Phương pháp trích xuất đặc trưng mới để phát hiện tín hiệu EEG động kinh sử dụng phân phối thời gian-tần số

Medical & Biological Engineering & Computing - Tập 48 - Trang 321-330 - 2010
Carlos Guerrero-Mosquera1, Armando Malanda Trigueros1, Jorge Iriarte Franco1, Ángel Navia-Vázquez1
1Signal Processing and Communications Department, University Carlos III of Madrid, Madrid, Spain

Tóm tắt

Bài báo này mô tả một phương pháp mới để nhận diện cơn co giật trong tín hiệu điện não (EEG) bằng cách sử dụng trích xuất đặc trưng trong các phân phối thời gian-tần số (TFDs). Cụ thể, phương pháp này trích xuất các đặc trưng từ phân phối Wigner-Ville giả mượt mà bằng cách sử dụng các đường đi ước lượng từ mô hình sinsoidal McAulay-Quatieri. Các đặc trưng được đề xuất bao gồm độ dài, tần số và năng lượng của đường đi chính. Chúng tôi đánh giá sơ đồ đề xuất bằng cách sử dụng nhiều bộ dữ liệu và tính toán độ nhạy, độ đặc hiệu, điểm F, đường đặc trưng hoạt động của bộ nhận (ROC), và độ tin cậy bootstrap percent để kết luận rằng sơ đồ đề xuất tổng quát tốt và là một phương pháp phù hợp cho việc phát hiện cơn co giật tự động với chi phí vừa phải, đồng thời mở ra khả năng xây dựng các tiêu chí mới để phát hiện, phân loại hoặc phân tích các tín hiệu EEG bất thường.

Từ khóa

#điện não #phát hiện cơn co giật #trích xuất đặc trưng #phân phối thời gian-tần số #Wigner-Ville

Tài liệu tham khảo

Abásolo D, Escudero J, Hornero R, Gómez C, Espino P (2008) Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput 46:1019–1028 Acir N, Oztura I, Kuntalp M, Baklan B, Guzelis C (2005) Automatic detection of epileptiform events in EEG by three-stage procedure based on artificial neural networks. IEEE Trans Biomed Eng 52:30–40 Afonso VX, Tompkins WJ (1995) Detecting ventricular fibrillation. IEEE Eng Med Biol 14:152–159 Akay M (1996) Detection and estimation methods for biomedical signals. Academic Press, New Jersey Auger F, Aldrin P, Goncalves P, Lemoine O (1996) Time–frequency toolbox for Matlab, user’s guide and reference guide. CNRS (France) and Rice University (USA), Paris Barlow JS (1985) Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review. J Clin Neurophysiol 2:267–304 Blume WT, Young GB, Lemieux JF (1984) EEG morphology of partial epileptic seizures. Electroencephalogr Clin Neurophysiol 4:295–302 Boashash B (2003) Time frequency signal analysis and processing. A comprehensive reference. Elsevier, Oxford Boashash B, Mesbah M (2001) A time–frequency approach for newborn seizure detection. IEEE Eng Med Biol Mag 20(5):54–64 Boashash B, Mesbah M (2002) Time–frequency methodology for newborn electroencephalographic seizure detection. In: Papandreou-Suppappola A (ed) Applications in time–frequency signal processing. CRC Press, Boca Raton, Florida Boashash B, Carson H, Mesbah M (2000) Detection of seizures in newborns using time–frequency of EEG signals. Proceedings of Tenth IEEE workshop on statistical signal and array processing, pp 564–568 Cardoso JF (1998) Blind signal separation: statistical principles. Proc IEEE 86:2009–2025 Carmona RA, Hwang WL, Torrésani B (1999) Multiridge detection and time–frequency reconstruction. IEEE Trans Signal Process 47:480–492 Cohen L (1989) Time–frequency distributions—a review. Proc IEE 77:941–981 Cohen L (1995) Time–frequency analysis. Prentice Hall, Upper Saddle River, NJ Colder BW, Frysinger RC, Wilson CL, Harper RM, et al (1996) Decreased neuronal burst discharge near site of seizure onset in epileptic human temporal lobes. Epilepsia 37:113–121 Durka PJ (1996) Time–frequency analysis of EEG. Thesis Institute of Experimental Physics, Warsaw University Freeman WJ (1963) The electrical activity of a primary sensory cortex: analysis of EEG waves. Int Rev Neurobiol 5:53–119 Gonzalez B, Sanei S, Chambers JA (2003) Support vector machines for seizure detection. Proceedings of the IEEE ISSPIT, pp 126–129 Gotman J (1982) Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol 54:530–540 Gotman J (1983) Measurement of small time differences between EEG channels: methods and application to epileptic seizure propagation. Electroencephalogr Clin Neurophysiol 56:501–514 Grewal S, Gotman J (2005) An automatic warning system for epileptic seizures recorded on intracerebral EEGs. Clin Neurophysiol 116:2460–2472 Guerrero C, Malanda A, Iriarte J (2005) Time–frequency EEG analysis in epilepsy: what is more suitable? Proceedings of the IEEE ISSPIT, pp 202–207 Guerrero-Mosquera C, Navia Vazquez A (2009) Automatic removal of ocular artifacts from EEG data using adaptive filtering and independent component analysis. Proceedings of the 17th European signal processing conference (EUSIPCO), pp 2317–2321 Harrell FE (2001) Regression modeling strategies. Springer, New York Hassanpour H, Mesbah M, Boashash B (2004) Time–frequency feature extraction of newborn EEG seizure using SVD-based techniques. Proceedings of EURASIP. J Appl Signal Process 16:2544–2554 He P, Wilson G, Russel C (2004) Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med Biol Eng Comput 42:407–412 Hinrikus H, Suhhova A, Bachmann M, et al (2009) Electroencephalographic spectral asymmetry index for detection of depression. Med Biol Eng Comput 47:1291–1299 Hlawatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time–frequency signal representation. IEEE SP Mag 9:21–67 Hoeve M, Zwaag BJ, Slump K, Jones R (2003) Detecting epileptic seizure activity in the EEG by independent component analysis. Proceedings of the ProRISC workshop on circuits systems and signal processing, pp 373–378 Iriarte J, Urrestarazu E, Valencia M, Alegre M, Malanda A, Viteri C, Artieda J (2003) Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J Clin Neurophysiol 20:249–257 Joyce CA, Gorodnitsky IF, Kutas M (2004) Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41:1–13 Kay SM, Marple SL (1981) Spectrum analysis: a modern perspective. Proc IEEE 69:1380–1419 Lehnertz K, Elger CE (1995) Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr Clin Neurophysiol 95:108–117 Le Van P, Urrestarazu E, Gotman J (2006) A system for automatic removal in ictal scalp EEG based on independent component analysis and Bayesian classification. Clin Neurophysiol 117:912–927 Li H, Sun Y (2005) The study and test of ICA algorithms. Proc IEEE Wirel Commun Netw Mob Comput 1:602–605 Lin Z-Y, Chen JDZ (1996) Advances in time–frequency analysis of biomedical signals. Crit Rev Biomed Eng 24:1–70 Makeig S, Bell AJ, Jung TP, Sejnowski T (1996) Independent component analysis of electroencephalogram data. Adv Neural Inf Process Syst 145–151 McAulay RJ, Quatieri TF (1986) Speech analysis/synthesis based on a sinusoidal representation. IEEE Trans Acoust Speech Signal Process 34:744–754 Mohseni HR, Maghsoudi A, Shamsollahi MB (2006) Seizure detection in EEG signals: a comparision of different approaches. Proceedings of the 28th IEEE annual EMBS international conference, pp 6724–6727 Muthuswamy J, Thakor NV (1998) Spectral analysis methods for neurological signals. J Clin Neurophysiol 83:1–14 Osorio I, Frei MG, Wilkinson SB (1998) Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsy 39:615–627 Rankine R, Mesbah M, Boashash B (2007) IF estimation for multicomponent signals using image processing techniques in the time–frequency domain. Signal Process 87:1234–1250 Sclabassi RJ, Sun M, Krieger DN, Scher MS (1990) Time–frequency analysis of the EEG signal. Proceedings of the international conference on signal processing, pp 935–938 Senhadji L, Wendling F (2002) Epileptic transient detection: wavelets and time–frequency approaches. Neurophysiol Clin 32:175–192 Swarnkar V, Abeyratne UR, Hukins C, Duce B (2009) A state transition-based method for quantifying EEG sleep fragmentation. Med Biol Eng Comput 47:1053–1061 Tognola G, Ravazzani P, Minicucci F, Locatelli T, et al (1996) Analysis of temporal non-stationarities in EEG signals by means of parametric modelling. Technol Health Care 4:169–185 Tseng SY, Chen RC, Chong FC, Kuo TS (1995) Evaluation of parametric methods in EEG signal analysis. Med Eng Phys 17:71–78 Tzallas AT, Tsipouras MG, Fotiadis DI (2007) The use of time–frequency distributions for epileptic seizure detection in EEG recordings. Proceedings of the IEEE EMBS, pp 3–6 Williams WJ, Zavery HP, Sackellares JC (1995) Time–frequency analysis in electrophysiology signals in epilepsy. IEEE Eng Med Biol 14:133–143