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Brain research journal regional differences nova


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Brain research journal regional differences nova

  1. 1. REGIONAL DIFFERENCES IN NEONATAL SLEEP ELECTROENCEPHALOGRAM Karel Paul 1), Vladimír Krajča 2), Zdeněk Roth 3), Jan Melichar 1), Svojmil Petránek 2) 1) Institute for the Care of Mother and Child, Prague, Czech Republic 2) Faculty Hospital Bulovka, Department of Neurology, Prague, Czech Republic 3) National Institute of Public Health, Prague, Czech RepublicKarel PaulInstitute for the Care of Mother and Child14 710 Prague 4Czech RepublicTel. : +420 296511498 Fax. : +420 241432572 e-mail:
  2. 2. ABSTRACTBackground and purpose: While EEG features of the maturation level and behavioral statesare visually well distinguishable in fullterm newborns, the topographic differentiation of theEEG activity is mostly unclear in this age. The aim of the study was to find out wether theapplied method of automatic analysis is capable of descerning topographic particulaities ofthe neonatal EEG. A quantitative description of the EEG signal can contribute to objectiveassessment of the functional condition of a neonatal brain and to rafinement of diagnosticsof cerebral dysfunctions manifesting itself as “dysrhytmia”, “dysmaturity” or“disorganization”.Subjects and methods: We examined polygraphically 21 healthy, full-term newborns duringsleep. From each EEG record, two five-minute samples were subject to off-line analysis andwere described by 13 variables: spectral measures and features describing shape andvariability of the signal. The data from individual infants were averaged and the number ofvariables was reduced by factor analysis.Results: All factors identified by factor analysis were statistically significantly influencedby the location of derivation. A large number of statistically significant differences was alsofound when comparing the data describing the activities from different regions of the samehemisphere. The data from the posterior-medial regions differed significantly from the otherstudied regions: They exhibited higher values of spectral features and notably highervariability. When comparing data from homotopic regions of the opposite hemispheres, weonly established significant differences between the activities of the anterior-medial regions:The values of spectral features were higher on the right than on the left side. The activitiesfrom other homotopic regions did not differ significantly.Conclusion: The applied method of automatic analysis is capable of discerning differencesin the sleep EEG activities from the individual regions of the neonatal brain.Significance: The capability of the used method to discriminate regional differences of theneonatal EEG represents a promise for their application in clinical practice. Keywords: Full-term newborn; EEG; Regional differences; Automatic analysisINTRODUCTION
  3. 3. When analyzed visually, the EEG activity of sleeping full-term newborns at the firstglance appears topographically non-differentiated for the most part. The EEG atlasesdealing with the earliest age either do not mention the regional differences in the EEGsignal in full-term sleeping newborns at all [1,2], or the EEG activity of these infants isbeing described as „uniformly distributed‟ [3]. The reason for this is probably the fact thatthe human eye will not discern differences in the activities from the individual cranialregions. However, the application of computing technology has proven that theelectroencephalogram of a full-term newborn is in fact topographically differentiated. Theregional differences in the values of spectral energies were described [4-8].Intrahemispheric and interhemispheric coherence of the EEG activity had been studied [8-14]. Automatic brain mapping was applied to the neonatal EEG [15,16]. Topographicinterdependencies of the neonatal EEG have been examined by the means of non-linearmethods [17-20]. In the present study, we have applied a multi-channel automatic method based onadaptive segmentation [21] in order to describe the EEG activity from the specific regionsof the neonatal brain. This method evaluates not only spectral measures but also additionalfeatures as amplitude level, shape and variance of the signal, in which it comes close tovisual analysis. The objective of the study is to verify whether the applied method is capableof discerning the differences in the EEG activities of the specific brain regions. It is possibleto suppose that if the used method is able to discerne physiological regional EEGdifferences it will be possible to use the method in a detection and objective description oftopographic deviations in patients with a cerebral pathology.SUBJECTS We included 21 healthy full-term newborns in the study. They were born in the 39thto the 40th week of gestation, the Apgar score was >7 in the first minute and >8 in the fifthminute, and their birthweights ranged from 3010 to 3950g. The infants were examined inthe 4th and 10th day of their life. Parents of the infants were informed of the methods andpurposes of the examination and gave their consent. The project was approved by theinstitute‟s ethical commission.METHODS
  4. 4. The examinations were carried out in an EEG laboratory in standardized conditionsafter morning feeding and lasted 90-120 minutes. The examination room was noise-protected and background noise level did not exceed 45dB. The illumination level wasreduced to a degree that would enable the observer to just perceive changes in infant‟sbehavior. Room temperature was in the 23-25°C range. Disturbing environmental stimuliwere excluded. Infants were examined in a crib, placed in supine position. The EEG activitywas recorded polygraphically from eight bipolar derivations, positioned under the system10-20 (Fp1-C3, C3-O1, Fp1-T3, T3-O1, Fp2-C4, C4-O2, Fp2-T4, T4-O2); the referencederivation, linked ear electrodes; filter setting, 0,2 and 60Hz; sensitivity, 100μV per 10mm.The respiration (PNG), ECG, EOG, and EMG of chin muscles were also recorded.Electrode impedances were not higher than 5kOhm. The recording was performed using theBrain-Quick (Micromed) digital system with sampling frequency of 128Hz and the datawere stored on CDs. An observer continuously recorded any change in infant‟s behavior onthe polygram. Two five-minute-samples free of artifacts (segments contaminated by artifacts wereeliminated by visual inspection) were selected from the EEG record of each infant. Onesample was chosen from the middle part of quiet sleep, the other from the middle of thesubsequent active sleep. In this study, we have defined mentioned sleep states according tothe following criteria: Quiet sleep was defined as sleep with closed eyes, absence of eyemovements, regular breathing, absence of body movements except for startles, and thetypical EEG pattern „tracé alternant‟. Active sleep was defined as a behavioral state inwhich the infant‟s eyes were closed or nearly closed, eye movements were apparent,breathing was irregular, and mimic muscle movements, small movements of extremities andeven large generalized movements occurred intermittently. The EEG showed the „activitémoyenne„ pattern [3]. Quantitative processing of EEG was performed off-line. Subject to analysis weredata from the above-mentioned bipolar montage. A method based on multi-channel adaptivesegmentation [21] was used. The method was selected for the following reasons: (a) Thealgorithm of the adaptive segmentation divides the EEG signal into quasi-stationarysegments of variable length. The idea was that the feature extraction from such relativelyhomogeneous epochs would be substantially more effective than the feature extraction fromfixed epochs. This holds especially true when analyzing the highly variable pattern as tracé
  5. 5. alternant. (b) The division of the signal into quasi-stationary segments made it possible toevaluate length, number and proportional occurrence of these segments and thus to quantifythe stability and variability of the signal. The method of applied automatic analysis was explained in detail in our previouspaper [22]. Therefore it is described only briefly in this study. Using adaptive segmentation,the EEG signal from each derivation was divided into relatively homogeneous segments ofvariable length. The limits of the segments were in fact defined by the change in stationarycharacter of the signal. The segments were distributed into three classes according to theirmaximum voltage. The segments whose amplitude didn‟t exceed 50μV were placed into the1st class, the 2nd class contained segments with voltage higher than 50μV and lower than90μV, and the 3rd class was occupied by segments with the amplitude of 90μV and more.Examples of the application of adaptive segmentation and the distribution of segments intovoltage classes are presented in Fig. 1. The activity of each segment was then described byten features. The AV feature described the variance of the segment‟s amplitude; Mm definedthe value of the maximum amplitude „peak-to-peak‟; the following five features providedinformation about the value of spectral amplitude in five frequency bands, δ1 in the 0.2-1.5Hz band, δ2 in the 1.6-3Hz band, θ1 in the 3.1-5Hz band, θ2 in the 5.1-8Hz band, α inthe 8.1-15Hz band; feature D1 described the steepness of the curve; D2 described itssharpness; ØF informed about the average frequency of an activity in the segment. The dataof the features describing each segment were then averaged in each class, and for each classthree additional features were extracted: t% defines the time percentage of the specific classoccurence; No gives the number of segments of a specific class; L provides the informationabout the average duration of the segments of a specific class in sec. In this manner theautomatic analysis provided 312 values (8 derivations x 3 classes x 13 features) from thefive-minute-sample of the analyzed EEG signal. An example of the numeric output of theautomatic analysis is presented in Table 1.STATISTICAL ANALYSIS The data collected from individual infants were averaged and the number ofvariables taken into account was reduced by means of factor analysis. Using the principalcomponent analysis, three factors – Fc1, Fc2, Fc3 – were extracted, transformed byVarimax rotation with the Kaiser normalization and the respective factor scores were
  6. 6. computed. Table 2 shows the list of factors identified by the factor analysis and the list offeatures represented by the specific factors; furthermore the table shows data about theeigenvalues of factors and the percentage of variance explained by these factors. In the first phase of the statistical analysis we tested (a) the effect of brain region,(the activity from each brain region is represented by a symbol of the individual bipolarderivation: Fp1-C3, …, T4-O2), (b) the effect of voltage class (low-, mid-, and high-voltageclass), (c) the effect of sleep state (quiet and active sleep), and (d) the mutual statisticaldependences of these effects on all three factors using the method of General Linear Model;the Wilks‟ multivariate test (λ) evaluated by means of F-test served as criterion.Subsequently using the F-test, the effect of brain region upon each factor was testedseparately, as well as the effect of voltage class, the effect of sleep state and mutualdependences of these effects. In the next phase of the statistical analysis, in order to determine the differencesbetween the individual brain regions, we evaluated the vector of the 13 EEG features ineach voltage class separately both in quiet and in active sleep. Using the General LinealModel method, we employed the multidimensional analysis of variance, which furthermodifies the calculations of comparative tests with regard of mutual correlations betweenthe 13 features, so that the final tests are not affected by these correlations. Following theinitial parallel analysis of the 13 features, we compared in detail the effects of individualbrain regions for each of the 13 features using the test according to Šidák. These tests forthe individual features serve as an explanatory supplement to the basic multidimensionaltests and they illustrate which brain areas and which features participate in the topographicdifferences, and the direction of these differences.RESULTSThe effect of brain region By evaluating the effect of brain region we were testing the presence of topographicdifferentiation of EEG activity. The influence upon the factors identified by factor analysisare shown in Table 3. It is apparent that both the entire set of factors – Fc1, Fc2, Fc3 – andeven each individual factor are highly significantly influenced by the brain region. Thismeans that both the factor Fc1 representing above all spectral features, and the Fc2 and Fc3factors, which represent non-spectral features, are influenced.
  7. 7. The effect of voltage class and dependence between the effects of brain region and voltageclass (Table 3) When analyzing the effect of voltage class we were testing whether the studiedfeatures of EEG signal differ significantly in individual voltage classes. We established thatthe effect of voltage class significantly influenced the entire set of factors as well as eachfactor in particular. We have also found a statistically significant dependence between theeffect of brain region and the effect of voltage class for all three factors together and foreach factor in particular, which points to the fact that the effect of brain region is different ineach voltage class.The effect of sleep state and dependence between the effects of brain region and sleep state(Table 3) The sleep state also significantly influenced all the analyzed factors together as wellas each factor separately. We have also proven the presence of a significant dependencebetween the effect of brain region and the effect of sleep state, documenting that the brainregion effect is influenced by the sleep state, for all the three studied factors as a whole andfor factors Fc1 and Fc3.The effect of brain region on the EEG features The results of the comparison of measured values of the EEG features between theindividual brain areas with respect to the voltage class and to the sleep state are depictedsynoptically in Fig. 2. First we compared the data from the specific regions of the givenhemisphere to one another, so that each region was compared to the other regions of thehemisphere (Fp1-C3 vs. C3-O1, …, C4-O2 vs. T4-O2; the activities from the studiedregions are in this case represented by the symbols of the specific derivations). Then wecompared the data from the homotopic regions of the two hemispheres to one another (Fp1-C3 vs. Fp2-C4, …, T3-O1 vs. T4-O2). In this way we have mutually compared the activityfrom the total of 12 pairs of regions altogether. In each pair of regions we were comparing39 pairs of items (13 features x 3 voltage classes). In the end we have acquired 468 items(12 pairs of areas x 39 pairs of items) for each sleep state, which provide the information onthe occurrence of statistically significant differences between the compared data, or lackthereof.
  8. 8. While comparing data describing the activities from the individual regions of thesame hemisphere, we have found a large number of significantly different values. (a) Wehave found most differences between the activities of the anterior and posterior medialregions (Fp1,2-C3,4 vs. C3,4-O1,2). It became evident that spectral features (AV, …, D2)and the feature No have significantly higher values in posterior regions. On the other handthe low voltage class values of the L and t% features were significantly higher in anteriorregions. (b) The differences in activities of the anterior and posterior temporal regions(Fp1,2-T3,4 vs. T3,4 – O1,2) were distinguished by the following fact: While the majorityof spectral features (AV, …θ2) of the high-voltage and mid-voltage class reachedsignificantly higher values in the anterior regions, the values of the α, D1 and D2 features inthe mid-voltage and low-voltage classes were higher in the rear. The values of non-spectralfeatures from both regions mostly did not differ. (c) We established sleep state dependentdifferences while comparing the values from the anterior-medial and anterior-temporalregions (Fp1,2-C3,4 vs. Fp1,2-T3,4). In quiet sleep, the values of spectral features (δ2, …,α) were higher in the medial regions, on the contrary in active sleep spectral featurespresented higher values in lateral regions. (d) The differences between activities from theposterior-medial and posterior-lateral regions (C3,4-O1,2 vs. T3,4-O1,2) were noted formedially localized higher values of spectral features (AV, …, α). However in active sleepthe α, D1, D2 features exhibited higher values laterally. The non-spectral features L and t%had in the low-voltage class significantly higher values temporally, while the values of thet% and No features in the high-voltage class were higher medially. The comparison of data from the homotopic regions of the opposite hemispheresexhibited only small number of statistically significant differences. Only activities from theright and left anterior-medial regions (Fp1-C3 vs. Fp2-C4) differed significantly from eachother: Most spectral features showed higher values on the right side. Lateral differencesbetween the other regions were rare. Each feature contributed to the topographic differentiation to a different degree.Features θ1, θ2, α, and δ2 exhibited the highest occurence of significantly different entriesin quiet sleep; in turn in active sleep, these were the features δ2, t%, D1 and α. In featureØF, we have encountered fewest significant differences.CONCLUSION
  9. 9. The main objective of the study was to establish whether the applied method iscapable of discerning topographic particularities of the neonatal EEG. The statisticalanalysis proved that the effect of the brain region influences all the factors, representing theoriginal measured EEG variables, in a highly significant manner. In this way itdemonstrated that the applied method is adequately sensitive and that it is capable todistinguish regional specifities of the neonatal EEG. Beside that the statistical analysisproved that all factors are significantly influenced by both voltage class and sleep state.Mutual statistical dependences between the effects of the brain region and voltage class andbetween the effects brain region and sleep state have been found. Paired comparison of the data acquired from each region of the individualhemisphere exhibited substantial number of significantly different values. Our findings arethus in accord with the outcomes of the preceding studies, which suggest topographicaldifferences in the values of spectral energies [6-8,10] and in the EEG complexity [17-20].Topographic differences in EEG activity are no doubt connected to the describedmorphological differences of the individual regions of the neonatal brain [23, 24], to theestablished regional variances in the brain metabolism [25, 26], as well as to the identifiedlocal differences in the maturation of brain structures [27,28]. We found that spectralfeatures exhibited higher values in medial derivations than in lateral ones, and at the sametime higher values in posterior than in anterior regions. The above mentioned findingsapparently testify to a more advanced functional organization in the posterior-medialregions of the brain cortex. The analysis of non-spectral features has shown that the low-voltage and mid-voltage segments of greater length (L) occupy greater time percentage (t%)in the activity of the anterior-medial and posterior-lateral regions. The activity of theseregions is therefore less changeable, more rigid, and apparently contributes decisively to thelow-voltage and mid-voltage part of the tracé alternant pattern. While comparing the data measured in the homotopic regions of the twohemispheres, we found greater number of significantly different values only between theactivities from the anterior-central regions. Right spectral features exhibited mostly highervalues than the same features on the left. When comparing the activities from the remaininghomotopic regions of the two hemispheres, we have not found any other markeddifferences. Consequently it became evident, that when using our method, the neonatal EEGactivity appears predominantly symmetrical. Other authors have come to a similar
  10. 10. conclusion [8, 29, 30]. One of the probable causes of the bilateral EEG symmetry are theconnections running through the corpus callosum. The described symmetry of the EEGactivity can, however, also support the idea that the functional organization of the majorityof homotopic cortical regions is not yet laterally distinguished in the neonatal period. In the present study, we have shown that the applied automatic method is capable ofdiscerning the differences in the EEG signals from the different regions of the neonatalbrain. We have also proven that the topographic differences in the neonatal EEG pertain notonly spectral measures, as it is evident from the preceding computer-aided studies, but thatthe topographic differences also pertain the shape and variance of the EEG signal – a factthat has so far solicited no attention. We believe that the discriminatory capabilities of theused method represent a promise for its application in clinical practice.REFERENCES1 De Weerd AW. Atlas of EEG in the first months of life. Amsterdam: Elsevier;1995.2 Mizrahi EM, Hrachovy RA, Kellaway P. Atlas of neonatal electro-encephalography. 3rd ed. Philadelphia: Lippinncot, Williams and Wilkins; 2004.3 Stockard-Pope JE, Werner SS, Bickford R. G. Atlas of neonatal electroencephalography. 2nd ed. New York: Raven Press; 1992.4 Eiselt M, Schendel M, Witte H, Dorschel J, Cruzi-Dascalova L, D`Allest AM et al. Quantitative analysis of discontinuous EEG in premature and full-term newborns during quiet sleep. Electroencephalogr clin Neurophysiol, 1997;103:528-534.5 Paul K, Krajča V, Roth Z, Melichar J, Petránek S. Quantitative topographic differentiation of the neonatal EEG. Clin Neurophysiol, 2006;117:2050-2058.6 Scher MS, Steppe DA, Sclabassi RJ, Banks DL. Regional differences in spectral EEG measures between healthy term and preterm infants. Pediatr Neurol, 1997;17:218-223.7 Thordstein M, Flisberg A, Lofgren N, Bagenholm R, Lindecrantz K, Wallin BG et al. Spectral analysis of burst periods in EEG from healthy and post-asphyctic neonates. Clin Neurophysiol, 2004;115:2461-2466.
  11. 11. 8 Willekens H, Dumermuth G, Duc G, Meith D. EEG spectral power and coherence analysis in healthy full-term neonates. Neuropediatrics, 1984;15:180-190.9 Duffy FH, Als H, Mc Anulty GB. Infant EEG spectral coherence data during quiet sleep; unrestricted principal components analysis-relation of factors to gestational age, medical risk and neurobehavioral status. Clin Electroencephalogr, 2003;34:54- 69.10 Eiselt M, Schindler J, Arnold M, Witte H, Zwiener U, Frenzl J. Functional interactions within the newborn brain investigated by adaptive coherence analysis of EEG. Neurophysiol Clin, 2001;31:104-114.11 Kuks JBM, Vos JB, O`Brien MJ. EEG coherence function for normal newborns in relation to their sleep state. Electroencephalogr clin Neurophysiol, 1988;69:295-302.12 Parmelee AH, Akyiama Y, Schulz M, Wenner WH, Schulte JF. Analysis of the EEG of sleeping infants. Activ Nerv Super, 1969;11:111-115.13 Prechtl HFR, Vos JE, Akyiama Y, Casaer P. Neonatal EEG: Neonatal EEG coherence function in relation to intrauterine growth and oestrogen levels.In: Jílek L, Trojan S, editors. Ontogenesis of the brain, vol.2. Praha: Universita Karlova; 1974;201-210.14 Scher MS, Jones BL, Steppe DA, Cork DL, Seltman HJ, Banks D. Functional brain maturation in neonates as measured by EEG-sleep analysis. Clin Neurophysiol, 2003;114: 875-882.15 Hughes JR, Kohram MH. Topographic mapping of the EEG in premature infants and neonates. Clin Electroencephalogr, 1989;20:228-234.16 Mandelbaum DE, Krawciw N, Assing E, Ostfeld B, Washburn D, Rosenfeld D etal. Topographic mapping of brain potentials in the newborn infant: the estabilishment of normal values and utility in assessing infants with neurological injury. Acta Pediatr, 2000; 89:1104-1110.17 de la Cruz MD, Manas S, Pereda E, Garrido JM, Lopez S, De Vera R et al. Maturational changes in the interdependecies between cortical brain areas of neonates during sleep. Cerebr Cortex, 2007;17:583-590.18 Meyer-Lindenberg A. The evolution of comlexity in human brain development: an EEG study. Electroencephalogr clin.Neurophysiol, 1996;99:405-411.
  12. 12. 19 Pereda E, de la Cruz DM, Maňas S, Garrido JM, Lopez S, De Vera R et al. Topography of EEG complexity in human neonates: Effect of postmenstrual age and sleep state. Neurosci Lett, 2006;394:152-157.20 Pereda E, Maňas S, De Vera L, Garido JM, López S, Gonzáles JJ. Non-linear asymetric interdependencies in the electroencephalogram of healthy term neonates during sleep. Neurosci Lett, 2003;337:101-105.21 Krajča V, Petránek S, Patáková J,Varri A. Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering . Int Biomed Comput, 1991;28:71-89.22 Paul K, Krajča V, Roth Z, Melichar J, Petránek S. Comparison of quantitative EEG characteristics of quiet and active sleep in newborns. Sleep Med, 2003;4:543-552.23 Conel J. The postnatal development of the human cerebral cortex. Vol.6.Cambridge: Harvard University Press; 1939-1963.24 Salamon G. Magnetic resonance imaging of the human brain: an anatomic atlas. New York: Raven Press; 1990.25 Chugani HT, Phelps ME. Maturational changes in cerebral function in infants determined by 18FDG positron emission tomography. Science, 1986; 231: 840-843.26 Chugani HT, Phelps ME. Imaging human brain development with positronemission tomography. J Nucl Med, 1990;32:23-25.27 Barchovich AJ. Normal development of the neonatal and infant brain. In: Barchovich AJ, editor. Pediatric neuroimaging. New York: Raven press; 1990.28 Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Compar Neurol, 1997;387:167-178.29 Varner JL, Ellingson RJ, Danahy T, Nelson B. Interhemispheric amplitudesymmetry of the EEGs of normal full-term newborns. Electroencephalogr clinNeurophysiol, 1976; 40:215-216.30 Sterman MB, Harper RM, Havens B, Hoppenbrowers T, Mc Ginty DJ, Hodgman JE.Quantitative analysis of infant EEG development during sleep. Electrencephalogr clin neurophysiol, 1977;43:371-385.ACKNOWLEDGEMENTS
  13. 13. This work was supported by the research program “Information Society” under Grant No.1ET101210512 “Intelligent methods for evaluation of long-term EEG recordings” , and byGrant IGA MZ ČR 1A8600.
  14. 14. 100μV; 1 secQS AS Fp1-C3 C3 -O1 Fp1-T3 T3 -O1 Fp2-C4 C4 -O2 Fp2-T4 T4 -O2 PNG EOG ECG EMG Fig. 1.
  15. 15. QSAS
  16. 16. AV Mm δ1 δ2 θ1 θ2 α D1 D2 ØF t% No L AV Mm δ1 δ2 θ1 θ2 α D1 D2 ØF t% No L A A P P P P ┐3 Fp1- 3┌ P PP P P P P P P P P A ┤2 C3 2├ A P P P P P P P ┘1 x 1└P P P P P P P P P A P A C3- P P P P P P P P A A O1 A A A P A P P ┐ Fp2- ┌ P PP P P P A P A ┤ C4 ├ A A A P P P P P P ┘ x └P P P P P P P P A A P A C4- P P P P P P P P P A P A O2A A A A A ┐ Fp1- ┌ A P A A P P P P ┤ T3 ├ A A A P P P ┘ x └ P P P T3- A A A P P O1A A A A A A A ┐ Fp2- ┌ A A P P P ┤ T4 ├ A A A P P P P ┘ x └ P P P P A T4- P P P O2 L M M M ┐ Fp1- ┌ L M M M M ┤ C3 ├ L L M L L L M ┘ x └ M M M M Fp1- L M L L L L L T3 M M M M ┐ Fp2- ┌ M M M M ┤ C4 ├ L L M L L L L M ┘ x └ M M M M L Fp2- M L L L T4 L M M M M M M ┐ C3- ┌ M M M L M M M M L M L ┤ O1 ├ M L M L L M M ┘ x └M M M M M M L L T3- M M M M M L L L L O1M M M M M M M M ┐ C4- ┌ M M MM M M M M L L ┤ O2 ├ M M M L L L M M ┘ x └M M M M M L M L T4- M M M M M L L L L L O2 D D D D D D S ┐ Fp1- ┌ D ┤ C3 ├ D D D D D S ┘ x └ S D Fp2- D C4D D ┐ C3- ┌ ┤ O1 x ├ ┘ C4- └ O2 ┐ Fp1- ┌S ┤ T3 x ├ ┘ Fp2- └ T4 ┐ T3- ┌ S ┤ O1 x ├ S ┘ T4- └ D O2 Fig. 2
  17. 17. Table 1 An example of the automatic analysis outputFp1-C3 AV Mm δ1 δ2 θ1 θ2 α D1 D2 ØF t% No L3: 29.5 119.0 134.1 94.4 54.0 32.5 15.7 25.3 26.3 2.4 21 27 22: 17.6 73.3 80.9 50.2 33.9 22.1 11.6 19.7 21.5 2.5 31 33 21: 11.1 44.7 50.0 28.0 18.1 11.9 6.7 12.4 14.8 3.0 48 37 3Numerical data obtained by the analysis of a 5–minute-period of the EEG activity from thechannel Fp1-C3 in quiet sleep. 1,2,3, voltage classes; AV,…,L, features.Table 2Features representation, eigenvalues and percentage of variance of factors identified byfactor analysisFactors Representation of features Eigenvalues % of varianceFc1 AV,Mm,δ1,δ2,θ1,θ2,α,D1,D2, ØF 7.38 56.76Fc2 No,t% 1.49 11.47Fc3 L 1.31 10.08
  18. 18. Table 3The effects of brain area, voltage class and sleep stat upon the factors identified by factoranalysis and the effects interactionsEffects Brain area Volt. class Sleep state Area x Class Area xSleep F p F p F p F p F pFactorsFc1,Fc2,Fc3 4.76 < .001 317.00 < .001 298.66 < .001 6.86 < .001 2.27 =.001Fc1 3.60 < .001 610.09 < .001 46.21 < .001 2.51 = .002 3.06 =.003Fc2 5.19 < .001 258.64 < .001 436.92 < .001 13.16 = .037 1.24 =.274Fc3 6.33 < .001 412.28 < .001 452.85 < .001 4.72 < .001 2.50 =.015