利用用正则化Fisher线性判别分析脑电信号诊断自闭症(IJIGSP-V4-N3-6)

Facul lty of Comput ting and Infor rmation Techn nology
King Abd dulaziz Uniቤተ መጻሕፍቲ ባይዱ versity KAU, , Jeddah, Sau udi Arabia
abit, fdehlawi (miali, hmalibary, h Ma alhaddad, drtha i, eaalsaggaf, ahadi008@ka a .sa)
I.J. Im mage, Graphic cs and Signal P Processing, 2012, 2 3, 35-41
36
EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis
analysis in detecting the autistic children based on EEG signal analysis. Thus, optimum preprocessing -which gives the highest classification accuracy- is studied. The artifacts of the recorded EEG signals were removed by visual inspection. Then, different preprocessing techniques were applied such as Re-referencing, Filtering, Winsorizing, Scaling, Single epoch extraction and Feature vector construction. After preprocessing, FFT was used as features. Dimensionality reduction using decimation factor 2 was applied. Finally, the extracted features were classified using RFLD. This research is considered as part of the main BCI project in the King AbdulAziz University that is funded by (King AbdulAziz City for Science and Technology) KACST, 8-NAN106-3. The layout of the paper is as follows. Section 2 focuses on the literature review, the experiments that were performed and the methods used for data preprocessing, feature extraction are described in section 3. Classification is given in section 4. Results are discussed in section 5.
Index Term ms—
Electro oencephalogra am, R Regularized
Automa ated lin near
sys stem perform mance and be etter predictio ons, but they y req quire a priori information on the syste em and often n nee ed more time and resources s [4]. a increasing g In recent years, there has been an inte erest in apply ying machine e learning me ethods to the e
aut tomated detec ction of autism m EEG signal ls [5, 6]. EEG G sig gnals analysis based on mac chine learning g methods has s three main steps s: preprocessi ing, feature ex xtraction, and d cla assification. is paper is to t utilize the e The major goal of thi Regularized isher’s Fi Line ear Discriminat (RFLD) )
I.J. Image, Graphics an nd Signal Pr rocessing, 20 012, 3, 35-41 1
Published On nline April 20 012 in MECS (http://www.m ( g/) DOI: 10.5815/ijigsp.2012. .03.06
EEG G based d Auti ism Diagnosis Us sing R Regula arized Fish her Lin near D Discrim minant t Anal lysis
Mahmoud I. Kamel , Mohammed M J. J Alhaddad, Hussein M. Malibary, Khalid K Thabit t, Foud Dahlw wi, Ebtehal A. Alsa aggaf, Anas A. A Hadi
diagnosis,
Autism,
Fisher’s
st Fourier Tran nsform. discriminant analysis, Fas
I. INTRODU UCTION Autism is a disorder rather than an n organic dise ease is of autism is s one of the difficult d proble ems and diagnosi facing resear rchers and th hose interested d in the field d of signal proces ssing and med dicine. Theref fore, there is a lot Copyright © 2012 MECS
problems
facing
rese earchers.
To o
reveal
the
ve pattern between b autis stic and nor rmal discriminativ children via electroenceph halogram (EE EG) analysis is a big challeng ge. The featur re extraction is averaged F Fast Fourier Tran nsform (FFT) with the Regulated R Fis sher Linear Discri iminant (RFL LD) classifier. Gaussinaty c condition for the optimali ity of Regula ated Fisher Linea ar Discriminan nt (RFLD) ha as been achie eved by a well-co onditioned app propriate prep processing of f the data, as wel ll as optimal shrinkage te echnique for the Lambda para ameter. Wins sorised Filtere ed Data gave the best result.
Abstract— D Diagnosis of autism is one e of the diffi icult
ng on around the world to oday trying to o of research goin use e neuroscienc ce such as EEG study y to identify y ind dividuals with h autism. Hen nce, a need for f automatic c det tection of EE EG signals h has been soug ght by many y res searchers to diagnose d auti istic people. Furthermore, , the ey report diff ferent findings t discriminat t s regarding to pat tterns between n normal and a autism disorders [1, 2]. Many caus ses of autism m have been proposed, p but t und derstanding of the theory o of causation of o autism and d the e other autism spectrum diso orders is incomplete [3]. In n this s case, the phenomenol logical mode els are most t app propriate to be b applied tha an the mechan nistic models. . Me echanistic models m typic cally involve e physically y inte erpretable pa arameters, allo ow deeper insights into o