7 days. he average cost rato of 0.66 ndcates that the oxmetry/psg scheme s more cost effectve than PSG scheme n the dagnoss of OSA patents. Furthermore, the average atng tme rato of 0.40 shos that the oxmetry/psg scheme s more tme effcent than the PSG scheme. otce that the hgher the

8 334 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea senstvty of detectng severe OSA patents usng oxmetry, the less the average cost and the affrmatve dagnoss tme are needed. It ndcates that desgn of a hgh-performance CDSS s mportant. In concluson, an ensemble of sngle-kernel SVM classfer as demonstrated to be effectve n the dagnoss of severe OSA patents usng to features, ODI and ether ESS or BMI. It can be used to dagnose severe OSA patents by usng cheap take-home oxmetry accompaned th ESS questonnare or BMI at the frst screenng stage, hch s then folloed by PSG examnaton to confrm severty of other suspected OSA patents. Furthermore, after cost-effectveness analyss, t as shon that the oxmetry/psg scheme s more effectve n reducng the healthcare cost and atng tme than the tradtonal scheme th PSG examnaton only. Acknoledgements hs ork as supported n part by atonal Scence Councl of aan (Grant SC H MY) and Central aan Unversty of Scence and echnology (Grants CU100-IS-0, CU100-IS-03, and PH1009). References [1]. Young, M. Palta and J. Dempsey, he occurrence of SDB among mddle-aged adults,. Engl. J. Med. (1993), 38, []. Young, L. Even and L. Fnn, Estmaton of the clncally dagnosed proporton of sleep apnea syndrome n mddle aged men and omen, Sleep. (1997), 0, [3]. Hartenbaum,. Collop, I. M. Rosen, B. Phllps and C. F. P. George, Sleep Apnea and Commercal Motor Vehcle Operators: Statement from the jont task force of the Amercan College of Chest Physcans, Amercan College of Occupatonal and Envronmental Medcne, and the atonal Sleep Foundaton, JOEM. (006), 48, S4-S37. [4] J. C. M. Lam, B. Lam, C. L. Lam, D. Fong, J. K. L. Wang, H. F. se, K. S. L. Lam and M. S. M. Ip, Obstructve sleep apnea and the metabolc syndrome n communty-based Chnese adults n Hong Kong, Respratory Medcne. (006) 100, [5] J. C. Coffee, Is chronc hyperventlaton syndrome a rsk factor for sleep apnea? Part 1, Journal of Bodyork and Movement herapes. (006), 10, [6] G. Devouassoux, P. Levy, E. Rossn, I. Pn, M. For- Gozlan, M. Henry, D. Segneurn and J. L. Pepn, Sleep apnea s assocated th bronchal mflammaton and contnuous postve aray pressure-nduced aray hyperresponsveness, J Allergy Cln Immunol. (007), 119, [7] J. Montplasr, M. A. Bedard, F. Rcher and I. Roulea, eurobehavoral manfestatons n obstructve sleep apnea syndrome before and after treatment th contnuous postve aray pressure, Sleep. (199), 15, [8] P. E. eloken, E. B. Smth, C. Lodosky,. Freedom and J. P. Mulhall, Defnng assocaton beteen sleep apnea syndrome and erectle dysfuncton, Urology. (006), 67, [9]. Gosseln, A. Matheu, S. Mazza, D. Pett, J. Malo and J. Montplasr, Attentonal defcts n patents th obstructve sleep apnea syndrome: An event-related potental study, Clncal europhysology. (006), 117, [10] R. aman and D. Gozal, Obesty and obstructve sleep apnea n chldren, Pedatr Respr Reve. (006), 7, [11] W. W. Flemons, M. R. Lttner, J. A. Roley, P. Gay, W. McDoell Anderson, D. W., Hudgel, R. D. McEvoy and D. I. Loube, Home dagnoss of sleep apnea: A systematc reve of the lterature, Chest. (003), 14, [1]. etzer, A. H. Elasson, C. etzer and D.A. Krsco, Overnght Pulse Oxmetry for Sleep-Dsordered Breathng n Adults-A Reve, Chest. (001), 10, [13] C. L. Ln, C. Yeh, C. W. Yen, W. H. Hsu and L. W. Hang, Comparson of the ndces of oxyhemoglobn saturaton by pulse oxmerty n obstructve sleep apnea hypopnea syndrome, Chest. (009), 135, [14] J. A. Osheroff, J. M. ech, B. Mddleton, E. B. Steen, A. Wrght and D. E. Detmer, A Roadmap for atonal Acton on Clncal Decson Support, JAMIA. (007), 14, [15] A. X. Garg,. K. J. Adhkar and H. McDonald, Effects of Computerzed Clncal Decson Support Systems on Practtoner Performance and Patent Outcomes- A Systematc Reve, JAMA. (005), 93(10), [16] L. Ln, P. J. H. Hu, O. R. L. Sheng, A decson support system for loer back pan dagnoss: Uncertanty management and clncal evaluatons, Decson Support Systems. (006), 4, [17] L.S. Goggn, R. H. Ekelboom and M.D. Atlas, Clncal decson support systems and computer-aded dagnoss n otology, Otolaryngology-Head and eck Surgery. (007), 136, S1-S6.

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10 336 Lang-Wen Hang et al.: Dagnoss of Severe Obstructve Sleep Apnea and feature selecton, Journal of Machne Learnng Research. (003), 3, [44] G. P. Zhang, eural netorks for classfcaton: a survey, IEEE rans SMC- Part C. (000), 30(4), [45] L. Boroczky, L. Zhao and K. P. Lee, Feature subset selecton for mprovng the performance of false postve reducton n lung nodule CAD, IEEE ransactons on Informaton echnology n Bomedcne. (006), 10(3), [46] S. Ososk, R. Sroć,. Markecz and K. Sek, Applcaton of support vector machne and genetc algorthm for mproved blood cell recognton, IEEE ransactons on Instrumentaton and Measurement. (009), 58(7), [47] Y. Baz and F. Melgan, oard an optmal SVM classfcaton system for hyperspectral remote sensng mages, IEEE ransactons on Geoscence and Remote Sensng. (006), 44(11), [48] S. Maldonado and R. Weber, A rapper method for feature selecton usng support vector machnes, Informaton Scences. (009), 179, [49] A. Rakotomamonjy, Varable selecton usng SVMbased crtera, Journal of Machne Learnng Research. (003), 3, [50] M. E. Blazadonaks and M. Zervaks, Wrapper flterng crtera va lnear neuron and kernel approaches, Computers n Bology and Medcne. (008), 38, [51] C. C. Chang, C. J. Ln, LIBSVM: a lbrary for support vector machnes, (001), /~cjln/lbsvm [5] S. Ososk, R. Sroc,. Markecz and K. Sek, Applcaton of support vector machne and genetc algorthm for mproved blood cell recognton, IEEE rans Instrument & Measurement. (009), 58(7), Lang-Wen Hang receved the M.D. and M.S. degrees from Chna Medcal Unversty (CMU), achung, aan n 1991 and 1998, respectvely. He s no a Ph.D. canddate of Department of Healthcare Admnstraton, Asa Unversty, achung, aan. Snce 1991, he has been th CMU, here he s currently Chef and Assstant Professor of Department of Respratory Care and Drector of Sleep Medcne Center of CMU Hosptal. He as elected as the Presdent of aan Socety of Sleep Medcne n 007. Hs research nterests nclude sleep medcne and bologcal nformatcs on small aray dseases. Hsuan-Hung Ln receved the PhD degree n Appled Mathematcs form atonal Chung Hsng Unversty, achung, aan, n 010. He s currently an assocate professor n Management Informaton System at Central aan Unversty of Scence and echnology, achung, aan. Hs research nterests nclude bomedcal nformaton and bonformatcs. John Y. Chang receved the B.S. degree n Electrcal Engneerng from atonal aan Unversty, ape, aan, n 1985, the M.S. and the Ph.D. degree n Electrcal Engneerng from the orthestern Unversty, Evanston, IL, USA, n 1987 and 1990, respectvely. He s currently an Assocate Professor of the Department of Computer Scence and Engneerng of the atonal Sun Yat-sen Unversty, Kaohsung, aan. Hs areas of research nclude pattern recognton, mage processng and content-based mage retreval. Hsang-Lng Wang receved the B.S. degree n nursng from Chna Medcal Unversty and M.S. degree n health management from Baker College, n 1986 and 001, respectvely. Snce 1987, she has been th atonal achung Unversty of Scence and echnology, achung, aan, here she s currently Lecturer of Department of Beauty Scence. Her research nterests nclude sleep medcne, body- eght control, and hepatts. Yung-Fu Chen receved the Dploma from atonal ape Insttute of echnology and M.S. degree from Unversty of Mssour- Rolla n 1983 and 1990, respectvely, both n electrcal engneerng, and the Ph.D. degree n bomedcal engneerng from atonal Cheng Kung Unversty, anan, aan, n 00. Snce 199, he has been th Central aan Unversty of Scence and echnology, achung, aan, here he s currently Professor of Department of Healthcare Admnstraton. Hs research nterests nclude medcal nformatcs, nnovatve applcatons of RFID n busness and medcne, and dgtal preservaton.