Analysis of EEG background activity in Autism disease patients with Bispectrum and STFT measure

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1 Proeedings of the th WSEAS International Conferene on COMMUNICATIONS, Agios Nikolaos, Crete Island, Greee, July 6-8, Analysis of EEG bakground ativity in disease patients with Bispetrum and STFT measure A. Sheikhani A, H. Behnam B, M.R. Mohammadi C, M. Noroozian D A-Department of Biomedial Engineering, Islami azad university siene and researh branh, Tehran, Iran B-Department of Eletrial Engineering, Iran university of siene & Tehnology C, D -Department of psyhatiar and neuroloyy, Tehran Medial University.Iran. sheikhaniali@yahoo.om Abstrat Eletroenephalography (EEG) is an essential tool for the evaluation and treatment of neurophysiologi disorders. Careful analysis of the EEG reords an provide insight and improved understanding of the mehanism ausing disorders. In this study we have investigated the EEG bakground ativity in patients with disease using frequeny analysis methods. We alulated Bispetrum tansform, short time Fourier transform (STFT) and also STFT at bandwidth of total spetrum (we named it STFT-BW) for hannel of EEG. Coeffiients of the EEG in 0 patients and 7 age ontrol subjets with the same age were measured. These oeffiient assessments with variane analysis. We did not find any signifiant differenes between patients and ontrol subjets EEGs with Bispetrum and STFT. On the other hand, patients had signifiantly differene STFT at bandwidth (STFT- BW). At eletrodes FP, F and T5 with (p<0.0) and F7, T and O with (p<0.05) had signifiantly differenes. In addition our findings suggested that STFT-BW an disriminate 8.4% between and subjet with mahalanobis distane. Keywords: Bispetrum; STFT; EEG; ; A I. INTRODUCTION UTISM spetrum disorders (ASDs) are devastating onditions with an onset in early hildhood and ore symptoms of varying degree involving ommuniation and soial and ognitive development, and usually sparing gross motor development. In 94, Kanner [] first desribed the ase of an autisti individual who developed epilepsy, and sine then, multiple ase reports or population series have desribed an assoiation of ab EEG findings within autisti individuals [-5]. A- PhD student of Biomedial Engineering Faulty, Eslami of Azad University.Thehran,Iran. Tel: FAX: sheikhaniali@yahoo.om B-Assistant Professor of Department of Eletial Engineering, Iran University of Siene & Tehnology, Tehran, Iran, behnam@iust.a.ir C-Professor of psyhiatry, Tehran Medial University. mohammadimr@tums.a.ir D-Assoiate Professor of Neurology, Tehran Medial University. mnoroozi@sina.tums.a.ir spetrum disorders affet in 66 births. Although EEG abities and linial seizures may play a role in ASDs, the eat frequeny of EEG abities in an ASD population that has not had linial seizures or prior ab EEGs is unknown [6]. The eletroenephalogram (EEG) is a reord of a time series of potentials aused by systemati neural ativities in brain. The measurements of the human EEG signals are performed through eletrodes plaed on the salp, and they are usually reorded on paper against time. The voltage of the EEG signal orresponds to its amplitude. The typial amplitudes of the salp EEG lie between 0 and 00 µ V, and in adults more ommonly 0 and 50 µ V [7, 8]. EEG signals involve a great amount of information about the funtion of the brain. But lassifiation and evaluation of these signals are limited. Sine there is no definite riterion evaluated by the eperts, visual analysis of EEG signals in time domain may be insuffiient. Routine linial diagnosis needs analysis of EEG signals. Therefore, some automation and omputer tehniques have been used for this aim. Sine the early days of automati EEG proessing, representations based on a Fourier transform have been most ommonly applied. This approah is based on earlier observations that the EEG spetrum ontains some harateristi waveforms that fall primarily within four frequeny bands-delta (<4HZ), theta (4-8HZ), alpha (8-HZ) and beta (- 0HZ).Suh methods have proved benefiial for various EEG haraterizations [7, 8]. Bispetrum is based on the third-order statistis whih an preserve phase information present in a signal. The phase of a signal is partiularly ritial in analyzing nonlinear systems where sinusoidal omponents of distint frequenies ould interat nonlinearly to produe one or more sinusoidal omponents at sum and differene frequenies [9-]. EEG, being generated by a nonlinear system, would be epeted to have many suh sinusoidal omponents produed due to the nonlinearity in the system. The third-order statistis, therefore, an help in identifying these omponents [].

2 Proeedings of the th WSEAS International Conferene on COMMUNICATIONS, Agios Nikolaos, Crete Island, Greee, July 6-8, Spetral analysis of the EEG signals is performed using the short-time Fourier transform (STFT), in whih the signal is divided into small sequential or overlapping data frames and fast Fourier transform (FFT) applied to eah one. The output of suessive STFTs an provide a timefrequeny representation of the signal. To aomplish this, the signal trunated into short data frames by multiplying it by a window so that the edified signal is zero outside the data frame. In order to analyze the whole signal, the window is translated in time and then reapplied to the signal []. The paper is organized as follow. In setion we eplain the seletion of patients and ontrol subjets, and the proedure for reording the EEG and seleting artifatfree epohs. Bispetrum, STFT and STFT at bandwidth in total spetrum, and their appliation used to evaluate the differenes between autism patient and ontrol subjets are also introdued in setion. Setion presents our results and ompares them in autism patients. Finally in setion 4 future works and onlusions will be presented. II. METHODS A. Seletion of patients and ontrols We studied 0 patients (9 boy and girl; age 9. ±.8 years, mean ± standard deviation (S.D.)). Patients were diagnosed as having an ASD by DSM-IV-TR riteria []. The patients were reruited from the Patient s Relatives Assoiation of Roozbeh Hospital (Tehran), where the EEG was reorded. The ontrol group onsisted 7 age-mathed, elderly ontrol subjets without past or present neurologial disorder (4 boys and girls; age 9. ± 0.7 years, mean ± S.D.). All ontrol subjets and all aregivers of the demented patients gave their informed onsent for partiipation in the urrent study. A EEG was reorded from all patients and ontrols. B. EEG reording The EEGs were reorded from the salp loi of the international 0 0 system (hannels FP, FP, F7, Fz, F4, F8, T, C, Cz, C4, T4, T5, P, PZ, P4, T6, O, O, A, A) with both earlobes hosen as ommon referee eletrodes. Reordings were made with the subjets in a relaed state and under the eyes-losed ondition in order to obtain as many artifat-free EEG data as possible. More than 0 min of data were reorded from eah subjet. Data were first proessed with a low-pass hardware filter at 00Hz, and then they were sampled at 56 Hz and digitized by a -bit analogue-digital onverter. The reordings were visually inspeted by a speialist physiian to rejet artifats. Thus, only EEG data free from eletrooulographi and movement artifats and with minimal eletromyography (EMG) ativity were seleted. Afterward, EEGs were organized in 0 s artifat-free epohs (560 points) that were opied as ASCII files for off-line analysis on a personal omputer. In order to remove the residual EMG ativity and the noise owing to the eletrial main, all seleted epohs were digitally filtered. We used a Hamming window FIR band-pass filter with ut-off frequenies at 0.5 and 48 Hz and another filter at 5 to 80 Hz designed with Matlab. C. Bispetrum, STFT and STFT-BW measure Higher-order spetra are multi-dimensional Fourier transforms of higher-order statistis. Thus, the bispetrum is defined in term of third-order umulate or thirdmoment sequene. Let (n) be a stationary, disrete, zero-mean random proess and its third-order umulant sequene (, ) will be idential to its third-moment sequene: (, ) E{ X ( k) X ( k + ) X ( k + ) } () where E {}. denote statistial epetation. The bispetrum B ( ω, ω) of X (n) is defined as the two-dimensional (D) Fourier transform of (, ) : B( ω, ω ) C ( ω, ω ) ω, ω π, (, )ep ω, ω π [ j( ω + ω )] In general, B( ω, ω) is omple and the suffiient ondition for its eistene is that (, ) is absolutely summable.also, B ( ω, ω) is periodi in ω and ω with period π, Thus B( ω, ω) is a symmetri funtion. For real proess, the bispetrum has symmetry regions. The bispetrum in the triangular region, where ( ω 0, ω ω, ω + ω π ), an ompletely desribe the whole bispetrum (as shown in fig. ). Fig. Symmetry regions of bispetrum. The bispetrum is apable of deteting and quantifying phase oupling [4]. Consequently, the bispetrum ()

3 Proeedings of the th WSEAS International Conferene on COMMUNICATIONS, Agios Nikolaos, Crete Island, Greee, July 6-8, evaluated in the triangle region (Fig. ) shows an impulse only at the point ( ω, ω) indiating that only this pair is phase oupled. If the phase oupling is totally absent, then both third-umulant sequene and bispetrum are zero. Thus, only the phase oupled omponents ontribute to the third-umulant sequene of a proess. This will make the bispetrum a useful tool for deteting whether and where the quadrati phase oupling eists and how to disriminate phase-oupling omponents [4]. Fourier analysis deomposes signal into its frequeny omponents and determines their relative strengths. the Fourier transform is defined as: F( ω) + f ( t) e jωt dt f ( t) + jωt ( ) () F ω e dω π This transform is applied to stationary signals, that is, signals whose properties do not evolve in time. When the signal is non-stationary we an introdue a loal frequeny parameter so that loal Fourier transform looks at the signal through a window over whih the signal is approimately stationary. Therefore, we applied the STFT to the EEG signals under study. The STFT positions a window funtion ψ (t) at on the time ais, and alulates the Fourier transform of the windowed signal as F( ω, ) + * f ( t) ψ ( t ) e jωt dt (4) when the window ψ (t) is a Gaussian funtion, the STFT is alled a Gabor transform. It is generated by modulation transformation of the window funtion ψ (t), where w and are modulation and translation parameters, respetively. The fied time window ψ (t) is the limitation of STFT as it auses a fied time-frequeny resolution. This is eplained by the unertainty priniple (Heisenberg inequality-meaning one an only trade time resolution for frequeny resolution, or vie versa) for the transform pair ψ ( t) Ψ( ω) t ω (5) where ω and t are the bandwidth and time spread (i.e. two pulses in time an be disriminated only if they are more than t apart) of ψ (t), respetively, and ( ) t ψ t dt t (6) ψ ( t) dt w ψ ( ω) dω ω (7) ψ ( ω) dω When t inreases, the window funtion translates in time. On the other hand, the inrease in w auses a translation in frequeny with a onstant bandwidth [5, 6]. In the STFT-BW we alulated mean of omponents STFT at bandwidth of total power spetrum. This is sign of ontributed peak of STFT in duration of time. This trend is designed with Matlab. STFT_BW has quality information of signal that we used at this study and Comparison with usual STFT at disriminations of autism and ontrol subjets. D. Statistial analysis One-way ANOVA tests were used to evaluate the statistial differenes between the estimated FFT, STFT and STFT-BW values for ASD patients and ontrol subjets. If signifiant differenes between groups were found, the ability of these analysis method to disriminate ASD patients from ontrol subjets was evaluated using reeiver operating harateristi (ROC) plots [7,8]. For lassifiation between autism and ontrol subjet s we used nearest neighbor lassifiers. It onsists of assigning a feature vetor to a lass aording to its nearest neighbor(s). This neighbor an be a feature vetor form the training set as in the ase of k nearest neighbors (KNN), or a lass prototype as in Mahalanobis distane. They are disriminative nonlinear lassifiers. Aording to the so-alled Mahalanobis distane d () [9]: T d ( ) ( µ ) M ( µ ) (8) This lead to a simple yet robust lassifier, whih even provide to be suitable for multiasts [0]. III. RESULTS AND DISCUSSION FFT, STFT and STFT-BW were estimated for hannels FP, FP, F7, Fz, F4, F8, T, C, Cz, C4, T4, T5, P, PZ, P4, T6, O, O, A, A. The results have been averaged based on all the artifat-free 0s epohs (N 560 points) within the 0-min period of EEG reordings. The Bispetrum values (mean ± S.D.) for ASD patients and ontrol subjets and the orresponding p values are summarized in Table I. The ASD patients have nt signifiantly differenes for all eletrodes. The average STFT values and standard deviations for the ASD patients and ontrol subjets for the eletrodes don t have signifiant differenes between both groups. and so the STFT-BW values (mean ± S.D.) are summarized in Table II. The ASD patients have signifiantly values ( p < 0.0) at eletrodes FP, F and T5 and with ( p < 0.05) at eletrodes F7, T and O in Table II. We evaluated the ability of the STFT-BW to disriminate ASD patients from ontrol subjets at the eletrodes in whih signifiant differenes were found using ROC plots. Table III summarizes the results. The value for the area under the ROC urve an be interpreted as follows: an area of 0.90(eletrode FP for eample) means that a randomly seleted individual from the ontrol subjet s group has a STFT-BW value larger

4 Proeedings of the th WSEAS International Conferene on COMMUNICATIONS, Agios Nikolaos, Crete Island, Greee, July 6-8, 007 than of a randomly hosen individual from the ASD patient s group in 90% of the time. A rough guide to lassify the preision of a diagnosti test is related to the area under the ROC urve. With values between 0.90 and the preision of the diagnosti test is onsidered to be eellent, good for values between 0.80 and Far fair if the results are in the range , poor when the value of the area under the ROC urve is between 0.60 and 0.69, and bad for values between 0.50 and TABLE I: THE AVERAGE BISPECTRUM VALUES OF THE EEGS FOR THE ASD PATIENTS AND CONTROL SUBJECTS FOR ALL CHANNELS Eletrode CONTROL STATISTICAL ASD patients SUBJECTS ANALYSIS (mean± S.D) (mean± S.D.) ( ρ value) FP 0.50± ± FP 0.59± ± F7 0.8± ± F 0.8± ± Fz 0.7± ± F4 0.± ± F ± ± T 0.09± ± C 0.07± ± Cz 0.± ± C4 0.0± ± T4 0.5± ± T5 0.9± ± P 0.44± ± Pz 0.40± ± P4 0.9± ± T6 0.47± ± O 0.± ± O 0.± ± A 0.0± ± A 0.9± ± It an be seen from Table III value of ROC for FP, F7, F, T, T5 and O have signifiant with STFT-BW for lassifiation. Finally, we used nearest neighbor lassifiers for lassifiation between autism and ontrol subjet s for STFT-BW at the eletrode. Using STFT-BW we obtained the highest lassifiation (8.4%). Results are summarized in Table IV. It should be noted that the used sample group is small, as a result, our findings are preliminary and require repliation in a larger patient population before any onlusion an be made of its linial diagnosti value. Moreover, the signifiant different of EEG seen in autism disorders with STFT-BW. V. CONCLUSIONS We evaluated the ability of Bispetrum and STFT to disriminate ASD patients from ontrol subjets. No signifiant disriminate were found between both groups. Using STFT-BW we obtained the highest lassifiation (8.4%) between both groups. With STFT-BW we found signifiant at FP, F and T5 with (p<0.0) and F7,T and O with (p<0.05). The results also demonstrate that there are pathophysiology differenes between hildren with autism. However suggest disorganization of ortial networks at autism patients. Diagnosis autism with quantitative EEG (qeeg) is the best of our knowledge, beause it is available and nonepensive proedures and also it s non-invasive method for hildren. TABLE I I: THE AVERAGE STFT-BW BISPECTRUM VALUES OF THE EEGS FOR THA ASD PATIENTS AND CONTROL SUBJECTS FOR ALL CHANNELS Eletrode CONTROL STATISTICAL ASD patients SUBJECTS ANALYSIS (mean± S.D) (mean± S.D.) ( ρ value) FP * 0.84± ± FP 0.60± ± F7 * 0.70± ± F * 0.85± ± Fz 0.0± ± F4 0.78± ± F8 0.80± ± T * 0.78± ± C 0.44± ± Cz 0.67± ± C4 0.± ± T4 0.5± ± T5 * 0.66± ± P 0.88± ± Pz 0.± ± P4 0.5± ± T6 0.44± ± O * 0.89± ± O 0.4± ± A 0.5± ± A 0.± ± SIGNIFICANT GROUP DIFFERENCES ARE MARKED WITH AN ASTERISK TABLE III:TEST RESULTS STFT-BW AND FFT METHODS ON CHANNELS IN WHICH THE DIFFERENCES BETWEEN BOTH GROUPS WERE SIGNIFICANT WITH ROC CURVE COMPONENT ELECTRODE AREA UNDER THE ROC CURVE STFT-BW FP F F 0.94 T 0.84 T O TABLE IV: CLASSIFICATION RESULTS WITH STFT-BW COMPONENT Cases Predited group membership total A 8.4% OF ORIGINAL GROUPED CASES CORRECTLY CLASSIFIED. Further work are needed using other EEGs analyses for having better perentage of disrimination from both groups, and autism patients.

5 Proeedings of the th WSEAS International Conferene on COMMUNICATIONS, Agios Nikolaos, Crete Island, Greee, July 6-8, 007 VI: ACKNOWLEDGEMENTS This study has been supported by psyhiatry enter whih is loated in Roozbeh hospital has been established by the department of psyhiatry Tehran University of medial sienes Referenes [] Kanner L. Autisti disturbanes of affetive ontat. Nervous Child 94; 0:7-50. [] Gubbay SS, Lobasher M, Kingerlee P. A neurologi appraisal of autisti hildren: results of western Australian survey. Dev Med Child Neurol 970; :4-9. [] Tuhman RF, Rapin I, Shinnar S. Autisti and dysphasi hildren:ii. Epilepsy. Pediatris 99; 88:9-5. [4] Tuhman RF,Rapin I, Epilepsy in autism. Lanet Neural 00; :5-8.. [5] Olsson I, Steffenburg S, Gillberg C. Epilepsy in autism and autisti like onditions. Arh Neural 988; 45; [6] M.G. Chez, Frequeny of epileptiform EEG abnor,malities in a sequential sreening of autisti patients with no known linial epilepsy from 996 to 05,ELSEVIER, Epilepsy&Behavior, [7] C. Yamaguhi, Fourier and Wavelet analyses ofmormal and epilepti eletroenephalogram (EEG),Proeedings of the st international IEEE EMBS onferene on Neural Engineering, Capri island,italy, Marh 0-,00. [8] E. Niedermeyer, Epilepti seizure disorders, Chapt. 7, in E. Niedermeyer and F.L. dasilva ed. Eletroenephalography: Basi priniples, Clinial appliation, and related fiels, 4 th ed. Lippinott Willams & Wilkins, Philadelphia (999). [9] Giannakis GB,Mendel JM. Cumulant-based order determination of NonGaussian ARMA models. IEEE Trans Aoust Speeh Signal Proess 990;8:4-. [0] Chow TWS,Tan HZ, Fei G. Third order umulant RLS algprithm for nonminimum ARMA systems identifiation. Signal Proess 997;6:-8. [] Liya Huang, Disrimination of erebral ishemi states using bispetrum analysia of EEG and artifiial neural network, Medial Engineering & Phisis 9 (007) -7. [] M. Kemal Kiymik, Comparison of STFT and wavelet transform methods in determining epilepti seizure ativity in EEG signals for real-tim appliation. Computers in Biology and Mediine 5(005) [] Amerian Psyhiatri Assoiation. Diagnosti and statistial manual of mental disorders, 4 th ed. Washington DC; 994. [4] Zhang J, Zeng C, Xie A. Bispetrum analysis of foal ishemi erebral EEG signal using third-order reursion method. IEEE Trans Biomed Eng 000;47:5-9. [5] H.O. Gulur,T. Demiralp. Analysis of EEG-EP variabilities: a parametri approah, Proeedings of the 99 International Biomedial Engineering Days (99), [6] O. Rioul, M. Vetterli, Wavelets and signal proessing, IEEE SP MAG. Otober (99) 4-8. [7] Zweig MH, Campbell G. Reeiver-operatingharateristi (ROC) plots: a fundamental evaluation tool in linial mediine, Clin Chem 99; 9: [8] D. Abasolo, Analysis of EEGbakground ativity in Alzheimer s disease patients with Lempel-Ziv ompleity and entral tendeny measure, Medial Engineering & Phsis 8 (006) 5-. [9] Cinotti F, Sipione A, Tiniperi A, Mattia D, Mariani M G, Millan J, Comparison of different feature lassifiers fo brain omputer interfaes Pro, st. IEEE EMBS onf. on Neural Engineering. [0] Shlogl A, Lee F,Bishof H and Pfurtsheller G 005, Charaterization of four-lass motor imagery EEG data for the BCI- Competitation 005 J.Neural Eng. L4-.

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