نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه رازی، کرمانشاه

2 استادیار، گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه رازی، کرمانشاه

10.22041/ijbme.2016.20434

چکیده

یکی از مهم‌ترین اختلالات سیگنال‌های EEG نوزادان، همزمان نبودن کانال‌ها است. مطالعات بالینی نشان­داده است که این امر می‌تواند به نتایج عصبی و جسمی نامطلوبی در بزرگسالی منجر شود. هدف اصلی این مقاله، معرفی روشی جدید برای تشخیص خودکار همزمانی فاز در سیگنال‌های EEG چندکانالة نوزادان است. در روش پیشنهادی، ابتدا فاز لحظه‌ای هر کانال از سیگنال EEG نوزاد با استفاده از تبدیل هیلبرت برآوردشده است. به­دلیل چند­جزئی بودن سیگنال‌های EEG، اجزای سیگنال قبل از استخراج فاز لحظه‌ای با استفاده از مجموعه‌ای از فیلترهای میان‌گذر روی باندهای فرکانسی EEG، به­دست می­آیند. سپس با استفاده از معیاری مبتنی­بر اطلاعات متقابل بین فازهای لحظه‌ای مولفه‌های به­دست­آمده، هم­زمانی کانال‌های مختلف در سیگنال به­طور کمّی اندازه‌گیری می‌شود. در ادامه، از روش پیشنهادی در این مقاله برای بررسی هم­زمانی کانال‌های سیگنال‌های EEG نوزادان در دوره‌های تشنجی-غیرتشنجی و الگوهای B-S استفاده می‌شود. از منحنی ROC برای نمایش عملکرد روش پیشنهادی استفاده­شده است.همچنین عملکرد روش پیشنهادی با روش مبتنی­بر کواینتگریشن مقایسه­شده است. نتایج به­دست­آمده از تحلیل سیگنال‌های EEG پنج نوزاد نشان می‌دهند که روش پیشنهادی عملکرد بهتری در اندازه‌گیری هم­زمانی فاز تعمیم‌یافته نسبت به روش‌های موجود دارد. همچنین با توجه به نتایج، مقدار هم­زمانی فاز تعمیم‌یافته در طول هر دو دورة تشنجی و غیرتشنجی بزرگ‌تر از صفر است که این نتیجه نشان­دهندة وجود اتصالاتی در نیم‌کره‌های مغز نوزادان در هر دو حالت است. همچنین نتایج نشان می‌دهند که دوره‌های تشنجی نسبت به دوره‌های غیرتشنجی هم­زمان‌تر هستند. از بررسی هم­زمانی فاز در الگوهای B-S مشاهده می‌شود که دوره‌های Burst هم­زمان‌تر از دوره‌های Suppression هستند و در هردو حالت، هم­زمانی فاز وجود دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Phase synchrony detection in multichannel newborn EEG signals using a mutual information based method

نویسندگان [English]

  • Sara Mohammadi 1
  • Ghasem Azemi 2

1 M.Sc Student, Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran

2 Assistant Professor, Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran

چکیده [English]

One of the most important newborn EEG abnormalities is the synchrony between different channels which, according to the clinical studies, can lead to neurological and neurodevelopmental outcomes in adulthood. This paper introduces a new method for automated detection of phase synchrony in multivariate signals with applications to newborn EEG signals. In this method, first the instantaneous phase of each channel of the signal is estimated using Hilbert transform. In the case of EEG signals, due to their multicomponent nature, single-band signalsof the signal are needed to be extracted using a bank of band-pass filters. The synchronization between different channels of the signal is then quantitatively measured using a criterion based on the mutual information between instantaneous phases of theextracted single-band signals. The proposed method in this paper is then used to analyze, from synchronization point of view, multichannel EEG signals acquired from 5 newborns which include seizure-nonseizure periods and burst-suppression (B-S) patterns.Reciever operating curves (ROCs) are used to illustrate the performance of the method. The performance of the proposed method is also compared with that of the existing one based on the cointegration concept. Experimental results prove that the proposed method outperforms the existing one in measuring the generalized phase synchrony in multichannel newborn EEG signals. Also, results of analyzing seizure and nonseizure segments show that for all segmants there is a phase synchronization among EEG channels which is due to the connections between brain hemispheres in both cases. The results also show that seizure periods are more synchronous than nonseizure periods. The phase synchrony assessment of B-S patterns indicates that burst patterns are more synchronous than suppression patterns and there is a phase synchrony in both cases.

کلیدواژه‌ها [English]

  • generalized phase synchronization
  • generalized mutual information
  • instantaneous phase
  • newborn EEG
  • multivariate signals
]         M. A. Awal, M. M. Lai, G. Azemi, B. Boashash, and P. B. Colditz, "EEG background features that predict outcome in term neonates with Hypoxic Ischaemic Encephalopathy: a structured review," Clinical Neurophysiology, 2015.
[2]     B. Boashash, G. Azemi, and N. Ali Khan, "Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection," Pattern Recognition, vol. 48, pp. 616-627, 3// 2015.
[3]     J. Riviello, Jr., "Pediatric EEG Abnormalities," in The Clinical Neurophysiology Primer, A. Blum and S. Rutkove, Eds., ed: Humana Press, 2007, pp. 179-204.
[4]     M. Scher, "Electroencephalography of the newborn: normal and abnormal features," Electroencephalography; Basic principles, pp. 896-946, 1999.
[5]     W. Blume, G. Holloway, M. Kaibara, and G. Young, "Normal EEG," Atlas of Pediatric and Adult Electroencephalography. Philadelphia (PA): Lippincott Williams & Wilkins, pp. 73-74, 2011.
[6]     A. Valentín and G. Alarcón, Introduction to epilepsy: Cambridge University Press, 2012.
[7]     L. M. Dubowitz, V. Dubowitz, and E. Mercuri, The neurological assessment of the preterm and full-term newborn infant: Cambridge University Press, 1999.
[8]     B. C. L. Touwen, Neurological development in infancy vol. 58: Heinemann Educational Books, 1976.
[9]     B. R. Tharp, F. Cukier, and N. Monod, "The prognostic value of the electroencephalogram in premature infants," Electroencephalography and clinical neurophysiology, vol. 51, pp. 219-236, 1981.
[10]   K. Watanabe, F. Hayakawa, and A. Okumura, "Neonatal EEG: a powerful tool in the assessment of brain damage in preterm infants," Brain and Development, vol. 21, pp. 361-372, 1999.
[11]   M. Le Van Quyen, J. Foucher, J.-P. Lachaux, E. Rodriguez, A. Lutz, J. Martinerie, et al., "Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony," Journal of Neuroscience Methods, vol. 111, pp. 83-98, 9/30/ 2001.
[12]   V. Sakkalis, "Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG," Computers in Biology and Medicine, vol. 41, pp. 1110-1117, 12// 2011.
[13]   J. Bhattacharya and H. Petsche, "Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise," Signal Processing, vol. 85, pp. 2161-2177, 11// 2005.
[14]   C. J. Stam, G. Nolte, and A. Daffertshofer, "Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources," Human brain mapping, vol. 28, pp. 1178-1193, 2007.
[15]   A. Omidvarnia, G. Azemi, P. B. Colditz, and B. Boashash, "A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals," Digital Signal Processing, vol. 23, pp. 780-790, 5// 2013.
[16]   M. Palus̆, "Detecting phase synchronization in noisy systems," Physics Letters A, vol. 235, pp. 341-351, 11/10/ 1997.
[17]   M. Palus̆ and D. Hoyer, "Surrogate data in detecting nonlinearity and phase synchronization," in IEEE Engineering in Medicine and Biology, 1998.
[18]   F. Mormann, K. Lehnertz, P. David, and C. E. Elger, "Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients," Physica D: Nonlinear Phenomena, vol. 144, pp. 358-369, 10/1/ 2000.
[19]   R. QuianQuiroga, A. Kraskov, T. Kreuz, and P. Grassberger, "Performance of different synchronization measures in real data: A case study on electroencephalographic signals," Physical Review E, vol. 65, p. 041903, 03/15/ 2002.
[20]   J.-P. Lachaux, E. Rodriguez, J. Martinerie, and F. J. Varela, "Measuring phase synchrony in brain signals," Human brain mapping, vol. 8, pp. 194-208, 1999.
[21]   A. R. Kammerdiner and P. M. Pardalos, "Analysis of multichannel EEG recordings based on generalized phase synchronization and cointegrated VAR," in Computational Neuroscience, ed: Springer, 2010, pp. 317-339.
[22]   M. Paluš, "Detecting nonlinearity in multivariate time series," Physics Letters A, vol. 213, pp. 138-147, 1996.
[23]   M. Paluš, V. Albrecht, and I. Dvořák, "Information theoretic test for nonlinearity in time series," Physics Letters A, vol. 175, pp. 203-209, 1993.
[24]   E. Niedermeyer and F. L. da Silva, Electroencephalography: basic principles, clinical applications, and related fields: Lippincott Williams & Wilkins, 2005.
[25]   T. Fawcett, "An introduction to ROC analysis," Pattern recognition letters, vol. 27, pp. 861-874, 2006.
[26]   J. A. Hanley and B. J. McNeil, "The meaning and use of the area under a receiver operating characteristic (ROC) curve," Radiology, vol. 143, pp. 29-36, 1982.
[27]   J. Altenburg, R. J. Vermeulen, R. L. Strijers, W. P. Fetter, and C. J. Stam, "Seizure detection in the neonatal EEG with synchronization likelihood," Clinical neurophysiology, vol. 114, pp. 50-55, 2003.
[28]   V. Doria, C. F. Beckmann, T. Arichi, N. Merchant, M. Groppo, F. E. Turkheimer, et al., "Emergence of resting state networks in the preterm human brain," Proceedings of the National Academy of Sciences, vol. 107, pp. 20015-20020, 2010.
[29]   P. Fransson, B. Skiöld, S. Horsch, A. Nordell, M. Blennow, H. Lagercrantz, et al., "Resting-state networks in the infant brain," Proceedings of the National Academy of Sciences, vol. 104, pp. 15531-15536, 2007.