Decision Support System for Epileptogenic Zone Location During Brain Resection

• Autorzy: Kołodziej M. , Majkowski A. , Rak R. J. , Rysz A. , Marchel A.

Identyfikatory

DOI

10.24425/118167

• Języki publikacji: EN

Abstrakty

EN

This paper presents a system for locating the epileptogenic zone (EZ) using an automated analysis of electrocorticography (ECoG) signal recorded with 20 electrodes placed on the brain surface. The developed system enables automatic determination of places where anomalies connected with epilepsy are observed. The developed algorithm was tested on signals recorded for 33 patients who, after a prior neurological analysis, underwent the brain resection surgery. The results obtained with the algorithm were compared with those of medical analyses performed by the neurologist. The proposed system has a satisfactory accuracy – 87.8% – and can be used as a decision-supporting tool by the neurosurgeon during brain resection.

Słowa kluczowe

EN

ECoG; iEEG; electrocorticography; epileptogenic zone; signal analysis; expert system; neural network

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2018
• Tom: Vol. 25, nr 1
• Strony: 15--32
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr., wzory

Twórcy

autor

Kołodziej M.

• Warsaw University of Technology, Institute of Theory of Electrical Engineering, Measurements and Information Systems, Koszykowa 75, 00-662 Warsaw, Poland

autor

Majkowski A.

• Warsaw University of Technology, Institute of Theory of Electrical Engineering, Measurements and Information Systems, Koszykowa 75, 00-662 Warsaw, Poland

autor

Rak R. J.

• Warsaw University of Technology, Institute of Theory of Electrical Engineering, Measurements and Information Systems, Koszykowa 75, 00-662 Warsaw, Poland

autor

Rysz A.

• Medical University of Warsaw, Department of Neurosurgery, Banacha 1A, 02-097 Warsaw, Poland

autor

Marchel A.

• Medical University of Warsaw, Department of Neurosurgery, Banacha 1A, 02-097 Warsaw, Poland

Bibliografia

• [1] Messenheimer, J.A. (1991). Epilepsy: Frequency, causes and consequences. J. Epilepsy, 4(4), 246.
• [2] Baumgartner, C., Lurger, S., Leutmezer F. (2001). Autonomic symptoms during epileptic seizures. Epileptic Disord. Int. Epilepsy J. Videotape, 3(3), 103-116.
• [3] Guerrini, R., Scerrati, M., Rubboli, G., Esposito, V., Colicchio, G., Cossu, M., Marras, C.E.,Tassi, L., Tinuper P., Canevini, M.P., Quarato, P., Giordano, F., Granata, T., Villani, F., Giulioni, M., Scarpa, P., Barbieri, V., Bottini, G., Sole, A.D., Vatti, G., Spreafico, R., Russo, G.L. (2013). Overview of presurgical assessment and surgical treatment of epilepsy from the Italian League Against Epilepsy. Epilepsia, 54, 35-48.
• [4] Tripathi, M., Garg, A., Gaikwad, S., Bal, C.S., Chitra, S., Prasad, K., Dash, H.H., Sharma, B.S., Chandra P.S. (2010). Intra-operative electrocorticography in lesional epilepsy. Epilepsy Res., 89(1), 133-141.
• [5] Hajek, M., Antonini, A., Leenders, K.L., Wieser, H.G. (1993). Mesiobasal versus lateral temporal lobe epilepsy Metabolic differences in the temporal lobe shown by interictal 18F-FDG positron emission tomography. Neurology, 43(1), 79-79.
• [6] Dupont, S., Semah, F., Boon, P., Saint-Hilaire, J.M., Adam, C., Broglin, D., Baulac, M. (1999). Association of ipsilateral motor automatisms and contralateral dystonic posturing: a clinical feature differentiating medial from neocortical temporal lobe epilepsy. Arch. Neurol., 56(8), 927-932.
• [7] Blümcke, I., Thom, M., Aronica, E., Armstrong, D.D., Vinters H.V., Palmini, A., Jacques, T.S., Avanzini, G., Barkovich, A.J., Battaglia, G., Becker, A., Cepeda, C., Cendes, F., Colombo, N., Crino, P., Cross, J.H., Delalande, O., Dubeau, F., Duncan, J., Guerrini, R., Kahane, P., Mathern, G., Najm, I., Ozkara, C., Raybaud, C., Represa, A., Roper, S.N., Salamon, N., Schulze-Bonhage, A., Tassi, L., Vezzani, A., Spreafico, R. (2011). The clinicopathologic spectrum of focal cortical dysplasias: a consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission. Epilepsia, 52(1), 158-174.
• [8] Bozek-Juzmicki, M., Colella, D., Jacyna, G.M. (1994). Feature-based epileptic seizure detection and prediction from ECoG recordings. Proc. of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis, 564-567.
• [9] Hill, N.J., Gupta, D., Brunner, P., Gunduz, A., Adamo, M.A., Ritaccio, A., Schalk, G. (2012). Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. J. Vis. Exp. JoVE, (64).
• [10] Gotman, J. (1999). Automatic detection of seizures and spikes. J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc., 16(2), 130-140.
• [11] McGrogan N.S., Tarassenko, L. (1999). Neural Network Detection of Epileptic Seizures in the Electroencephalogram.
• [12] Harner, R. (2009). Automatic EEG Spike Detection. Clin. EEG Neurosci., 40(4), 262-270.
• [13] Dümpelmann, M., Elger, C.E. (1998). Automatic detection of epileptiform spikes in the electrocorticogram: a comparison of two algorithms. Seizure , 7(2), 145-152.
• [14] Wilson, S. B., Turner, C.A., Emerson, R.G., Scheuer, M.L. (1999). Spike detection II: automatic, perception-based detection and clustering. Clinical Neurophysiology, 110(3), 404-411.
• [15] Webber, W.R., Litt, B., Wilson, K., Lesser R.P. (1994). Practical Detection of Epileptiform Discharges (EDs) in the EEG Using an Artificial Neural Network: A Comparison of Raw and Parameterized EEG Data. PubMed Journals, 91(3), 194-204.
• [16] Fan, J., Shao, C., Ouyang, Y., Wang, J., Li, S., Wang, Z. (2006). Automatic Seizure Detection Based on Support Vector Machines with Genetic Algorithms. Simulated Evolution and Learning, 845-852.
• [17] Exarchos, T.P., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S., Giannopoulos, S. (2006). EEG Transient Event Detection and Classification Using Association Rules. IEEE Trans. Inf. Technol. Biomed., 10(3), 451-457.
• [18] Srinivasan, V., Eswaran, C., Sriraam, N. (2005). Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features J. Med. Syst., 29(6), 647-660.
• [19] Polat, K., Güne ̧s, S. (2007). Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput., 187(2),1017-1026.
• [20] Übeyli, E.D., Güler, ̇I. (1996). Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recognit. Lett., 28(5), 592-603.
• [21] Iasemidis, L.D., Sackellares, J.C. (1996). REVIEW: Chaos Theory and Epilepsy. The Neuroscientist, 2(2), 118-126.
• [22] Kannathal, N., Acharya, U.R., Lim, C.M., Sadasivan, P.K. (2005). Characterization of EEG - A comparative study. Comput. Methods Programs Biomed. , 80(1), 17-23.
• [23] Lerner, D.E. (1996). Monitoring changing dynamics with correlation integrals: Case study of an epileptic seizure. Phys. Nonlinear Phenom., 97(4), 563-576.
• [24] Srinivasan, V., Eswaran, C., Sriraam, N. (2007). Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. Publ. IEEE Eng. Med. Biol. Soc., 11(3), 288-295.
• [25] Osowski, S., Swiderski, B., Cichocki, A., Rysz, A. (2007). Epileptic seizure characterization by Lyapunov exponent of EEG signal. COMPEL - Int. J. Comput. Math. Electr. Electron. Eng., 26(5), 1276-1287.
• [26] Schwab, M., Schmidt, K., Witte, H., Abrams, R.M. (2000). Investigation of Nonlinear ECoG Changes during Spontaneous Sleep State Changes and Cortical Arousal in Fetal Sheep. Cereb. Cortex, 10(2), 142-148.

Uwagi

EN

This work was partly financed from the funds for the statutory activities of the Faculty of Electrical Engineering of the Warsaw University of Technology, under Dean’s Grant in 2017.

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).