Diagnosis of Epilepsy from EEG signals using Hilbert Huang transform

Transcription

1 Original article Diagnosis of Epilepsy from EEG signals using Hilbert Huang transform Sandra Ibrić 1*, Samir Avdaković 2, Ibrahim Omerhodžić 3, Nermin Suljanović 1, Aljo Mujčić 1 1 Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina 2 Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina 3 Clinical Center, University of Sarajevo, Sarajevo, Bosnia and Herzegovina Abstract In this paper application of Hilbert-Huang transform (HHT) to electroencephalogram (EEG) signals analysis is presented, in order to simplify diagnosis of epilepsy. HHT consists of empirical mode decomposition (EMD) and Hilbert spectral analysis. Hilbert marginal spectrum represents a contribution of total amplitude (or energy) over various frequency values. This approach is used for analyzing 200 EEG signals, where half of all signals are from healthy subjects and the other half are signals of subjects with epileptic syndrome without seizure. It is showed that this method provides clear distinctions in visualisation of EEG signals of healthy and ill subjects, so that it can efficiently identificate epilepsy syndrome. Further, this approach can be a foundation for development of simple automated EEG signal classifiers in the aspect of recognition subjects with epilepsy syndrome and find its place in standard clinical practice. Keywords: EEG, epilepsy, Hilbert Huang transform, Hilbert marginal spectrum 2015 Folia Medica Facultatis Medicinae Universitatis Saraeviensis. All rights reserved. *Corresponding author: Sandra Ibrić Department of Telecommunications, Faculty of Electrical Engineering, University of Tuzla, Franjevačka 2, Tuzla, Bosnia and Herzegovina Phone: sandra.ibric@untz.ba Introduction Epilepsy is one of the most common neurological diseases followed with frequent epileptic seizures. Cause of epilepsy originates from chronic structural and functional abnormalities in cerebral cortex. EEG is essential for diagnosis of epilepsy and EEG signal analysis is also used for seizure type detection. Electroencephalograph is an apparatus for recording electrical brain activity over a period of time. The analysis of obtained signals is complex. Various transform methods are used with the goal of getting the form of signals more suitable for further analysis. There are many methods that have been investigated in context of EEG signal processing, and some of them are wavelet transform (WT) [1], Hilbert-Huang transform [2], and some other can be found in the work of Shayan et al. [3]. In this paper, Hilbert-Huang transform is applied to the analysis of 200 EEG signals, where 100 EEG signals are from healthy subjects and 100 EEG signals are from subjects with epileptic syndrome without seizure. Hilbert-Huang transform is the time-frequency analysis method of nonstationary and nonlinear data. Empirical mode decomposition method EMD is the fundamental part of HHT and it is used to decompose signal into simple harmonic functions. After EMD for each extracted component Hilbert spectrum is determined, resulting with time-frequency distribution. In this paper it is shown that examined method can efficiently identificate subjects with epilepsy syndrome based on their EEG signals. This paper has the following structure: In section II short theoretical background of HHT method is provided. Section III describes simulation results and contains obtained results interpretation, while conclusions are found in section IV. 68 foliamedica.mf.unsa.ba Folia Med. Fac. Med. Univ. Saraeviensis 2015; 50(1): 68-73

2 Theoretical basics In EEG signals all frequency components do not exist during complete signal duration, so that they are denoted as nonstationary signals. Nonstationary signals are signals in which frequency component varies over their duration.this paper will indicate how HHT transform gives good results for analysing such data. HHT performs the decomposition of a dataset into a finite set od functions, and uses Hilbert spectral analysis for each component in order to compute instantaneous frequencies [4-6]. Component functions, obtained as a result of EMD method, are called intrinsic mode functions (IMFs). In order to be IMF, a function has to meet the following conditions [2]: total number of extreme values and total number of zero crossings in the whole dataset should be the same or must not differ by more than one; mean envelope value, determined by local minima and local maxima envelope, has to be zero at any point. Figure 1 illustrates examples of EMD method in case when applied to EEG signal of a) a healthy subject and b) a subject with epileptic syndrome without seizure. a) b) Figure 1a. Examples of EEG signal decomposition with EMD method in case of EEG signal of a) a healthy subject b) a subject with epileptic syndrome without seizure and marginal Hilbert spectrum Folia Med. Fac. Med. Univ. Saraeviensis 2015; 50(1): foliamedica.mf.unsa.ba 69

3 c) d) Figure 1b. Examples of EEG signal decomposition with EMD method in case of EEG signal of c) a healthy person EEG signal d) EEG signal of a patient with epileptic syndrome EMD algorithm of selecting IMF function from the given dataset includes next steps: find all local extreme values that occure in signal, then determine the upper envelope connecting all local maxima by cubic spline line, and create the lower envelope connecting the local minima with a cubic spline interpolation. Let m 1 be the mean value of the upper and the lower envelope. Then the difference between the original signal x(t) and m 1 is the first component if it complies with IMF function definition. Therefore, h 1 (t)=x(t)-m 1, if h 1 (t) does not satisfy all requirements to be an IMF function, the procedure is repeated observing h 1 (t) as a new signal. Calculate h 11 (t): h 11 (t)=h 1 (t)-m 11, where m 11 is mean of the upper and the lower envelope value for signal h 1 (t). The described algorithm is iterated up to k times, as long as we do not get h 1k (t) component that meets the IMF criteria. In that case, h 1k (t) is denoted as c 1 (t) and it represents the first IMF component. As the first IMF component, c 1 (t) contains the highest frequencies appearing in the analyzed data. Once the first IMF is identified, it is substracted from the original data, and substraction result is referred to as residue. Then the residue is treated as the original signal and the same sifting process is aplied to it. The procedure is repeated until all IMF components are determined (final residue should be constant or monotonic function) or predefined condition is met. After all IMFs are extracted from the analyzed signal, Hilbert transform is applied to each IMF component. If H[c i (t)] denotes i-th component Hilbert trasform, then z i (t) expressed as: z i (t)=c i (t)+j*h[c i (t)] form a complex conjugate pair. If a i (t), θ i (t) and ω i (t) 70 foliamedica.mf.unsa.ba Folia Med. Fac. Med. Univ. Saraeviensis 2015; 50(1): 68-73

4 are instantaneous amplitude, phase and frequency functions of z i (t) signal respectively, the original signal can be shown as: x(t)=h[ω,t]=re{a i (t)exp(j* ω i (t)dt)}, which represents Hilbert-Huang spectrum. For thus defined Hilbert-Huang spectrum, marginal Hilbert spectrum is obtained as: h(ω)= T 0 H[ω,t]dt, where T is the total data length. Figure 1 c) and d) show marginal Hilbert spectrum examples. Hilbert spectrum gives time-frequency amplitude distribution, while marginal Hilbert spectrum represents the total amplitude (or energy) contribution from each frequency value [4-6]. By visual examination of the two observed EEG signals from Figure 1 a) and b), it is difficult or almost impossible to detect clear differences and finally determine epilepsy syndrome observing these signals. However, using the presented method and marginal Hilbert spectrum enables a clear identification of different amplitudes in EEG signal components, which is especially obvious at frequency range up to 30 Hz. Results and discussion The methodology briefly described in the previous section and illustrated on two EEG signals (signal of a healthy subject and signal of a subject with epileptic syndrome without seizure) is applied to 200 EEG signals collected by Dr. Ralph Andrzejak, Epilepsy Center in Bonn, Germany, ( de/) [7]. The analysis of these signals is made using the available sofware codes presented in works [8-9]. The results are compared with one obtained over the same signals but using global wavelet spectrum (GWS) [1]. Electrical brain signals Information transfer through the brain has electrochemical nature, but electrical potentials created by individual neurons have extremely small amplitudes, and only synchronized activity of larger number of neurons creates a potential large enough to be measured with electrodes placed on the scalp [10]. Signals collected with these electrodes represent the brain activity and are called electroencephalogram (EEG). Recorded electrical activity of the brain is based on postsynaptic potentials that last longer and occur on a larger neuron surface than action potentials and therefore have a higher detection probability [10]. In terms of denoting individual EEG frequency bands, the commonly used classification contains: delta (0 4 Hz), theta (4 8 Hz), alpha (8 12 Hz), beta (13 30 Hz), and gamma (30 60 Hz) rhythms or signals [11]. Activities or amplitudes of particular EEG signal components can have great benefits in terms of patient status identification or certain diagnosis establishment. Thus, according to Krbot [10], the characteristics of EEG signal components can be shortly described as follows: Alfa- centered in the frequency range from 8 to 12 Hz with 20 to 60 μv amplitudes, typically occurring in the occipital brain areas, and this rhythm is specific for a complete relaxation state [10]: also, with the beginning of either mental or physical action, these waves disappear. Beta- in 13 to 30 Hz frequency range, amplitudes from 2 to 20μV, characteristic of the state of normal, healthy vigilance, increased concentration and intense mental activity [10]. Delta-oscillation with great amplitudes and low frequencies, with 20 to 200 μv, while theta components amplitudes are in the range from 20 to 100 μv. Gamma waves typically have higher frequencies, about 40 Hz. In the context of epilepsy or epilepsy identification based on EEG signals, numerous studies show that amplitudes of EEG signals components from healthy and patients with epilepsy distinguish significantly. This kind of identification needs additional signal processing to achieve clear separation between individual components and practical application. Examples of discrete wavelet transform or continuous wavelet transform are shown in [13], [12] and [1]. Figure 2 represents results of the research conducted in this paper: a) Marginal Hilbert spectrum of healthy subjects (the first 100 signals) and patiens with epilepsy syndrome (the other 100 signals) compared with b) Global wavelet spectrum of healthy subjects (the first 100 signals) and patiens with epilepsy syndrome (the other 100 signals) [1]. These results point to the differences in activity of individual EEG signals components between healthy patients and patients with epilepsy syndrome EEG signals. Significantly higher amplitudes are identified in individuals with epilepsy syndrome than in healthy subjects in band up to 10 Hz (delta and theta waves), and the comparison of the obtained results with global wavelet spectrum results shows that both approaches provide clear separation of EEG signals from the two analyzed groups. Certain amplitudes in the area of alfa waves are noted in both groups, while for some signals from the healthy subject group marginal Hilbert spectrum identifies significant amplitudes in the frequency band from Hz. Possibly the most important conclusion that has been reached in this work is that clear differentiation between two EEG signal groups is achieved. In addition to the EEG signal component identification achieved with this kind of signal processing, the presentation by spectrum (marginal Hilbert or global wavelet spectrum) keeps features related to individual components amplitudes and reduce the number Folia Med. Fac. Med. Univ. 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5 Figure 2. a) Marginal Hilbert spectrum of healthy subjects (the first 100 signals) and patiens with epilepsy syndrome (the other 100 signals) compared with b) Global wavelet spectrum of healthy subjects (the first 100 signals) and patiens with epilepsy syndrome (the other 100 signals) [1]. of numerical values, which gives a potential for creating simple automated classifiers of EEG signals. Declaration of interest The authors declare no conflict of interest for this study. Conclusions In this paper, Hilbert-Huang transform was performed for two groups of EEG signals: one representing EEG signals of healthy subjects and the other from patiens with epileptic syndrome without seizure. Results presentation has been done through EMD procedure and marginal Hilbert spectrum. Results obtained using this methodology indicate that the EEG signals from the subjects with epileptic syndrome contain components with significant amplitude values in the frequency range up to 10 Hz, which physically represents the area of delta and theta waves. According to these results, clear differentiation between two signal groups is enabled, offering an excellent base for simple and efficient automated classifier development, which is reserved for further researche in this area. 72 foliamedica.mf.unsa.ba Folia Med. Fac. Med. Univ. Saraeviensis 2015; 50(1): 68-73

6 References [1] Avdakovic S., Omerhodzic I., Badnjevic A. and Boskovic D., Diagnosis of Epilepsy from EEG signals using Global Wawelet Power Spectrum,6th European Conference of the International Federation for Medical and Biological EngineeringIFMBE Proceedings Volume 45, 2015, pp [2] Hui Li, Yuping Zang and Haiqu Zheng (2008) Hilbert- Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings, Journal of Mechanical Science and Technology 23 (2009) 291~301 [3] ShayanMotamedi-Fakhret et al (2014), Signal processing techniques applied to human sleep EEG signals A review. Biomedical Signal Processing and Control 10: [4] Huang, N., Shen, Z., Long, S., Wu, M., Shih, E., Zheng, Q., Tung, C., Liu, H., (1998), The Empirical Mode Decomposition Method and the Hilbert Spectrum for Non-Stationary Time Series Analysis: Proceedings of the Royal Society of London, A454, [5] Huang, N., Wu, M.C., Long, S.R., Shen, S.S.P., Qu, W., Gloersen, P., Fan, K.L. (2003) A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis. Proceedings of the Royal Society of London, A459, [6] Huang, N., Wu, Z., Long, S., Arnold, K., Chen, X., Blank, K., (2009), On Instantaneous Frequency. Advances in Adaptive Data Analysis, 1, [7] Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE. Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E Stat Nonlin Soft Matter Phys 2001; 64(6 Pt 1): Online available at: de/cms/front_content.php?idcat=193&lang=3&changelang=3. [8] Flandrin, P., Rilling, G. Goncalves, P. (2004), Empirical Mode Decomposition as a Filter Bank. IEEE Signal Process. Lett., 11, [9] Battista, B., Knapp, C., McGee, T., Goebel, V. (2007). Application of the Empirical Mode Decomposition and Hilbert-Huang Transform to Seismic Reflection Data. Geophysics, 72(2), H29 H37, doi: / [10] Krbot, M. Električna aktivnost mozga i njezina primjena upreoperativnoj procjeni lateralizacije govornefunkcije u pacijenata s epilepsijom, FER Zagreb [11] Adeli H. Ghosh-Dastidar S, Dadmehr (2007). A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans Biomed Eng. 54(2): [12] Ibrahim Omerhodzic, Samir Avdakovic, Amir Nuhanovic, Kemal Dizdarevic and Kresimir Rotim (2012). Energy Distribution of EEG Signal Components by Wavelet Transform, Wavelet Transforms and Their Recent Applications in Biology and Geoscience, Dr. Dumitru Baleanu (Ed.), ISBN: , InTech, DOI: / [13] Omerhodzic I, Avdakovic S, Nuhanovic A, Dizdarevic K. Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. World Academy of Science, Engineering and Technology 2010; 61: Folia Med. Fac. Med. Univ. Saraeviensis 2015; 50(1): foliamedica.mf.unsa.ba 73