استخراج اطلاعات تفاضلی سیگنال‌های الکتروانسفالوگرام جهت تشخیص اختلال وسواس اجباری
محورهای موضوعی : سامانه های پردازشی و ارتباطی چند رسانه ای هوشمند
فرزانه منظری
1
(دانشجوی کارشناسی ارشد، دانشکده‌ی مهندسی پزشکی، دانشگاه صنعتی سهند تبریز)
پیوند قادریان
2
(دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران)
کلید واژه: ویژگی‌های آماری تفاضلی, اختلال وسواس اجباری, تجزیه به مؤلفه‌های مد ذاتی, ماشین بردار پشتیبان,
چکیده مقاله :
اختلال وسواس اجباری یک بیماری مزمن ذهنی و اجتماعی است که در بین حدود 2 تا 3 درصد جمعیت انسان‌ها شیوع دارد و سبب مشکلاتی در عملکردهای شناختی و افت کیفیت زندگی مبتلایان می‌گردد. به همین علت تشخیص صحیح و به هنگام آن می‌تواند به روان پزشکان در درمان و یا کنترل این بیماری کمک کند. تحقیقات پیشین در زمینه‌ی بیماری وسواس اجباری نشان‌دهنده‌ی اختلال در فعالیت الکتریکی بین نواحی مختلف مغزی بوده‌اند. بنابراین، در این مطالعه رویکردی جدید در زمینه‌ی تشخیص اختلال وسواس اجباری ارائه شده است که مبتنی بر تجزیه سیگنال به توابع پایه‌ای ذاتی و استخراج تغییرات لحظه‌ای دامنه و فاز سیگنال الکتروانسفالوگرام به صورت تفاضلی در حین انجام تکالیف فلانکر می‌باشد. ارزیابی الگوریتم پیشنهادی با داده‌های الکتروانسفالوگرام 19 فرد سالم و 11 بیمار دارای اختلال وسواس اجباری توسط طبقه‌بند ماشین بردار پشتیبان صورت گرفته است. نتایج به دست آمده، قابلیت روش پیشنهادی در تشخیص بیماری را با صحت بالای 89/93 درصد با استفاده از اطلاعات تفاضلی دامنه‌ی سیگنال الکتروانسفالوگرام نشان داده‌اند. در مقایسه بین نواحی مختلف ویژگی‌های آماری مستخرج از لوب پیشانی، شبکه‌ی پیشانی-آهیانه و نیم‌کره‌ی مغزی کارایی بیشتری در تشخیص بیماری ارائه داده‌اند.
Introduction: Obsessive-Compulsive Disorder (OCD) is a chronic mental and social disease that is prevalent in about 2 to 3% of the human population leading to cognitive impairments and affected quality of patient's life. Therefore, a reliable and timely diagnosis can help psychiatrists in better treating or controlling this disease.Method: Previous studies have demonstrated interdependence impairments between different brain regions in patients with OCD. Hence, this study has provided a new approach based on the decomposition of signals into intrinsic components and extraction of differential transient changes in amplitude envelope and phase spectra of the EEG signal recorded during Flanker tasks. The proposed algorithm has been evaluated using 19 healthy subjects and 11 patients by the Support Vector Machine (SVM) classifier.Result: The obtained results have confirmed the capability of the proposed method in diagnosing the disease with high accuracy of 93.89% using amplitude differential information of the electroencephalogram signal.Conclusion: In comparison between different regions, the statistical features extracted from the frontal lobe, the frontal-parietal network, and the inter-hemispheric features have offered better detection ability.
[2] S. Ferreira, J. M. Pego, and P. Morgado, "The efficacy of biofeedback approaches for obsessive-compulsive and related disorders: A systematic review and meta-analysis," Psychiatry Research, vol. 272, pp. 237-245, 2019.
[3] T. O. Gründler, J. F. Cavanagh, C. M. Figueroa, M. J. Frank, and J. J. Allen, "Task-related dissociation in ERN amplitude as a function of obsessive–compulsive symptoms," Neuropsychologia, vol. 47, no. 8-9, pp. 1978-1987, 2009.
[4] F. Karadag, N. K. Oguzhanoglu, T. KURT, A. Oguzhanoglu, F. Atesci, and O. ÖZDEL, "Quantitative EEG analysis in obsessive compulsive disorder," International journal of neuroscience, vol. 113, no. 6, pp. 833-847, 2003.
[5] M. P. N. Perera, N. W. Bailey, S. E. Herring, and P. B. Fitzgerald, "Electrophysiology of obsessive compulsive disorder: a systematic review of the electroencephalographic literature," Journal of Anxiety Disorders, vol. 62, pp. 1-14, 2019.
[6] A. Naro et al., "Theta burst stimulation for the treatment of obsessive–compulsive disorder: a pilot study," Journal of Neural Transmission, vol. 126, no. 12, pp. 1667-1677, 2019.
[7] E. E. Smith et al., "A brief demonstration of frontostriatal connectivity in OCD patients with intracranial electrodes," NeuroImage, p. 117138, 2020.
[8] X. Ma, Y. Huang, L. Liao, and Y. Jin, "A randomized double-blinded sham-controlled trial of α electroencephalogram-guided transcranial magnetic stimulation for obsessive-compulsive disorder," Chinese medical journal, vol. 127, no. 4, pp. 601-606, 2014.
[9] S. Aydın and O. Tan, "Classification of band-specific regional hemispheric connectivity in obsessive compulsive disorder," in 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017: IEEE, pp. 1-4.
[10] U. Hegerl et al., "EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls," European Archives of Psychiatry and Clinical Neuroscience, vol. 258, no. 3, pp. 137-143, 2008.
[11] S. Aydin, N. Arica, E. Ergul, and O. Tan, "Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements," International journal of neural systems, vol. 25, no. 03, p. 1550010, 2015.
[12] G. Chandel, O. Farooq, M. H. N. Shaikh, and P. M. Shanir, "Seizure detection in neonatal EEG signals using EMD based features," in 2017 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), 2017: IEEE, pp. 89-93.
[13] P. Ghaderyan and A. Abbasi, "Dynamic Hilbert warping, a new measure of RR-interval signals evaluated in the cognitive load estimation," Medical engineering & physics, vol. 40, pp. 103-109, 2017.
[14] M. Chaumon, D. V. Bishop, and N. A. Busch, "A practical guide to the selection of independent components of the electroencephalogram for artifact correction," Journal of neuroscience methods, vol. 250, pp. 47-63, 2015.
[15] P. Ghaderyan and A. Abbasi, "An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations," International Journal of Psychophysiology, vol. 110, pp. 91-101, 2016.
[16] A. Hamad, E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Feature extraction of epilepsy EEG using discrete wavelet transform," in 2016 12th international computer engineering conference (ICENCO), 2016: IEEE, pp. 190-195.
[17] S. M. G. Beyrami and P. Ghaderyan, "A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis," Measurement, vol. 156, p. 107579, 2020.
[18] W. S. Noble, "What is a support vector machine?," Nature biotechnology, vol. 24, no. 12, pp. 1565-1567, 2006.
[19] D. A. Pisner and D. M. Schnyer, "Support vector machine," in Machine Learning: Elsevier, 2020, pp. 101-121.
[21] Ş. Tot, A. Özge, Ü. Çömelekoğlu, K. Yazici, and N. Bal, "Association of QEEG findings with clinical characteristics of OCD: evidence of left frontotemporal dysfunction," The Canadian Journal of Psychiatry, vol. 47, no. 6, pp. 538-545, 2002.
[1] B. Tan, Q. Liu, C. Wan, Z. Jin, Y. Yang, and L. Li, "Altered functional connectivity of alpha rhythm in obsessive-compulsive disorder during rest," Clinical EEG and neuroscience, vol. 50, no. 2, pp. 88-99, 2019.
[2] S. Ferreira, J. M. Pego, and P. Morgado, "The efficacy of biofeedback approaches for obsessive-compulsive and related disorders: A systematic review and meta-analysis," Psychiatry Research, vol. 272, pp. 237-245, 2019.
[3] T. O. Gründler, J. F. Cavanagh, C. M. Figueroa, M. J. Frank, and J. J. Allen, "Task-related dissociation in ERN amplitude as a function of obsessive–compulsive symptoms," Neuropsychologia, vol. 47, no. 8-9, pp. 1978-1987, 2009.
[4] F. Karadag, N. K. Oguzhanoglu, T. KURT, A. Oguzhanoglu, F. Atesci, and O. ÖZDEL, "Quantitative EEG analysis in obsessive compulsive disorder," International journal of neuroscience, vol. 113, no. 6, pp. 833-847, 2003.
[5] M. P. N. Perera, N. W. Bailey, S. E. Herring, and P. B. Fitzgerald, "Electrophysiology of obsessive compulsive disorder: a systematic review of the electroencephalographic literature," Journal of Anxiety Disorders, vol. 62, pp. 1-14, 2019.
[6] A. Naro et al., "Theta burst stimulation for the treatment of obsessive–compulsive disorder: a pilot study," Journal of Neural Transmission, vol. 126, no. 12, pp. 1667-1677, 2019.
[7] E. E. Smith et al., "A brief demonstration of frontostriatal connectivity in OCD patients with intracranial electrodes," NeuroImage, p. 117138, 2020.
[8] X. Ma, Y. Huang, L. Liao, and Y. Jin, "A randomized double-blinded sham-controlled trial of α electroencephalogram-guided transcranial magnetic stimulation for obsessive-compulsive disorder," Chinese medical journal, vol. 127, no. 4, pp. 601-606, 2014.
[9] S. Aydın and O. Tan, "Classification of band-specific regional hemispheric connectivity in obsessive compulsive disorder," in 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017: IEEE, pp. 1-4.
[10] U. Hegerl et al., "EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls," European Archives of Psychiatry and Clinical Neuroscience, vol. 258, no. 3, pp. 137-143, 2008.
[11] S. Aydin, N. Arica, E. Ergul, and O. Tan, "Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements," International journal of neural systems, vol. 25, no. 03, p. 1550010, 2015.
[12] G. Chandel, O. Farooq, M. H. N. Shaikh, and P. M. Shanir, "Seizure detection in neonatal EEG signals using EMD based features," in 2017 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), 2017: IEEE, pp. 89-93.
[13] P. Ghaderyan and A. Abbasi, "Dynamic Hilbert warping, a new measure of RR-interval signals evaluated in the cognitive load estimation," Medical engineering & physics, vol. 40, pp. 103-109, 2017.
[14] M. Chaumon, D. V. Bishop, and N. A. Busch, "A practical guide to the selection of independent components of the electroencephalogram for artifact correction," Journal of neuroscience methods, vol. 250, pp. 47-63, 2015.
[15] P. Ghaderyan and A. Abbasi, "An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations," International Journal of Psychophysiology, vol. 110, pp. 91-101, 2016.
[16] A. Hamad, E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Feature extraction of epilepsy EEG using discrete wavelet transform," in 2016 12th international computer engineering conference (ICENCO), 2016: IEEE, pp. 190-195.
[17] S. M. G. Beyrami and P. Ghaderyan, "A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis," Measurement, vol. 156, p. 107579, 2020.
[18] W. S. Noble, "What is a support vector machine?," Nature biotechnology, vol. 24, no. 12, pp. 1565-1567, 2006.
[19] D. A. Pisner and D. M. Schnyer, "Support vector machine," in Machine Learning: Elsevier, 2020, pp. 101-121.
[20] P. Ghaderyan, A. Abbasi, and M. H. Sedaaghi, "An efficient seizure prediction method using KNN-based undersampling and linear frequency measures," Journal of neuroscience methods, vol. 232, pp. 134-142, 2014.
[21] Ş. Tot, A. Özge, Ü. Çömelekoğlu, K. Yazici, and N. Bal, "Association of QEEG findings with clinical characteristics of OCD: evidence of left frontotemporal dysfunction," The Canadian Journal of Psychiatry, vol. 47, no. 6, pp. 538-545, 2002.
[22] S. Yazdi-Ravandi et al., "Differential pattern of brain functional connectome in obsessive-compulsive disorder versus healthy controls," EXCLI journal, vol. 17, p. 1090, 2018.
[23] P. Desarkar, V. K. Sinha, K. Jagadheesan, and S. H. Nizamie, "Subcortical functioning in obsessive-compulsive disorder: an exploratory EEG coherence study," The World Journal of Biological Psychiatry, vol. 8, no. 3, pp. 196-200, 2007.
[24] S. Olbrich, H. Olbrich, M. Adamaszek, I. Jahn, U. Hegerl, and K. Stengler, "Altered EEG lagged coherence during rest in obsessive–compulsive disorder," Clinical Neurophysiology, vol. 124, no. 12, pp. 2421-2430, 2013.
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