Dept. of Physiology, Istanbul Medical Faculty, Istanbul University. Istanbul, Turkey. Private Pediatric ... Wavelet Transform, Boundary Element Method, MUSIC.
Proceedings of the 25* Annual International Conference of the IEEE EMBS Cancun, Mexico September 17-21,2003
Epileptic Source Localization Using Wavelet Prefiltering and MUSIC Scanning Ahmet Ademoglul Tamer Demiralp2 Yorgo Istefanopulosl , Sinan Comu3. Betul Baykan4 Inst. of Biomedical Engineering, Bogazici Cniversity, Istanbul, Turkey Dept. of Physiology, Istanbul Medical Faculty, Istanbul University. Istanbul, Turkey Private Pediatric Seurologist Dept. of Neurology, Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey AbstractThe epileptic focus localization in the 3-D brain structure is performed using integer spline wavelet prefiltering and source localization. The forward and inverse problems a r e solved by Boundary Element Method (BEM)and MUSIC (Multiple Signal Classification) Scanning algorithm, respectively. The realistic head model is obtained by using an average human MRI d a t a released by t h e Montreal Neurological Institute (MNI). 19 Channel EEG data from three different patients are collected. T h e technique is tested on these three clinical cases with secondary epileptic seizures based on morphological lesions t h a t could be depicted in t h e anatomical MRI examinations of t h e patients. The estimated locations of t h e generators of interictal epileptiform signals correspond well t o the surrounding neural tissue of t h e lesions. Especially, t h e disappearance of t h e seizures in two operated patients after t h e removal of t h e astrocytomas verifies t h e results. The wavelet prefiltering facilitates t h e visual inspection and detection of t h e interictal epileptic spikes and t h e BEM based on a realistic head model gives a more accurate anatomical localization for the focus. A focal source model is widely accepted in many cases of epilepsy and the MUSIC scanning seems t o be very suitable for localizing these type of sources. K e y w o r d s a E G , Epilepsy, Dipole Source Localization, Wavelet Transform, Boundary Element Method, MUSIC
troencephalogram (EEG) is clinically very important for the diagnosis and treatment. Since these waveforms are usually spiky in time and focal in space, the inverse problem method called hKCIC [2] is a convenient technique for the type of signal to be localized. The forward problem can be accurately solved by a realistic head model using the BEhl [3]. The identificiation of the spike waveforms in the ongoing EEG is possible with a trained eye. However, wavelet prefiltering [4] as a preprocessing tool always facilitates the Yisual inspection and improves the detection performance. The EEG from three epileptic patients are analyzed using the wavelet prefiltering and SIUSIC scanning to localize the epileptic foci. BEN is used for the forward calculations. The BEhl is based on a human head model which is formed using the average human AlRI data released by the SINI.
11, THEORY The wavelet transform decomposes a signal in both time a,nd frequency [3] using fa,milies of functions
I. INTRODUCTION
F
OR the last three decades, the brain electrical activ-
ity researchers have developed several techniques to localize the electrical dipole sources in the 3-Dbrain structure [l]. This problem is formulated in two stages called the forward and the inverse problem. The former is to determine the electrical potentials (or magnetic currents) given the source location and strength parameters and the latter is to estimate these parameters from the measurements. Interestingly, the inverse problem always involves solving the forward problem for multiple times to determine the most optimum parameter estimates which will yield a minimum error between the observed and computed values. -4nother aspect of the problem is that there is not a single unique source configuration that will a give a certain measurement distribution. Therefore, the inverse approaches generally impose a constraint either geometrically or mathematically t o determine the most plausible solution. Localization of the interictal epileptic spikes in the Elec-
0-7803-7789-3/03/$17.00 02003 IEEE
that are obtained by dilations and translations of +(t). The wa.velet transform (of a. continuous signal x ( t ) can be defined as
where :.*" and "" being the complex conjugation and the inner product, respectively. If we have P different measurement sites in the conductor model for the potential (or the current), the forward problem can be formulated by
V(r) = H(r, si)m(r)
(2)
Here V is P x 1 electrical/magnetic field vector. m(r),is a 3 x 1 strength vector of the current dipole source located at r and H(r. si) is the f' x 3 dimensional transfer function
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which depends on the dipole location r, the measurement sites si and the geometrical and physical properties of the media. The forward problem is solved by the BEM which is a numerical approximation technique which partitions the surface of a volume conductor into closed triangular meshes. This technique has been used in dipole source localization of brain electromagnetic activity since the end of 80s [3]. The human head is modeled as three homogeneous conductor layers; the outermost surface being the boundary for the scalp, and the intermediate and the innermost being the one for the skull and the brain, respectively. In order to apply the BEM, a realistic head model has to be formed and its surfaces must be tesselated into triangles. The electrical potential V(r) at any surface point r can be represented by an integral equation
I 10
20
30 h t derlvdlv.
U)
n1t.r
S
M
IO
Fig. 1. The first derivative integer wavelet filter.
n is the unit normal vector t o the surface and V, is the potential in an infinite extent conductor with unit conductivity due to a primary source at r‘ with strength p 17x --
1 p(r - r’) -
47r )r - r’13
(4)
The conductivities outside and inside the surface S, are U: and uJ-. respectively. The conductor surfaces S, are approximated by small triangles. The surface integral in Eq. 3 reduces t o a sum and is evaluated over each triangle as a secondary source contribution to the potential. There are several methods for approximating the potential on the triangles like constant, linear or quadratic polynomial interpolation. By these approximations, Eq. 3 becomes a linear system of equations to be solved for the potentials [3]. The MUSIC scanning algorithm is chosen as a method for the inverse problem [2]. This method is based on subdividing the brain tissue into a 3-D grid and computing the spatial power spectrum with an eigenbased approach for each voxel element. In order to do this. the transfer function H in Eq.2 has to be computed numerically by using Eq. 3.
Fig. 2. Wavelet prefiltering to pronounce the location of spikes.
a
b
I I I. APPL IC AT I 0K The epileptic data is recorded from three different subjects from 19 electrode locations placed according t o the International 10-20 Electrode Placement system. The settings of the antialiasing filter are 0.05-70 Hz. A 32-channel AIedelec and a 32-channel Lallont EEG amplifiers are used for recording the EEG signals. The sampling rates
C
d
Fig. 3. The spike location of Case I. a) transversal b) sagittal c) coronal views d) original. transversal XIRI slice of the patient.
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a
come Institute is used for 3-D segmentation of the brain. The skull and scalp surfaces are segmented by appropriate thresholding and manual segmentation. After the 3-D segmentation. the surfaces are triangulated. For the BEN. the center of gravity method is used which is based on the assumption that the potential at the center of gravity is the same as the potential at everywhere on the triangle. The number of triangles used for the scalp. skull and brain surfaces are 1092, 1026. and 1104. respectively. For the inverse method of the dipole source localization. the AILSIC algorithm is used. The brain volume is scanned through voxels of size 8 < 8 x 8 mm.
b
11.. RESULTS X X D CONCLUSIOKS C
d
Fig. 4. The spike location of Case 11. a) transversal b) sagittal c) coronal views d) original transversal MRI slice of the patient.
a
b
The technique is tested on three clinical cases with secondary epileptic seizures based on morphological lesions that could be depicted in the anatomical AIR1 examinations of the patients. Two of the patients (cases I1 and 111) mere operated and became seizure-free after the removal of the tumors. The first case had a lesion on the left middle inferior temporal lobe. The EEG showed correspondingly interictal discharges on the left temporal area. Case I1 had a left perisvlvian mass that reached over to the hippocampus. Clinically, simple partial seizures were spreading from the left 1 emporal area. and a phase reversal could be observed at F7 in routine EEG recordings. The pathological evaluai.ion after the removal of the mass showed that it consisted of a diffuse astrocytoma. Case I11 had generalized convulsions during sleep. hIRI showed a left midle inferior temporal mass, that was diagnosed as astrocytoma in pathological evaluation. Routine clinical
EEG showed bitemporal abnormalities and hypersynchro-
C
d
Fig. 5 . The spike location of Case 111. a) transversal b) sagittal c) coronal views d) original transversal hIRI slice of the patient.
used are 236 Hz for hledelec and 200 Hz for the Lallont systems: respectively. The signals are sampled using a digitial to analog converter with 12-bit resolution. The integer spline wavelet called the 1st derivative filter is used [4]for preprocessing the epileptic EEG prior t o visual inspection. The shape of the filter is given in Fig. 1. The integer scale that determines the bandpass characteristics of the filter is optimized according t o the waveshape of the spike. Original and filtered waveforms of a typical multichannel EEG data are given in Fig. 2. The average T1 weighted human brain hIRI data obtained from 152 subjects and provided by the MY1 is used as a human head model. SPA199 software from Well-
nization in T 5 and F 7 . The results in Figs. 3-5 show that the estimated locations of the generators of interictal epileptiform signals correspond well to the surrounding neural tissue of the lesions. Especially, the disappearance of the seizures in two operated patients after the removal of the astrocytomas verifies the results. -4lthough our results on the event-related potentials
(ERPs) showed that the SIUSIC algorithm could not estimate reliable generators for the ERPs, it performed well in the case of epileptic E,EG. This should be attributed to the fact that the ERP generators are assumed to extend t o larger and multiple cortical patches rather than being pointwise activations. However. the epileptic activity better corresponds t o a such focal activation model. The application of the hfUSIC scanning for the solution of the inverse problem combined with wavelet prefiltering leads to plausible localization of epileptic foci on realistic head models which helps the diagnosis of the problem and the planning of the treatment.
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REFERENCES [l] 2. 3. Koles, “Trends in EEG source localization,” Electroenceph. Clin. Neurophysiol.? vol. 106, pp. 127-137, 1998. [2] J . C. hiosher P. S. Lewis and R. 41. Leahy, ”hlultiple dipole modeling and localization from spatio-temporal SIEG data,” IEEE Trans. Biomed. Eng.: vol. 39, pp. 541-557, 1992. [3] M. S. Hamalainen and J. Sarvas, “Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data?” IEEE Trans. Biomed. Eng., vol. 36, pp. 165-171, 1989. [4] 51. Unser, A. Aldroubi, and S. 3 . Schiff, ”Fast implementation of the continuous wavelet transform with integer scales,” IEEE Trans. Signal Proc., vol. 42, pp. 3519-3523, 1994. (51 A. Grossman and J. Morlet, “Decomposition of Hardy functions into square integrable wavelets of constant shape,” S I A M Journal of Mathematical Analysis, vol. 15, pp. 723-736, 1986.
ACKNOWLEDGMENTS lye would like to thank Dr. Olaf Steinstrater for providing us the electrode location coordinates on MY1 brain MRI data. and Dr. Cengizhan Ozturk for providing us the surface triangulation algorithm. This work is supported by Bogazici University Research Fund under the Project Code 99x01.
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