A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method

• Autorzy: Abdolzadegan Donya , Moattar Mohammad Hossein , Ghoshuni Majid

Identyfikatory

DOI

10.1016/j.bbe.2020.01.008

• Języki publikacji: EN

Abstrakty

EN

Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism Spectrum Disorder (ASD). There are different challenges such as feature selection and the existence of artifacts in EEG signals. This article aims to present a robust method for early diagnosis of ASD from EEG signal. The study population consists of 34 children with ASD between 3–12 years and 11 healthy children in the same ranges of age. The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal. In addition Density Based Clustering is utilized for artifact removal and robustness. Besides, features selection is applied based on different criterions such as Mutual Information (MI), Information Gain (IG), Minimum-Redundancy Maximum-Relevancy (mRmR) and Genetic Algorithm (GA). Finally, the K-Nearest-Neighbor (KNN) and Support Vector Machines (SVM) classifiers are used for final decision. As a result, the investigation indicates that the classification accuracy of the approach using SVM is 90.57% while for KNN it is 72.77%. Moreover, the sensitivity of the proposed method is 99.91% for SVM and 91.96% for KNN. Also, experiments show that DFA, LE, Entropy and SL features have considerable influence in promoting the classification accuracy.

Słowa kluczowe

EN

electroencephalography; autism spectrum disorder; feature extraction; correlation dimension; Lyapunov exponent; density based clustering; feature selection

PL

elektroencefalografia; spektrum zaburzeń autystycznych; ekstrakcja cech; wymiar korelacyjny; wykładnik Lapunowa; selekcja cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 482--493
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Abdolzadegan Donya

• Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

autor

Moattar Mohammad Hossein

• Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

autor

Ghoshuni Majid

• Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

Bibliografia

• [1] Laske C, et al. Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimer's & Dementia 2014;1–18.
• [2] Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer's disease from EEG signals: where are we standing? Curr Alzheimer Res 2010;7:487–505.
• [3] Pritchard WS, Duke DW, Coburn KL, Moore NC. EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures. Electroencephalogr Clin Neurophysiol 1994;91:118–30.
• [4] Tsuno N, Shigeta M, Hyoki K, Faber PL, Lehmann D. Fluctuations of source locations of EEG activity during transition from alertness to sleep in Alzheimer's disease and vascular dementia. Neuropsychobiology 2004;50:267–72.
• [5] Steriade M. Grouping of brain rhythms in corticothalamic systems. Neuroscience 2006;137:1087–106.
• [6] Anderer P, Saletu B, Klöppel B, Semlitsch HV, Werner H. Discrimination between demented patients and normals based on topographic EEG slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers. Electroencephalogr Clin Neurophysiol 1994;91:108–17.
• [7] Besthorn C, Zerfass R, Geiger-Kabisch C, Sattel H, Daniel S, Schreiter-Gasser U, et al. Discrimination of Alzheimer's disease and normal aging by EEG data. Electroencephalogr Clin Neurophysiol 1997;103:241–8.
• [8] Coben R, Clarke AR, Hudspeth W, Barry RJ. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol 2008;119:1002–9.
• [9] Schwartz S, Kessler R, Gaughan T, Buckley AW. Electroencephalogram coherence patterns in autism: an updated review. Pediatr Neurol 2017;67:7–22.
• [10] Grossi E, Olivieri C, Buscema M. Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study. Comput Methods Programs Biomed 2017;142:73–9.
• [11] Grossi E, Buscema M, Della Torre F, Swatzyna R. The "MS-ROM/IFAST" model, a novel parallel nonlinear EEG analysis technique, distinguishes ASD subjects from children affected with other neuropsychiatric disorders with high degree of accuracy. Clin EEG Neurosci 2019;50(5):319–31.
• [12] Sheikhani A, Behnam H, Mohammadi MR, Noroozian M, Golabi P. ‘‘Analysis of quantitative electroencephalogram background activity in Autism disease patients with Lempel-Ziv complexity and short time fourier transform measure,’’ in Proceedings of the 4th IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2007; 2007;111–4.
• [13] Nasehi S, Pourghassem H. Seizure detection algorithms based on analysis of EEG and ECG signals: A survey. Neurophysiology 2012;44(2):174–86.
• [14] Ahmadlou M, Adeli H, Adeli A. Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J Clin Neurophysiol 2010;27(5):328–33.
• [15] Ahmadlou M, Adeli H, Adeli A. Fuzzy Synchronization Likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J Neurosci Methods 2012;211(2):203–9.
• [16] Loo CK, Samraj A, Lee GC. Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface;vol. 2011, pp. 1–9, 2011.
• [17] Alhaddad MJ, et al. Diagnosis autism by fisher linear discriminant analysis FLDA via EEG. International Journal of Bio-Science and Bio-Technology 2012;4(2):45–54.
• [18] Razali N, Wahab A. 2D Affective Space Model ( ASM ) for detecting autistic children; 2011;536–41.
• [19] Duffy FH, Als H. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study. BMC Med 2012;10:64.
• [20] Ibrahim S, Djemal R, Alsuwailem A. Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern Biomed Eng 2018;38(1):16–26.
• [21] Djemal R, Alsharabi K, Ibrahim S, Alsuwailem A. EEG-Based computer aided diagnosis of autism spectrum disorder using wavelet, entropy, and ANN. Biomed Res Int 2017;2017.
• [22] Behnam H, Sheikhani A, Mohammadi MR, Noroozian M, Golabi P. ‘‘Abnormalities in connectivity of quantitative electroencephalogram background activity in autism disorders especially in left hemisphere and right temporal,’’ in Proceedings - UKSim 10th International Conference on Computer Modelling and Simulation, EUROSIM/UKSim2008; 2008;82–7.
• [23] Alsaggaf EA, Kamel M. Using EEGs to diagnose autism disorder by classification algorithm. Life Sci J 2014;11 (6):305–8.
• [24] Chan AS, et al. Executive function deficits and neural discordance in children with Autism Spectrum disorders. Clin Neurophysiol 2009;120(6):1107–15.
• [25] Sheikhani A, Behnam H, Noroozian M, Mohammadi MR, Mohammadi M. Abnormalities of quantitative electroencephalography in children with Asperger disorder in various conditions. Res Autism Spectr Disord 2009;3 (2):538–46.
• [26] Thapaliya S, Jayarathna S, Jaime M. ‘‘Evaluating the EEG and eye movements for autism Spectrum disorder,’’ in proceedings - 2018 IEEE international conference on big data, big data 2018; 2019.
• [27] Bosl WJ, Tager-Flusberg H, Nelson CA. EEG analytics for early detection of autism Spectrum disorder: a data-driven approach. Sci Rep 2018.
• [28] Buscema M, Rossini P, Babiloni C, Grossi E. The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy. Artif Intell Med 2007;40:127–41.
• [29] Yoon J, Schaar vander. INVASE: instance-wise variable selection using neural networks. ICLR; 2019.
• [30] Buscema M, Breda M, Weldon. Training with input selection and testing (TWIST) algorithm: a significant advance in pattern recognition performance of machine learning. J Intell Learn Syst Appl 2013;5:29–38.
• [31] Bosl W, Tierney A, Tager-Flusberg H, Nelson C. EEG complexity as a biomarker for autism spectrum disorder risk.. BMC Med 2011;9:18.
• [32] Ahmadlou M, Adeli H, Adeli A. Fractality and a Wavelet- Chaos-Neural Network Methodology for EEG-Based Diagnosis of Autistic Spectrum Disorder. J Clin Neurophysiol 2010;27(5):328–33.
• [33] Hashemian M, Pourghassem H. Diagnosing autism Spectrum disorders based on EEG analysis: a survey. Springer Sci Media New York Neurophysiol 2014;14(2).
• [34] Duffy FH, Als H. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro- typical controls - a large case control study A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large ca;vol. 64, no. June 2012.
• [35] Rodríguez-Bermúdez G, García-Laencina PJ. Analysis of EEG signals using nonlinear dynamics and Chaos : a review. Appl Math Inf Sci Lett 2015;2321(5):2309–21.
• [36] Derya E. Statistics over lyapunov exponents for feature extraction : electroencephalographic changes detection case; 2005;132–5.
• [37] Mantri S, Chavan P, Kadam P, Patil D, Wadhai VM. Classifying mood disordered patients and normal subjects using various machine learning techniques. Int J 2013;1 (4):90–7.
• [38] Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Second Int Conf Knowl Discov Data Min 1996;226–31.
• [39] Han J, Kamber M, Pei J. Data mining; 2012.
• [40] Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng 2014;40(1):16–28.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).