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Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure

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Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure
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  Medical Engineering & Physics 28 (2006) 315–322 Analysis of EEG background activity in Alzheimer’s disease patients withLempel–Ziv complexity and central tendency measure Daniel Ab´asolo ∗ , Roberto Hornero, Carlos G ´omez, Mar´ıa Garc´ıa, Miguel L´opez  E.T.S. Ingenieros de Telecomunicaci´ on, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain Received 17 March 2005; received in revised form 7 June 2005; accepted 4 July 2005 Abstract In this study we have investigated the electroencephalogram (EEG) background activity in patients with Alzheimer’s disease (AD) usingnon-linear analysis methods. We calculated the Lempel–Ziv (LZ) complexity – applying two different sequence conversion methods – and thecentral tendency measure (CTM) of the EEG in 11 AD patients and 11 age-matched control subjects. CTM quantifies the degree of variability,while LZ complexity reflects the arising rate of new patterns along with the EEG time series. We did not find significant differences betweenAD patients and control subjects’ EEGs with CTM. On the other hand, AD patients had significantly lower LZ complexity values (  p <0.01)at electrodes P3 and O1 with a two-symbol sequence conversion, and P3, P4, O1 and T5 using three symbols. Our results show a decreasedcomplexity of EEG patterns in AD patients. In addition, we obtained 90.9% sensitivity and 72.7% specificity at O1, and 72.7% sensitivityand 90.9% specificity at P3 and P4. These findings suggest that LZ complexity may contribute to increase the insight into brain dysfunctionin AD in ways which are not possible with more classical and conventional statistical methods.© 2005 IPEM. Published by Elsevier Ltd. All rights reserved. Keywords:  Alzheimer’s disease; EEG; Non-linear dynamics; Lempel–Ziv complexity; Central tendency measure 1. Introduction Alzheimer’s disease (AD) is the most common neurode-generative disease and is considered to be the main causeof dementia in western countries [1]. AD is characterizedby progressive impairments in cognition and memory whosecourse lasts several years prior to death. These clinical fea-tures are accompanied by characteristic histological changesin the brain. They include diffuse atrophy of the cortexandmicroscopicalneuriticplaques(containingamyloidA  ),neurofibrillarytanglesanddepositsofamyloidinthewallsof the brain arteries. Although a definite diagnosis is only pos-sible by necropsy, a differential diagnosis with other typesof dementia and with major depression should be attempted.Magnetic resonance imaging and computerized tomographycan be normal in the early stages of AD but a diffuse corticalatrophy is the main sign in brain scans. Mental status testsare also useful. ∗ Corresponding author. Tel.: +34 983 423981; fax: +34 983 423667.  E-mail address:  danaba@tel.uva.es (D. Ab´asolo). The electroencephalogram (EEG) has been used as a toolfor diagnosing dementias for several decades. Typical EEGabnormalities in AD are characterized by a diffuse slowingofthebackgroundactivity[2],namelythroughanincreaseindeltaandthetaactivityalongwithadecreaseinalphaactivity,andadecreasedcoherenceamongcorticalareas[3],althoughintheearlystagesofthediseasetheEEGmayexhibitnormalfrequencies.Recent progress in the theory of non-linear dynamics hasprovided new methods for the study of the EEG [4]. Non-linearity in the brain is introduced even at the cellular level,since the dynamical behaviour of individual neurons is gov-erned by threshold and saturation phenomena. Moreover, thehypothesis of an entirely stochastic brain can be rejected duetoitsabilitytoperformsophisticatedcognitivetasks.Consid-eringthis,non-lineardynamicalanalysistechniquesmaybeabetterapproachthantraditionallinearmethodstoobtainabet-terunderstandingofabnormaldynamicsinEEGsignals[5,6]. Many studies are known in which non-linear time seriesanalysis techniques were applied to EEGs. Correlationdimension (  D 2 ), a measure of system dimensional complex- 1350-4533/$ – see front matter © 2005 IPEM. Published by Elsevier Ltd. All rights reserved.doi:10.1016/j.medengphy.2005.07.004  316  D. Ab´ asolo et al. / Medical Engineering & Physics 28 (2006) 315–322 ity [7], has been extensively investigated in EEG studies of different physiological and pathological states [8–13]. Par- ticularly, several studies have examined the  D 2  in AD. Theidea behind them is that the loss of neurons and synapsesresults in less complex dynamics of neural networks and,consequently, reduced  D 2  values [14]. It has been found thatAD patients have lower  D 2  values than controls [10,15–17]. These results show a decrease in the complexity of the elec-trical activity in brains injured by AD [4].TheLyapunovexponentshavealsotraditionallybeenusedto characterize non-linear behaviour: if the first Lyapunovexponent (L1) is positive, the system is “chaotic” [18]. L1 reflects the “unpredictability” of the underlying system andhas been applied in EEG analysis to study the changes innormalbraindevelopment[19].Moreover,ithasbeenshownthatADpatientshavesignificantlylowerL1valuesthancon-trols in almost all EEG channels [16,17]. The decrease of L1 in the EEG of AD patients reflects a drop in the flexibility of information processing in the injured brain [16].Nevertheless, the amount of data required for meaningfulresults in the computation of   D 2  and Lyapunov exponentsis beyond the experimental possibilities for physiologicaldata [20]. Moreover, the algorithms used to estimate the  D 2 assume the time series to be stationary, something generallynot true with biological data. Therefore, it becomes neces-sary to apply other non-linear methods to study the EEGbackground activity. For instance, non-linear forecasting andentropy maps have been used to characterize drug effects onbrain dynamics in AD [21] and mutual information analysisto assess information transmission between different corticalareas in AD [22]. More recently, Pijnenburg et al. [23] have foundadecreaseofbetabandsynchronizationlikelihoodbothin a resting condition and during a working memory task inAD.In the present study, we have examined the EEG back-ground activity in AD patients with two non-linear methods:Lempel–Ziv (LZ) complexity and computation of the centraltendency measure (CTM) from scatter plots of first differ-encesofthedata.WewantedtotestthehypothesisthattheADpatients’EEGmightbecharacterizedbyanabnormaltypeof dynamics. The LZ complexity [24] is a non-parametric mea-sure of complexity in a one-dimensional signal related to thenumber of distinct substrings and the rate of their recurrence.CTMisanon-linearapproachusingcontinuouschaoticmod-elling that summarizes the degree of variability in a signal[25].Thepaperisorganizedasfollows.InSection2weexplainthe selection of patients and controls, and the procedure forrecording the EEG and selecting artefact-free epochs. LZcomplexity,CTMandthestatisticaltoolsusedtoevaluatethedifferencesbetweenADpatientsandcontrolsubjectsarealsointroduced in Section 2. Section 3 presents the results of our study.Finally,inSection4wediscussourresultsandcomparethem with other studies of the EEG background activity inAD patients with non-linear analysis methods, and we drawour conclusions. 2. Methods 2.1. Selection of patients and controls We studied 11 patients (5 men and 6 women;age=72.5 ± 8.3 years, mean ± standard deviation (S.D.))fulfilling the criteria of probable AD. The patients wererecruited from the Alzheimer’s Patients’ Relatives Associ-ation of Valladolid (AFAVA) and referred to the Univer-sity Hospital of Valladolid (Spain), where the EEG wasrecorded. All of them had undergone a thorough clinicalevaluation that included clinical history, physical and neu-rological examinations, brain scans and a mini-mental stateexamination(MMSE),generallyacceptedasaquickandsim-ple way to evaluate cognitive function [26]. Five patients hada MMSE score of less than 12 points, indicating a severedegree of dementia. The mean MMSE score for the patientswas 13.1 ± 5.9 (mean ± S.D.). Two subjects were receivinglorapezam. With therapeutic doses, benzodiapzepines mayenhance beta activity, although no prominent rapid rhythmswere observed in the visual examination of these two sub- jects’ EEGs. None of the other patients used medication thatcould be expected to influence the EEG.The control group consisted of 11 age-matched, elderlycontrol subjects without past or present neurological dis-orders (7 men and 4 women; age=72.8 ± 6.1 years,mean ± S.D.). The MMSE score value for all control sub- jects was 30.Thelocalethicscommitteeapprovedthestudy.Allcontrolsubjectsandallcaregiversofthedementedpatientsgavetheirinformed consent for participation in the current study. AnEEG was recorded from all patients and controls. 2.2. EEG recording The EEGs were recorded from the 19 scalp loci of theinternational 10–20 system (channels Fp1, Fp2, F3, F4, C3,C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz andPz), with all electrodes referenced to the chin. Recordingswere made with the subjects in a relaxed state and under theeyes-closedconditioninordertoobtainasmanyartefact-freeEEGdataaspossible.Morethan5minofdatawererecordedfrom each subject using a Profile Study Room 2.3.411 EEGequipment (Oxford Instruments). Data were first processedwith a low-pass hardware filter at 100Hz, then they weresampled at 256Hz and digitised by a 12-bit analogue-digitalconverter.The recordings were visually inspected by a specialistphysician to reject artefacts. Thus, only EEG data free fromelectrooculographic and movement artefacts, and with mini-mal electromyographic (EMG) activity were selected. After-wards,EEGswereorganizedin5sartefact-freeepochs(1280points) that were copied as ASCII files for off-line analysison a personal computer. An average number of 30.0 ± 12.5artefact-free epochs (mean ± S.D.) were selected from eachelectrode for each subject.   D. Ab´ asolo et al. / Medical Engineering & Physics 28 (2006) 315–322  317 In order to remove the residual EMG activity and thenoise owing to the electrical mains, all selected epochs weredigitally filtered prior to the non-linear analysis. We used aHamming window FIR band-pass filter with cut-off frequen-cies at 0.5 and at 40Hz designed with Matlab ® . 2.3. Central tendency measure Chaotic equations are sometimes used to generate graphs.We can produce scatter plots of first differences of the datawhich graph  x  ( n +2) −  x  ( n +1) versus  x  ( n +1) −  x  ( n ), whereeach  x  ( n ) is the value of the EEG time series at time  n . Theseplots, centred around the srcin, give a graphical represen-tation of the degree of variability in the time series and areuseful in modelling biological systems such as hemodynam-ics and heart rate variability [25]. With this approach, rather thandefiningatimeseriesaschaoticornotchaotic,thedegreeof variability or chaos is evaluated.To quantify this level of variability, the central tendencymeasure (CTM) has been used [25]. CTM is computed from the scatter plots of first differences of the data selecting acircular region of radius  ρ  around the srcin, counting thenumber of points that fall within the radius, and dividing bythetotalnumberofpoints.AlowCTMvalueindicatesalargeamount of dispersion and a high value concentration near thecentre. Given  N   data points from a time series,  N  − 2 wouldbe the total number of points in the scatter plot. Then, theCTM can be computed as [25],CTM =  N  − 2 i = 1  δ ( d  i ) N  − 2 (1)where δ ( d  i ) =  1 if [( x ( i + 2) − x ( i + 1)) 2 + ( x ( i + 1) − x ( i )) 2 ] 1 / 2 < ρ 0 otherwise (2)The application of this approach to a classification prob-lem involving the separation of congestive heart failurepatients from normal individuals analysing R–R intervalsfrom Holter tapes shows promise [25]. Preliminary studies indicatethatthemethodcanbeadaptedtodeterminetheclin-ical significance of the variability findings in more complextime series such as the EEG [27]. Moreover, the combinationof CTM analysis of the EEG, clinical parameters and neu-ropsychological testing can be useful in the diagnosis of AD[28] and in the differentiation among types of dementia [29]. Although the radius is critical in determining the outcomeof CTM, no guidelines exist for optimising its value. Hence,it is usually chosen depending upon the character of the data.Wehavedevelopedanewmethodtoselecttheradius ρ .First,we compute the CTM with several radii. Then, we apply aone-way ANOVA test to compare the CTM results of bothgroups and we estimate the  p  value for each of the radii. Theselected radius is that for which we obtain the lowest  p  valueinthecomparisonbetweenbothgroups.Inthisstudywehavecomputed the CTM with  ρ =2. 2.4. Lempel–Ziv complexity The Lempel–Ziv (LZ) complexity for sequences of finitelength was suggested by Lempel and Ziv [24]. It is a non-parametric, simple-to-calculate measure of complexity in aone-dimensional signal that does not require long data seg-ments to compute [30]. LZ complexity is related to the num- berofdistinctsubstringsandtherateoftheirrecurrencealongthe given sequence [31], with larger values corresponding tomore complexity in the data. It has been applied to studythe brain function [32], brain information transmission [33] and to detect ventricular tachychardia and fibrillation [30].Preliminary evidence suggests that, applied to EEGs, LZcomplexity is predictive of epileptic seizures [31] and canbe useful to quantify the depth of anaesthesia [6,34]. More- over, it has been applied to extract complexity from mutualinformation time series of EEGs in order to predict responseduring isoflurane anaesthesia with artificial neural networks[35].LZ complexity analysis is based on a coarse-graining of themeasurements,sobeforecalculatingthecomplexitymea-sure  c ( n ), the signal must be transformed into a finite symbolsequence. In this study we have used two different sequenceconversion methods:(a)  0–1 sequence conversion . The median value is estimatedas a threshold  T  d , as partitioning about the median isrobusttooutliers[36].Bycomparisonwiththethreshold,thesignaldataareconvertedintoa0–1sequence P = s (1), s (2),  ... ,  s ( n ), with  s ( i ) defined by [6]: s ( i ) =  0 if   x ( i )  < T  d 1 if   x ( i ) ≥ T  d (3)(b)  0–1–2 sequence conversion . For each of the EEGsegments, the median  x  m , maximum  x  max  and mini-mum  x  min  are calculated. Two thresholds are obtained: T  d1  =  x  m −|  x  min |  /16 and  T  d2  =  x  m  + |  x  max |  /16 [6]. Thenthe EEG data are converted into a 0–1–2 sequence P = s (1),  s (2),  ... ,  s ( n ), with  s ( i ) defined by [6]: s ( i ) =  0 if   x ( i ) ≤ T  d1 1 if   T  d1  < x ( i )  < T  d2 2 if   x ( i ) ≥ T  d2 (4)The sequence  P  is scanned from left to right for both con-versionmethodsandthecomplexitycounter c ( n )isincreasedby one unit every time a new subsequence of consecutivecharacters is encountered. The complexity measure can beestimated using the following algorithm [6,30,34]: 1. Let  S   and  Q  denote two subsequences of   P  and  SQ  be theconcatenation of   S   and  Q , while sequence  SQ π  is derivedfrom  SQ  after its last character is deleted ( π  means the  318  D. Ab´ asolo et al. / Medical Engineering & Physics 28 (2006) 315–322 Fig. 1. Block diagram summarizing the steps followed in this study, from signal recording to the statistical analysis of the LZ complexity and CTM results. operation to delete the last character in the sequence).Let  v ( SQ π ) denote the vocabulary of all different sub-sequences of   SQ π . At the beginning,  c ( n )=1,  S  = s (1), Q = s (2), therefore,  SQ π = s (1).2. In general,  S  = s (1),  s (2),  ... ,  s ( r  ),  Q = s ( r  +1), then SQ π = s (1),  s (2),  ... ,  s ( r  ); if   Q  belongs to  v ( SQ π ), then Q  is a subsequence of   SQ π , not a new sequence.3. Renew  Q  to be  s ( r  +1),  s ( r  +2) and judge if   Q  belongs to v ( SQ π ) or not.4. Repeat the previous steps until  Q  does not belong to v ( SQ π ).Now Q = s ( r  +1), s ( r  +2), ... , s ( r  + i )isnotasub-sequence of   SQ π = s (1),  s (2),  ... ,  s ( r  + i − 1), so increase c ( n ) by one.5. Thereafter,  S   is renewed to be  S  = s (1),  s (2),  ... ,  s ( r  + i ),and  Q = s ( r  + i +1).These procedures have to be repeated until  Q  is the lastcharacter. At this time the number of different subsequencesin  P  – the measure of complexity – is  c ( n ).In order to obtain a complexity measure which is inde-pendent of the sequence length,  c ( n ) should be normalized.If the length of the sequence is  n  and the number of differentsymbols in the symbol set is  α , it has been proved [24] thatthe upper bound of   c ( n ) is given by: c ( n )  <n (1 − ε n )log α ( n ) (5)where ε n  isasmallquantityand ε n → 0( n →∞ ).Ingeneral, n  /log α  ( n ) is the upper bound of   c ( n ), where the base of thelogarithm is  α , i.e.,lim n →∞ c ( n ) = b ( n ) ≡ n log α ( n ) (6)and  c ( n ) can be normalized via  b ( n ): C ( n ) = c ( n ) b ( n ) (7) C  ( n ), the normalized LZ complexity, reflects the arising rateofnewpatternsalongwiththesequence.Thus,itcapturesthetemporal structure of the sequence. 2.5. Statistical analysis One-way ANOVA tests were used to evaluate the statis-tical differences between the estimated LZ complexity andCTM values for AD patients and control subjects. If sig-nificant differences between groups were found, the abilityofthenon-linearanalysismethodtodiscriminateADpatientsfrom control subjects was evaluated using receiver operatingcharacteristic (ROC) plots [37].ROC plots can be obtained by plotting the sensitivityvalues (the proportion of patients with a diagnosis of ADwho test positive) on the  y  axis against their equivalent(1 − specificity) values (specificity represents the percentageof controls correctly recognized) for all the available cut-off points (in this case, the non-linear analysis method values)on the  x   axis. Accuracy is a related parameter that quanti-fies the total number of subjects (AD patients and controlsubjects) precisely classified. The optimum threshold is thecut-off point in which the highest accuracy (minimal falsenegativeandfalsepositiveresults)isobtained.Itcanbedeter-mined from the ROC curve as the closest value to the left toppoint (100% sensitivity, 100% specificity).Fig. 1 shows a block diagram with the different steps fol-lowed in this study. 3. Results LZcomplexityandCTMwereestimatedforchannelsFp1,Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5 andT6. The results have been averaged based on all the artefact-free 5s epochs (  N  =1280 points) within the 5-min period of EEG recordings.The CTM values (mean ± S.D.) for the AD patients andcontrol subjects and the corresponding  p  values are sum-marized in Table 1. No significant differences were foundbetween both groups (  p >0.01).The average LZ complexity values and standard devia-tions for the AD patients and normal control subjects forthe 16 electrodes are summarized in Table 2 (0–1 sequenceconversion) and Table 3 (0–1–2 sequence conversion).The AD patients have significantly lower LZ complexityvalues (  p <0.01) at electrodes P3 and O1 for the two-symbolsequence conversion, and electrodes P3, P4, O1 and T5 forthe three-symbol sequence conversion. These results suggestthat EEG activity of AD patients is less complex in certainregions than in a normal brain.   D. Ab´ asolo et al. / Medical Engineering & Physics 28 (2006) 315–322  319Table 1TheaverageCTMvaluesoftheEEGsfortheADpatientsandcontrolsubjectsfor all channelsElectrode AD patients(mean ± S.D.)Control subjects(mean ± S.D.)Statistical analysis(  p  value)F3 0.52 ± 0.16 0.63 ± 0.14 0.1050F4 0.54 ± 0.15 0.63 ± 0.15 0.1747F7 0.31 ± 0.10 0.44 ± 0.14 0.0205F8 0.33 ± 0.14 0.45 ± 0.14 0.0795Fp1 0.40 ± 0.17 0.50 ± 0.14 0.1623Fp2 0.41 ± 0.15 0.50 ± 0.18 0.1834T3 0.23 ± 0.12 0.31 ± 0.17 0.2036T4 0.21 ± 0.11 0.35 ± 0.17 0.0336T5 0.41 ± 0.08 0.38 ± 0.19 0.6844T6 0.40 ± 0.13 0.36 ± 0.16 0.5182C3 0.61 ± 0.19 0.69 ± 0.17 0.2898C4 0.58 ± 0.19 0.67 ± 0.18 0.2746P3 0.57 ± 0.15 0.55 ± 0.21 0.7932P4 0.57 ± 0.14 0.53 ± 0.20 0.6001O1 0.40 ± 0.10 0.33 ± 0.18 0.2097O2 0.41 ± 0.10 0.32 ± 0.16 0.1224Table 2The average LZ complexity (0–1 sequence conversion) values of the EEGsfor the AD patients and control subjects for all channelsElectrode AD patients(mean ± S.D.)Control subjects(mean ± S.D.)Statistical analysis(  p  value)F3 0.35 ± 0.06 0.39 ± 0.08 0.2909F4 0.38 ± 0.05 0.38 ± 0.08 0.8025F7 0.39 ± 0.06 0.40 ± 0.08 0.7425F8 0.39 ± 0.05 0.41 ± 0.07 0.5213Fp1 0.33 ± 0.09 0.39 ± 0.06 0.0730Fp2 0.33 ± 0.06 0.37 ± 0.09 0.2264T3 0.47 ± 0.09 0.48 ± 0.12 0.8449T4 0.48 ± 0.11 0.47 ± 0.10 0.8539T5 0.39 ± 0.08 0.46 ± 0.07 0.0236T6 0.38 ± 0.08 0.46 ± 0.08 0.0521C3 0.39 ± 0.08 0.43 ± 0.06 0.2166C4 0.41 ± 0.09 0.43 ± 0.05 0.4098P3 * 0.35 ± 0.07 0.44 ± 0.05 0.0017P4 0.39 ± 0.07 0.44 ± 0.05 0.0124O1 * 0.39 ± 0.07 0.49 ± 0.07 0.0056O2 0.39 ± 0.07 0.47 ± 0.08 0.0214Significant group differences are marked with an asterisk. Finally, we evaluated the ability of the LZ complexityto discriminate AD patients from control subjects with bothsequence conversions at the electrodes in which significantdifferences were found using ROC plots [37]. Table 4 sum- marizes the results. Table 3TheaverageLZcomplexity(0–1–2sequenceconversion)valuesoftheEEGsfor the AD patients and control subjects for all channelsElectrode AD patients(mean ± S.D.)Control subjects(mean ± S.D.)Statistical analysis(  p  value)F3 0.33 ± 0.09 0.37 ± 0.08 0.2319F4 0.37 ± 0.06 0.37 ± 0.07 0.8442F7 0.36 ± 0.10 0.39 ± 0.07 0.3801F8 0.36 ± 0.09 0.40 ± 0.07 0.2324Fp1 0.29 ± 0.11 0.37 ± 0.08 0.0710Fp2 0.31 ± 0.07 0.38 ± 0.07 0.0500T3 0.44 ± 0.08 0.47 ± 0.10 0.4397T4 0.45 ± 0.09 0.45 ± 0.09 0.9735T5* 0.38 ± 0.06 0.45 ± 0.06 0.0072T6 0.35 ± 0.10 0.44 ± 0.07 0.0194C3 0.35 ± 0.11 0.42 ± 0.05 0.0810C4 0.33 ± 0.13 0.42 ± 0.04 0.0456P3 * 0.33 ± 0.07 0.43 ± 0.04 0.0005P4 * 0.34 ± 0.06 0.42 ± 0.05 0.0028O1 * 0.38 ± 0.06 0.47 ± 0.06 0.0029O2 0.37 ± 0.06 0.45 ± 0.08 0.0225Significant group differences are marked with an asterisk. The value for the area under the ROC curve can be inter-preted as follows: an area of 0.893 (electrode P3 with 0–1–2sequence conversion, for example) means that a randomlyselected individual from the control subjects’ group has a LZcomplexity value larger than that of a randomly chosen indi-vidualfromtheADpatients’groupin89.3%ofthetime[37].A rough guide to classify the precision of a diagnostic test isrelatedtotheareaundertheROCcurve.Withvaluesbetween0.90 and 1 the precision of the diagnostic test is consideredto be excellent, good for values between 0.80 and 0.90, fairif the results are in the range 0.70–0.79, poor when the valueof the area under the ROC curve is between 0.60 and 0.69,and bad for values between 0.50 and 0.59. Thus, the resultsobtained with LZ complexity and both sequence conversionmethods can be considered good.With the 0–1 sequence conversion the highest sensitivitywas obtained at O1 (90.9%) and the highest specificity at P3(90.9%). The accuracy of the diagnostic test was similar atboth electrodes (81.8%). Fig. 2 shows the ROC curves forboth electrodes.Using a three-symbol sequence conversion we obtainedthe highest specificity at P4 (90.9%), while the highest sensi-tivity was obtained at O1 (90.9%). The accuracy was 81.8%at P3, P4 and O1. Fig. 3 shows the ROC curves for all the Table 4Test results for LZ complexity with both sequence conversion methods on the channels in which the differences between both groups were significant: theoptimum threshold to discriminate AD patients and control subjects is includedLZ complexity sequence conversion Electrode Threshold Sensitivity (%) Specificity (%) Accuracy (%) Area under the ROC curve0–1 P3 0.3995 72.7 90.9 81.8 0.876O1 0.4538 90.9 72.7 81.8 0.8510–1–2 P3 0.3962 81.8 81.8 81.8 0.893P4 0.3485 72.7 90.9 81.8 0.843O1 0.4411 90.9 72.7 81.8 0.851T5 0.4160 72.7 72.7 72.7 0.802
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