Beyin osilasyonlarına ilişkin enerji değişimlerinin dalgacık dönüşümü temelli yeni bir yaklaşım ile saptanması ve haritalanması: Epilepsi örneği

Year 2020, Volume: 26 Issue: 2, 328 - 334, 07.04.2020

Evren Değirmenci , Okan Yalçın Özge Çekirge Cansu Gelgeç Zülal Kızılaslan Ülkü Çömelekoğlu

Abstract

Beyin osilasyonlarının nicel analizi, başta epilepsi olmak üzere çeşitli beyin patolojilerinde beyin dinamiklerinin anlaşılmasına katkıda bulunur. Bu çalışmada dalgacık dönüşümü temelli yeni bir nicel yaklaşım kullanılarak beyin osilasyonlarının enerji değişimlerinin saptanması ve haritalanması amaçlanmıştır. Oluşturulan sinyallerin analizinde EEG’yi dört temel alt frekans bandına ayrıştıran ve ardından bu bantların enerji değerlerini nicelleştiren bir yaklaşım önerilmiştir. Ayrıca, bu enerji değerleri kullanılarak kafa yüzeyi enerji haritaları oluşturulmuştur. Önerilen yöntemi test etmek için CHB-MIT Scalp EEG Database veri tabanından alınan epilepsili hastalara ait EEG kayıtları kullanılmıştır. Analiz edilecek sinyaller nöbet anı ortada olmak üzere nöbet süresi uzunluğu kadar öncesi ve sonrasını kapsayacak şekilde tüm kayıttan ayrılarak hazırlanmıştır. Elde edilen sayısal veriler kullanılarak her bir alt bant enerji yüzdesi için nöbet öncesi, nöbet anı ve nöbet sonrası enerji farklılıkları istatistiksel olarak da karşılaştırılmıştır. Bu çalışmada sunulan yöntemin epilepsi ve diğer beyin patolojileri için kayıtlanan tüm EEG’ler için uygulanarak beyin dinamikleri hakkında bilgi edinilebileceği ve aynı anda çok sayıda montajdan kayıt alınması durumunda beyin patolojilerine yol açan odakların belirlenmesinde diğer beyin görüntüleme yöntemleriyle birlikte kullanılabileceği düşünülmüştür.

Keywords

Epilepsi, EEG, Nicel analiz, Beyin enerji haritası, Dalgacık dönüşümü

Determination and mapping of energy changes related to brain oscillations with a new approach based on wavelet transform: An epilepsy example

Abstract

The quantitative analysis of brain oscillations has an importance in understanding brain dynamics of several brain pathologies. In this study, it is purposed to determine and map the change in the energy of brain oscillations using a new quantitative approach, based on wavelet transform. An approach, seperating the EEG records to four main sub-band and then quantizing the energy values of them, is suggested. Moreover, energy maps of scalp surface are formed by using these energy values. To test the suggested method, EEG records of epileptic patients which had been obtained from CHB-MIT Scalp EEG database were utilized. The signals which would be analyzed were prepared such that the seizure was in the middle and the signal consists of pre-seizure and post-seizure as long as the seizure time. Then, by using the data obtained, energy differences during pre-seizure, seizure and post-seizure were compared for each sub-band energy percentage. It is thought that the suggested method can be applied to EEGs recorded for epilepsy and other pathologies to be informed about brain dynamics. In addition, in case of getting records simultaneously from several montages, the method can be used for localization of sources causing brain pathologies with other brain imaging techniques.

Keywords

Epilepsy, EEG, Quantitative analysis, Brain energy map, Wavelet transform

References

• Teplan M. “Fundamentals of EEG measurement”. Measurement Science Review, 2(2), 1-11, 2002.
• Fleischmann R, Tränkner S, Bathe-Peters R, Rönnefarth M, Schmidt S, Schreiber SJ, Brandt SA. “Diagnostic performance and utility of quantitative eeg analyses in delirium: confirmatory results from a large retrospective case-control study”. Clinical EEG and Neuroscience, 50(2), 111-120, 2019.
• Dharmadhikari AS, Tandle AL, Jaiswal SV, Sawant VA, Vahia VN, Jog N. “Frontal theta asymmetry as a biomarker of depression”. East Asian Archives of Psychiatry, 28(1), 17-22, 2018
• Goenka A, Boro A, Yozawitz E. “Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes”. Seizure, 55(1), 70-75, 2018.
• Ouyang C, Chiang C, Yang R, Wu R, Wu H, Lin L. “Quantitative EEG findings and response to treatment with antiepileptic medications in children with epilepsy”. Brain & Development, 40(1), 26-35, 2018.
• Smailovic U, Koenig T, Kåreholt I, Andersson T, Kramberger MG, Winblad B, Jelic V. “Quantitative EEG power and synchronization correlate with Alzheimers disease CSF biomarkers”. Neurobiology of Aging, 63(1), 88-95, 2018.
• Ianof J. N, Fraga FJ, Ferreira LA, Ramos RT, Demario JL, Baratho R, Anghinah R. “Comparative analysis of the electroencephalogram in patients with Alzheimers disease, diffuse axonal injury patients and healthy controls using LORETA analysis”. Dementia & Neuropsychologia, 11(2), 176-185, 2017.
• Tolonen A, Särkelä MO, Takala RS, Katila A, Frantzén J, Posti JP, Tenovuo O. “Quantitative EEG parameters for prediction of outcome in severe traumatic brain injury: development study”. Clinical EEG and Neuroscience, 49(4), 248-257, 2018.
• Iznak AF, Iznak EV, Klyushnik TP, Kobel’kov GM, Damjanovich EV, Oleichik IV, Abramova LI. “Neurobiological parameter s in quantitative prediction of treatment outcome in schizophrenic patients”. Journal of Integrative Neuroscience, 17(3-4), 317-329, 2018.
• Sridhar C, Bhat S, Acharya UR, Adeli H, Bairy GM. “Diagnosis of attention deficit hyperactivity disorder using imaging and signal processing techniques”. Computers in Biology and Medicine, 88, 93-99, 2017.
• Markovska-Simoska S, Pop-Jordanova N. “Quantitative EEG in children and adults with attention deficit hyperactivity disorder”. Clinical EEG and Neuroscience, 48(1), 20-32, 2016.
• Gigola S, Ortiz F, D’attellis CE, Silva W, Kochen S. “Prediction of epileptic seizures using accumulated energy in a multiresolution framework”. Journal of neuroscience methods, 138(1-2), 107-111, 2004.
• Hojjat A, Ziqin Z, Nahid D. “Analysis of EEG records in an epileptic patient using wavelet transform”. Journal of Neuroscience Methods, 123(1), 69-87, 2003.
• Procházka A, Jech J, Smith J. “Wavelet transform use in signal processing”. 31st International Conference in Acoustics, Prague, Czechia, 1-3 June 1994.
• Tscharner V. “Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution”. Journal of Electromyography and Kinesiology, 10(6), 433-445, 2000.
• Chen D, Wan S, Xiang J, Bao FS. “A high-performance seizure detection algorithm based on discrete wavelet transform (DWT) and EEG”. PloS one, 12(3), 1-21, 2017.
• Keys R. “Cubic Convolution Interpolation for Digital Image Processing”. IEEE Transactions on Acoustics, Speech and Signal Processing, 29(6), 1153-1160, 1981.
• MIT. “CHB-MIT Scalp EEG Database”. https://www.physionet.org/pn6/chbmit (11.11.2017).
• Singh V, Rana RK, Singhal R. “Analysis of repeated measurement data in the clinical trials”. Journal of Ayurveda and Integrative Medicine, 4(2), 77-81, 2013.
• Rosner B. Fundamentals of Biostatistics. 8th ed. Boston, Massachusetts, USA, Cengage Learning, 2016.
• Thakor NV, Tong S. “Advances in quantitative electroencephalogram analysis methods”. Annual Reviews in Biomedical Engineering, 6, 453-495, 2004.
• Al-shargie FM, Tang TB, Badruddin N, Kiguchi M. “Mental stress quantification using EEG signals”. International Conference for Innovation in Biomedical Engineering and Life Sciences, Putrajaya, Malaysia, 6-8 December 2015.