Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension

Nasrin Shoouri

DOI: 10.4103/jmss.jmss_119_21

Abstract


Background: Previous research has shown that eye movements are different in patients with attention deficit hyperactivity disorder (ADHD) and healthy people. As a result, electrooculogram (EOG) signals may also differ between the two groups. Therefore, the aim of this study was to investigate the recorded EOG signals of 30 ADHD children and 30 healthy children (control group) while performing an attention-related task. Methods: Two features of approximate entropy (ApEn) and Petrosian's fractal dimension (Pet's FD) of EOG signals were calculated for the two groups. Then, the two groups were classified using the vector derived from two features and two support vector machine (SVM) and neural gas (NG) classifiers. Results: Statistical analysis showed that the values of both features were significantly lower in the ADHD group compared to the control group. Moreover, the SVM classifier (accuracy: 84.6% ± 4.4%, sensitivity: 85.2% ± 4.9%, specificity: 78.8% ± 6.5%) was more successful in separating the two groups than the NG (78.1% ± 1.1%, sensitivity: 80.1% ± 6.2%, specificity: 72.2% ± 9.2%). Conclusion: The decrease in ApEn and Pet's FD values in the EOG signals of the ADHD group showed that their eye movements were slower than the control group and this difference was due to their attention deficit. The results of this study can be used to design an EOG biofeedback training course to reduce the symptoms of ADHD patients.

Keywords


Approximate entropy, attention deficit hyperactivity disorder, electrooculogram, neural gas, Petrosian's fractal dimension, support vector machine

Full Text:

PDF

References


Küpper T, Haavik J, Drexler H, Ramos-Quiroga JA, Wermelskirchen D, Prutz C, et al. The negative impact of attention-deficit/hyperactivity disorder on occupational health in adults and adolescents. Int Arch Occup Environ Health 2012;85:837-47.

Reinhardt MC, Reinhardt CA. Attention deficit-hyperactivity disorder, comorbidities, and risk situations. J Pediatr (Rio J) 2013;89:124-30.

Strahler Rivero T, Herrera Nuñez LM, Uehara Pires E, Amodeo Bueno OF. ADHD rehabilitation through video gaming: A systematic review using PRISMA guidelines of the current findings and the associated risk of bias. Front Psychiatry 2015;6:151.

Tajik-Parvinchi D, Wright L, Schachar R. Cognitive rehabilitation for Attention Deficit/Hyperactivity Disorder (ADHD): Promises and problems. J Can Acad Child Adolesc Psychiatry 2014;23:207-17.

Gholami R, Esteki M, Nosratabadi M. Relationship between IVA measures and QEEG pattern in children with attention-deficit/hyperactivity disorder. J Neuropsychol 2017;3:25-8.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). 5th ed. American Psychiatric Pub. 2013.

Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett 2016;6:66-73.

Abibullaev B, An J. Decision support algorithm for diagnosis of ADHD using electroencephalograms. J Med Syst 2012;36:2675-88.

Ghassemi F, Hassan-Moradi M, Tehrani-Doost M, Abootalebi V. Using non-linear features of EEG for ADHD/normal participants' classification. Procedia Soc Behav Sci 2012;32:148-52.

Marcano JL, Bell MA, Beex AA. Classification of ADHD and Non-ADHD subjects using a universal background model. Biomed Signal Process Control 2018;39:204-12.

Lubar JF. Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback Self Regul 1991;16:201-25.

Jalali P, Sho'ouri N. Neurofeedback training protocol based on selecting distinctive features to treat or reduce ADHD symptoms. Clin EEG Neurosci 2021;52:414-21.

Ayoubipour S, Hekmati H, Sho'ouri N, editors. Analysis of EOG Signals Related to ADHD and Healthy Children Using Wavelet Transform. 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), IEEE; 2020.

Sho'ouri N. Diagnosis of attention deficit hyperactivity disorder using detrended fluctuation analysis of EOG signal. Iran J Biomed Eng 2020;14:161-70.

Mahone EM, Mostofsky SH, Lasker AG, Zee D, Denckla MB. Oculomotor anomalies in attention-deficit/hyperactivity disorder: Evidence for deficits in response preparation and inhibition. J Am Acad Child Adolesc Psychiatry 2009;48:749-56.

Gargouri-Berrechid A, Lanouar L, Kacem I, Ben Djebara M, Hizem Y, Zaouchi N, et al. Eye movement recordings in children with attention deficit hyperactivity disorder. J Fr Ophtalmol 2012;35:503-7.

Solé Puig M, Pérez Zapata L, Puigcerver L, Esperalba Iglesias N, Sanchez Garcia C, Romeo A, et al. Attention-related eye vergence measured in children with attention deficit hyperactivity disorder. PLoS One 2015;10:e0145281.

Fried M, Tsitsiashvili E, Bonneh YS, Sterkin A, Wygnanski-Jaffe T, Epstein T, et al. ADHD subjects fail to suppress eye blinks and microsaccades while anticipating visual stimuli but recover with medication. Vision Res 2014;101:62-72.

Sho'ouri N. EOG biofeedback protocol based on selecting effective features to treat or reduce ADHD symptoms. Biomed Signal Process Control 2021;70:102748.

Munoz DP, Armstrong IT, Hampton KA, Moore KD. Altered control of visual fixation and saccadic eye movements in attention-deficit hyperactivity disorder. J Neurophysiol 2003;90:503-14.

Hanisch C, Radach R, Holtkamp K, Herpertz-Dahlmann B, Konrad K. Oculomotor inhibition in children with and without attention-deficit hyperactivity disorder (ADHD). J Neural Transm (Vienna) 2006;113:671-84.

Granet DB, Gomi CF, Ventura R, Miller-Scholte A. The relationship between convergence insufficiency and ADHD. Strabismus 2005;13:163-8.

Türkan BN, Amado S, Ercan ES, Perçinel I. Comparison of change detection performance and visual search patterns among children with/without ADHD: Evidence from eye movements. Res Dev Disabil 2016;49-50:205-15.

Vakil E, Mass M, Schiff R. Eye movement performance on the stroop test in adults with ADHD. J Atten Disord 2019;23:1160-9.

Dankner Y, Shalev L, Carrasco M, Yuval-Greenberg S. Prestimulus inhibition of saccades in adults with and without attention-deficit/hyperactivity disorder as an index of temporal expectations. Psychol Sci 2017;28:835-50.

Solé Puig M, Pérez Zapata L, Aznar-Casanova JA, Supèr H. A role of eye vergence in covert attention. PLoS One 2013;8:e52955.

Latifo?lu F, Esas MY, Demirci E. Diagnosis of attention-deficit hyperactivity disorder using EOG signals: A new approach. Biomed Tech (Berl) 2020;65:149-64.

Wang H, Wu C, Li T, He Y, Chen P, Bezerianos A. Driving fatigue classification based on fusion entropy analysis combining EOG and EEG. IEEE Access 2019;7:61975-86.

Pincus SM, Keefe DL. Quantification of hormone pulsatility via an approximate entropy algorithm. Am J Physiol 1992;262:E741-54.

Mandelbrot BB. The Fractal Geometry of Nature. Macmillan; 1983.

Taghavi M, Boostani R, Sabeti M, Taghavi SM. Usefulness of approximate entropy in the diagnosis of schizophrenia. Iran J Psychiatry Behav Sci 2011;5:62-70.

Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med 2009;47:263-74.

Sho'ouri N, Firoozabadi M, Badie K. Neurofeedback training protocols based on selecting distinctive features and identifying appropriate channels to enhance performance in novice visual artists. Biomed Signal Process Control 2019;49:308-21.

Shourie N, Firoozabadi M, Badie K. Analysis of EEG signals related to artists and nonartists during visual perception, mental imagery, and rest using approximate entropy. Biomed Res Int 2014;2014:764382.

Shourie N. Cepstral analysis of EEG during visual perception and mental imagery reveals the influence of artistic expertise. J Med Signals Sens 2016;6:203-17.

Shourie N, Firoozabadi SM, Badie K. A comparative investigation of wavelet families for analysis of EEG signals related to artists and nonartists during visual perception, mental imagery, and rest. J Neurother 2013;17:248-57.

Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:H2039-49.

Petrosian A. Kolmogorov Complexity of Finite Sequences and Recognition of Different Preictal EEG Patterns. IEEE Symposium on Computer-Based Medical Systems. Texas: IEEE; 1995. p. 212-7.

Martinetz TM, Schulten KJ. A neural gas network learns topologies. Artificial Neural Networks. 1991. p. 397-402.

Shourie N, Firoozabadi SM, Badie K, editors. Information Evaluation and Classification of Scaling Exponents of EEG Signals Corresponding to Visual Perception, Mental Imagery & Mental Rest for Artists and Non-Artists. Biomedical Engineering (ICBME), 2011 18th Iranian Conference of; 2011: IEEE; 2011.

Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97.

Boser BE, Guyon IM, Vapnik VN, editors. A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning theory. ACM; 1992.

Accardo A, Affinito M, Carrozzi M, Bouquet F. Use of the fractal dimension for the analysis of electroencephalographic time series. Biol Cybern 1997;77:339-50.

Gómez-Herrero G, De Clercq W, Anwar H, Kara O, Egiazarian K, Van Huffel S, et al., editors. Automatic Removal of Ocular Artifacts in the EEG Without an EOG Reference Channel. Proceedings of the 7th Nordic Signal Processing Symposium-NORSIG 2006. IEEE; 2006.

Bulling A, Ward JA, Gellersen H, Tröster G. Eye movement analysis for activity recognition using electrooculography. IEEE Trans Pattern Anal Mach Intell 2011;33:741-53.

Schmidt J, Laarousi R, Stolzmann W, Karrer-Gauß K. Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera. Behav Res Methods 2018;50:1088-101.

Zhu X, Zheng WL, Lu BL, Chen X, Chen S, Wang C, editors. EOG-Based Drowsiness Detection Using Convolutional Neural Networks. 2014 International Joint Conference on Neural Networks (IJCNN). IEEE; 2014.

Barth B, Mayer K, Strehl U, Fallgatter AJ, Ehlis AC. EMG biofeedback training in adult attention-deficit/hyperactivity disorder: An active (control) training? Behav Brain Res 2017;329:58-66.

Steiner NJ, Frenette EC, Rene KM, Brennan RT, Perrin EC. Neurofeedback and cognitive attention training for children with attention-deficit hyperactivity disorder in schools. J Dev Behav Pediatr 2014;35:18-27.

Vernon D, Frick A, Gruzelier J. Neurofeedback as a treatment for ADHD: A methodological review with implications for future research. J Neurother 2004;8:53-82.

Hadavi H, Sho'ouri N, editors. Soft Boundary-Based Neurofeedback Training Procedure: A Method to Control EEG Signal Features during Neurofeedback Training Using Fuzzy Similarity Measures. 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME). IEEE; 2019.

Sho'ouri N. Soft boundary-based neurofeedback training based on fuzzy similarity measures: A method for learning how to control EEG Signal features during neurofeedback training. J Neurosci Methods 2020;343:108805.

Sho'ouri N. A new neurofeedback training method based on feature space clustering to control EEG features within target clusters. J Neurosci Methods 2021;362:109304.

Sho'ouri N. Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects within Target Areas during Neurofeedback Training, Clinical EEG and Neuroscience; 2021.


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477