FEATURE EXTRACTION AND SELECTION FOR AUTOMATIC SLEEP STAGING USING EEG

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1 FEATURE EXTRACTION AND SELECTION FOR AUTOMATIC SLEEP STAGING USING EEG Hugo Smões, Gabrel Pres Insttute o Systems and Robotcs, Unversty o Combra, Combra,Portugal hugo@sr.uc.pt, gpres@sr.uc.pt Urbano Nunes, Vtor Slva Department o Electrcal Engneerng,Unversty o Combra Polo II, Combra, Portugal Keywords: Abstract: Feature extracton, eature selecton, EEG sleep stagng, Bayesan classer. Sleep dsorders aect a great percentage o the populaton. The dagnostc o these dsorders s usually made by a polysomnography, requrng patent s hosptalzaton. Low cost ambulatory dagnostc devces can n certan cases be used, especally when there s no need o a ull or rgorous sleep stagng. In ths paper, several methods to extract eatures rom 6 EEG channels are descrbed n order to evaluate ther perormance. The eatures are selected usng the R-square Pearson correlaton coecent (Guyon and Elssee, 003), provdng ths way a Bayesan classer wth the most dscrmnatve eatures. The results demonstrate the eectveness o the methods to dscrmnate several sleep stages, and ranks the several eature extracton methods. The best dscrmnaton was acheved or relatve spectral power, slow wave ndex, harmonc parameters and Hjorth parameters. INTRODUCTION About a thrd o the populaton suers rom sleep dsorders, ncludng the obstructve sleep apnea syndrome (Doroshenkov et al, 007). The dagnoss o such dseases s perormed by a polysomnography (PSG) whch requres the patent's hosptalzaton wth costs and dscomort or the patent. Ambulatory dagnostc devces may have an mportant role n order to mtgate these actors. The PSG conssts on the acquston o varous electrcal bosgnals ncludng electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG). The sgnals are segmented nto epochs o 30 seconds and assgned to a sleep stage by an expert (Iber et al, 007). Ths s a tedous and tme consumng task. Automatc sleep stages classcaton (ASSC) s thereore an attractve soluton. However, the general opnon s that most o the experts do not rely on ASSC sotware, because they usually present a low perormance (.e. present a hgh level o dsagreement). One o the man reasons s due to the hgh varablty between subjects whch makes t dcult to obtan robust models or classcaton. The expert uses sometmes heurstcs dcult to mplement n the algorthms and combnes a macro and mcro perspectve o the overall epochs. It should be hghlghted that there s also some level o dsagreement between experts. Ths work descrbes part o an apnea detecton system to be used n ambulatory stuatons by patents at home. It does not ntend to substtute the PSG, but only to determne prmarly the patent s sleepng at the occurrence o the apnea epsode, and secondly to determne n whch sleepng stage t dd occur. The stage classcaton reles only on EEG sgnals. Ths paper nvestgates several eature extracton methods to compare ther perormance amng to acheve mproved results n the ollowng sleep detecton stages: wake (W) vs. sleep (S), NREM (NR) sleep vs. REM (R) sleep, NREM N vs. NREM N + NREM N3, NREM N + NREM N vs. NREM N3, NREM N vs. NREM N, NREM N vs. NREM N3 and NREM N vs. REM sleep (Iber et al, 007). Moreover, a eature selecton method based on the squared Pearson correlaton coecent (Guyon and Elssee, 003), henceorth desgnated R-square crtera, s appled wth the purpose o ndng a reduced set o dscrmnatve eatures. These eatures are used to provde addtonal normaton to the expert, and also to automatcally classy each sleep stage wth

2 some degree o certanty. The classcaton s perormed by a Bayesan classer usng -class detecton. Scorng sleep s done accordng to rules o the Amercan Academy o Sleep Medcne (AASM) Manual or Scorng Sleep (Iber et al, 007), an actualzaton o the rules o Rechtschaen and Kales (Rechtschaen and Kales, 968). Accordng to AASM Manual, sleep s dvded nto ve stages: wake, NREM (Non Rapd Eye Movement) sleep (N, N and N3) and REM (Rapd Eye Movement) sleep. Consderng only EEG sgnals, the wake stage s characterzed by a low ampltude alpha actvty (8-3 Hz); N by a low ampltude theta actvty (3-7 Hz); n N the predomnant requences are n the Hz range and there s the arsng o sleep spndles and K- complexes; N3 presents at least 0% o the epochs wth delta actvty (< Hz) wth ampltude greater than 75 µv; REM s characterzed by requences mostly between and 6 Hz wth low ampltude. Sleep stagng based only on EEG presents some dcultes because derent stages such as wake, REM and NREM N present smlar patterns. The ASSC has been addressed by many research groups. In (Tang et al, 007), Hlbert-Hang transorm and wavelet transorm were appled to extract harmonc parameters rom EEG sgnals, (Hese et al, 00) mplemented a sem-automatc method based on k- means clusterng algorthm. (Ebrahm et al, 008) used neuronal networks and wavelet packet coecents to dscrmnate between derent sleep stages. Doroshenkov et al. (007) have developed a classcaton algorthm based on Hdden Markov Models usng only EEG sgnals. (Zoubek et al, 007) have used eature selecton algorthms to nd the relevant eatures extracted rom PSG sgnals. Schwabold et al (003) have mplemented a neurouzzy algorthm to model the rules o Rechtschaen and Kales. Although some studes show good perormance, they are very lmted to specc groups o patents and t has not been possble yet to create generalzed models that provde results accepted by the experts. Moreover, t remans dcult to dscrmnate between certan sleep stages usng only EEG sgnals. DATABASE Data rom all-nght PSG records were provded by the Laboratory o Sleep rom Centro Hosptalar de Combra. The PSG was recorded by the model Somnostar Pro rom Vasys at a samplng requency o 00 Hz. The database comprses seven patents (ve males and two emales) wth ages between 7 and 64 years old (mean = 50 years; standard devaton =.88 years). Only sx EEG channels were used: F3-A, C3-A, O-A, F4-A, C4-A and O-A. All recordngs were segmented nto epochs o 30 seconds and labelled by an expert. The dataset was ntally composed by 6558 epochs. In order to avod the over-ttng n the learnng and testng o algorthms, the number o sleep epochs n the database was reduced to 3000, balancng the dstrbuton o epochs o derent sleep stages accordng to a normal nght sleep dstrbuton as presented n Table. Snce the sleep stages N and N are the ones wth the hghest and lowest occurrence durng a normal nght sleep, respectvely, they were set as the stages wth major and mnor number o epochs n the dataset, respectvely, and the other sleep stages have a number o epochs between these lmts. Sleep Stages Full dataset Reduced dataset Table : Full and reduced datasets. NREM Wake N N N3 REM AUTOMATIC SLEEP SCORING The classcaton methodology s llustrated n the block dagram presented n gure. The EEG sgnals are ltered and segmented. Derent types o eatures extracton are used. These eatures are then selected usng the correlaton crtera R-square measure n order to provde the classcaton stage, EEG Sgnal Patent Database Patent Testng Pre-Processng Notch lter Butterworth lter Segmentaton Feature Extracton RSP SWI Harmonc Parameters Parameters o Hjorth Entropy Skweness & Kurtoss Feature Selecton R-square Classcaton Bayesan Classer LDA Decson Tree Fgure : Classcaton methodology.

3 a Bayesan-based classer, wth the most dscrmnatve ones. The tranng process uses data rom a pool o patents and some data rom the patent beng montored, namely, the wake recorded epochs beore the patent all asleep. Ths way, the wake model can be mproved. Moreover, the wake epochs can be used or calbraton o sleep stages. The perormance analyss o the o eature extracton algorthms was done through ten-old cross valdaton. The patents database s parttoned nto ten groups wth the same number o epochs rom each sleep stage. Nne o them are used to perorm the models o classcaton and one or testng. Ths process s repeated 0 tmes usng a derent group or testng. 4 FEATURE EXTRACTION AND SELECTION In ASSC, the EEG s tradtonally analyzed n requency doman because, accordng wth AASM Manual, each sleep stage s essentally dstngushed by some spectral propertes. However, temporal analyss provdes also useul normaton. For each EEG channel, 34 eatures were extracted usng several methods as descrbed n the ollowng. Spectral analyss provdes some o the most mportant eatures. For each sleep epoch, an autoregressve method solved by the Yule-Walker algorthm was appled to estmate the power spectral densty (PSD) (Ylmaz et al, 007). The spectrum s dvded nto ten requency sub-bands as represented n Table. For each sub-band, the relatve spectral power (RSP) was computed. Ths parameter s gven by the rato between the subband spectral power (BSP) and the total spectral power,.e., the sum o all 0 BSP sub-bands. Ths normalzaton s mportant to ncrease classcaton robustness durng the recordng sesson. Table : Spectral sub-bands used n RSP computaton. Some spectral bands can be hghlghted over slow wave bands by means o slow wave ndex (SWI) dened by the ollowng ratos: DSI = BSP /( BSP + BSP ) () where DSI, TSI and ASI stand or delta-slow-wave ndex, theta-slow-wave ndex and alpha-slow-wave ndex, respectvely (Agarwal et al, 00). Harmonc parameters allow the analyss o a specc band n the EEG spectrum. They nclude three parameters: center requency ( c ), bandwdth ( σ ) and spectral value at center requency (S c ), dened as ollows (Tang et al, 007): H H c = Pxx ( ) Pxx ( ) (4) L Delta Theta Alpha TSI = BSP /( BSP + BSP ) () Theta Delta Alpha ASI = BSP /( BSP + BSP ), (3) Alpha Delta Theta L H H = ( ) ( ) ( ) σ c Pxx Pxx (5) L L xx ( ) S c = P, (6) where, P xx () denotes the PSD, whch s calculated or the requency bands { L, H } (see Table ). The Hjorth parameters provde dynamc temporal normaton o the EEG sgnal. Consderng the epoch x, the Hjorth parameters are computed rom the varance o x, var(x), and the rst and second dervatves x, x accordng to (Ansar-Asl et al, 007) c Actvty = var(x) (7) Moblty = var( x') var( x) (8) Bands Delta Theta Alpha Sgma Beta Sub-bands Bandwdth { L, H} (Hz) Delta {0.5,.0} Delta {.0,4.0} Theta {4.0,6.0} Theta {6.0,8.0} Alpha {8.0,0.0} Alpha {0.0,.0} Sgma {.0,4.0} Sgma {4.0,6.0} Beta {6.0,5.0} Beta {5.0,35.0} = var( x'') var( x) var( x'). (9) Complexty The entropy gves a measure o sgnal dsorder and can provde relevant normaton n the detecton o some sleep dsturbs. It s computed rom hstogram o the EEG samples o each sleep epoch, accordng wth (Zoubek et al, 007) = N n n Entropy ln = n n, (0)

4 where n s the number o samples wthn the sleep epoch, N s the number o bns used n computaton o hstogram and n s the number o samples wthn the th bn. The skewness s a measure o symmetry. The kurtoss s a measure o wether the data are peaked or lat relatve to a normal dstrbuton. Denng the kth order moment m k as (Zoubek et al, 007) = ( ( ) ) n mk = y y n k, () where n s the number o samples o an epoch and y s the mean o these samples, the skewness and kurtoss are gven by and skewness = m () 3 m m kurtoss = m. (3) 4 m m Features are usually selected by wrapper or lter methods usng sequental approaches. The results rom wrappers methods are dependent o the choce o the classcaton algorthm. Our opton ell on an R-square lter approach whch s ndependent o the classer, based on the Pearson correlaton coecent dened as (Guyon and Elssee, 003): ( X, Y ) ( X ) var( Y ) cov R =, (4) var where X and Y represent two random dstrbutons o samples, and cov and var desgnates covarance and varance, respectvely. Consderng x and y as the sample values o eature labelled wth class and class, respectvely, the value R() or the eature s gven by: R( ) = m k = ( x x )( y y ), k, k m m ( x, k x ) ( y, k y ) k = k =, (5) where x and y represent the mean value o x and y o the m samples. The R-square, computed as R(), provde a level o dscrmnaton between the two classes. Hgh values o R-square ndcate large nter-class separaton and small wthn-class varance. The R-square provdes a eature dscrmnaton rankng. 5 BAYESIAN CLASSIFICATION The condtonal densty uncton o the class s modelled as a multvarate dstrbuton under gaussan assumpton where, ( / ) T ( Y µ, Σ ) = K exp ( Y µ ) Σ ( Y ) P µ n ( ) Σ ) (6) K = π, (7) Y s the eature vector resultng rom concatenaton o the extracted eatures, µ and Σ are respectvely, the mean and covarance matrces computed or each class w rom the tranng data. The Bayes decson uncton s wrtten as: ( ) = arg max{{ p( Y w ) P( w )}, { p( Y w ) P( w )}} wˆ Y, (8) where P(w ) s the th class pror probablty and an adjustment parameter to control the rate o alse postves and alse negatves (Hejden et al, 004). 6 RESULTS AND DISCUSSION The eature extracton process provdes a vector o 04 eatures, 34 eatures per each EEG channel: 0 RSP, 3 SWI, 5 harmonc parameters, 3 Hjorth Parameters, entropy eature, skewness and kurtoss. Next, the eatures are sorted n a decreasng order o level o dscrmnaton by applyng the R-squared based selecton approach. Fgure shows the percentage o dsagreement or wake/sleep detecton between our ASSC system and expert classcaton (.e. the percentage o epochs or whch the automatc classcaton ders rom manual classcaton made by the expert), as uncton o the number o eatures,. e., the n-most dscrmnatve eatures wth n =,,5. The dsagreement values are obtaned rom a ten-old cross valdaton. The lowest dsagreement value was reached usng the rst 9 ranked eatures. Table 3 presents the results or each bnary classer, usng,, 3, 9 most dscrmnatve eatures and all 04 eatures. Selectng the relevant eatures reduces the number o eatures used n the ASSC leadng to an ncreased robustness o the classers. The eature selecton also enables to denty the type o eatures and channels that lead to hgher dscrmnaton results or each -class dscrmnator

5 Percentage o Dsagreement Wake vs. Sleep Number o Features Fgure : Percentage o dsagreement vs. number o eatures used n wake vs. sleep classcaton. eatures, skewness or kurtoss. These parameters are related to the sgnal shape. However, snce the EEG sgnal patterns are very random, t s dcult to obtan useul normaton rom these parameters. Instead, the set o most dscrmnatory eatures between sleep stages was composed manly by RSP and Harmonc Parameters. Ths result emphaszes the act that the spectral analyss has more dscrmnatve normaton than temporal sgnal analyss as already concluded n (Hese et al, 00; Tang et al, 007). Table 4: Number o eature type and channels wthn the 0 most dscrmnatve eatures. Table 3: Percentage o dsagreement obtaned usng,, 3 and the 9 most dscrmnatve eatures and all 04 eatures W vs. S,4 0,7 8,8 7,0 6,7 R vs NR,5,4 9,5 5,6 30,8 N vs. N/N3 5, 5,7 5,7 0,6 7,5 N/N vs. N3 5,7 4,7 4,6 5,5 30,3 N vs. N,9,6 8,5 5,6 63,9 N vs. N3 9,0 8, 6,7 7,7 39,8 N vs. R 5,5 4,7 4,4 5,0 64,7 Mean 8,7 8,3 6,9 5,3 45,5 (Table 4). As t can be seen, the eature entropy (Ent), Skewness (Skw) and kurtoss (Krt) never appear n the 0 most dscrmnatve eatures. On the other hand, the most requents are the RSP and harmonc parameters. Analyzng the orgn o the 0 most dscrmnatve eatures or each case, the parameters o Hjorth (PHj) are most evdent n N/N vs. N3 and N vs. N3, but they have no weght n R vs. NR and N vs. R. The harmonc parameters are more requent n W vs. S, N vs. N/N3 and N vs. N, but are not relevant n R vs. NR, N vs. N/N3, N vs. N3 and N vs. R. For the RSP and SWI, they have a smlar number o eatures n all dscrmnatons, except or N vs. R, where the RSP has several eatures wth good dscrmnaton, and or N vs. N, where SWI does not assume any mportance. Analyzng the EEG channels, t can be seen that OA (O) and OA (O) are the most relevant n dscrmnaton wake vs. sleep; F3A (F3) and F4A (F4) n REM vs. NREM; and C3A (C3) and C4A (C4) n N vs. N3. In the remanng dscrmnatons, they all have a relatvely unorm dstrbuton, except n N vs. R, n whch the channels OA and OA do not have any type o contrbuton. Fgure 3 shows the type o eatures and channels that lead to hgher dscrmnaton results, takng all dscrmnators together. Summarzng, the best ranked dscrmnatve eatures never nclude entropy Number o tmes t appears Features Channels W vs. S R vs. NR N vs. N/N3 N/N vs. N3 N vs. N N vs. N3 N vs. R 0 RSP SWI HP PHj Ent Sk Krt F3 C3 O F4 C4 O Features Channels Total RSP SWI HP PHj Ent Skw Krt F C O F C O Fgure 3: Number o tmes that each group o eatures and each channel appears n the 0 most dscrmnatve eatures. On the other hand, all the 6-sx EEG channels provde useul eatures or sleep stagng dscrmnaton. Analyzng the results or each o the bnary classers, there s greater dsagreement n the case o N vs. R sleep. Ths stuaton relates to the act that, n terms o EEG, the patterns presented n these two stages are very smlar. Fnally, a

6 decson tree was mplemented based on -class detecton, as represented n Fgure 4. At each step, a new level was ntroduced rom a wake/sleep to all stages classcaton. The results were compared wth and wthout eature selecton (Table 5). The mprovements rom eature selecton are evdent. The results obtaned wth our ASSC system are comparable to the ones obtaned n other methods based on EEG only descrbed n lterature (zoubek et al, 007; Doroshenkov et al, 007). Epoch Level Wake Sleep Level REM NREM Level 3 N N/N3 Level 4 Fgure 4: Decson tree based on -class detecton. Table 5: Dsagreement obtaned wth usng 9 most dscrmnatve eatures and all 04 n, 3, 4 and 5 sleep stages classcaton. Classcaton Dasagreement (%) All Features 9 Class Class Class 83 5 Class CONCLUSIONS In ths paper, the use o several eature extracton methods was nvestgated n the context o EEGbased sleep stagng. The rst concluson was that the most dscrmnatve eatures were determned by RSP, SWI, Harmonc Parameters and Parameters o Hjorth. All the 6-EEG channels provde useul normaton. On the other hand, the applcaton o the eature selecton method mproved, n general, the process o dscrmnaton by selectng the set o eatures that provded a lower percentage o dsagreement. One o the bggest problems n automatc sleep stagng based on EEG s the smlarty between patterns o derent sleep stages such as REM and NREM N. Ths can be mproved recurrng to other bosgnals, such as EOG and EMG. Another problem n ASSC s the hgh level o varablty between patents. Usng an ambulatory system, the patent can perorm perodc recordngs at home. Ths way, the rst sesson can be ully analysed by the expert. The labelled data can be used to obtan classcaton models specc N N3 to the patent. Further sessons can then use these robust user-dependent models. Ths approach s under research presently. REFERENCES Ansar-Asl. K., Chanel, G., Pun, T., A channel Selecton Method or EEG Classcaton n Emoton Assessment Based on Synchronzaton Lkelhood. In EUSIPCO 07, Doroshenkov, L., Konyshev, V., Selshchev, S., 007, Classcaton o Human Sleep Stages Based on EEG Processng Usng Hdden Markov Models. Bomedcal Engneerng, 4(), 5-8. Ebrahm, F., Mkael, M., Estrada, E., Nazeran, H., 008, Automatc Sleep Stage Classcaton Based on EEG Sgnals by Usng Neural Networks and Wavelet Packet Coecents. In IEEE EMBS 08,, Guyon, I., Elssee, A., 003, An ntroducton to varable and eature selecton. Journal o Machne Learnng Research, 3, Hejden, F., Dun, R., Rdder, D., Tax, D., 004, Classcaton, Parameter Estmaton and State Estmaton. John Wley & Sons. Hese, P., Phlps, W., Konnck, J., Walle, R., Lemaheu, I., 00, Automatc Detecton o Sleep Stages Usng the EEG. In IEEE EMBS 0,Proc. o, Iber., C., Ancol-Israel, S., Chesson, A., Quan, S., 007, The AASM Manual or the scorng o Sleep and Assocated Events: Rules. Termnology and Techncal Speccatons (st ed.). Westchester, Illnos: Amercan Academy o Sleep Medcne. Rechtscheen, A., Kales, A., 968, A Manual o Standardzed Termnology, Technques ans Scorng System or Sleep Stages o Human Subjects, US Government Prntng Oce, Natonal Insttute o Health Publcatons, Washngton DC. Schwabold, M., Harms, R., Scholer, B., Pnnow, I., Cassel, W., Penzel, T., Becker, H., Bolz, A., 003, Knowledge-Based Automatc Sleep-Stage Recognton Reducton n the Interpretaton Varablty. Somnologe, 7, Tang, W. C., Lu, S. W., Tsa. X. M., Kao. C. Y., Lee. H. H., 007, Harmonc Parameters wth HHT and Wavelet Transorm or Automatc Sleep Stages Scorng. Proc. o World Academy o Scence. Engneerng and Technology,, Ylmaz, A., Alkan, A., Asyal, M., 007, Applcatons o parametrc spectral estmaton methods on detecton o power system harmoncs. Electrc Power Systems Research (008), 78, Zoubek, L., Charbonner, S., Lesecq, S., Buguet, A., Chapolot, F., 007, Feature selecton or sleep/wake stages classcaton usng data drven methods. Bomedcal Sgnal Processng and Control,, 7-79.