The Influence of Age and Skull Conductivity on Surface and Subcutaneous Bipolar EEG Leads Katrina Wendel, Juho Väisänen, Jari Hyttinen, Jaakko Malmivuo Dept. of Biomedical Engineering, Tampere University of Technology, Tampere, Finland Correspondence: K. Wendel, Tampere University of Technology, Korkeakoulunkatu 3, P.O. Box 692, 33101 Tampere, Finland. E-mail:
[email protected], phone +358 44 5726658, fax+358 3 3115 2162
Abstract. Bioelectric measurements are influenced by the measurement location as well as the conductive properties of the tissue. Volume conductor effects such as the poorly conducting bones or the moderately conducting skin are known to affect the specificity and accuracy of the surface electroencephalography (EEG) measurements. This paper investigates the influence of age and skull conductivity upon surface and subcutaneous bipolar EEG measurement sensitivity. The subcutaneous electrodes are implanted on the skull beneath the skin, fat, and muscles. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV) and the region of interest sensitivity ratio (ROISR). The results indicate that subcutaneous implantation notably enhances the accuracy and specificity of EEG measurement compared to the surface measurement. The effect of studying the skull to scalp conductivity ratios of 5, 8, 15, and 30:1 indicate that the sensitivity and the specificity of the lead decrease as the skull conductivity decreases. Subcutaneous measurements enable specific monitoring of small source volumes in contrast to entire lobes of the brain such as the occipital or temporal lobes. The results of the study implicate that the applied resistivity ratio has minor effects on ratios between skalp and subcutaneous measurements. Furhermore in clinical practice the subcutaneous needle electrodes would provide more specific and accurate measurements of cortical activation than scalp measurements. Lastly, the evidence indicates that the younger the person is the better the accuracy and specificity of the surface and most especially the subcutaneous measurement leads. Keywords: Sensitivity distribution; lead field current density; ROISR; EEG; volume conductor
1. Introduction Clinical electroencephalography (EEG) and evoked potentials (EP) such as the visually evoked potentials (VEP) demand high signal-to-noise ratios (SNR), minimization of skin artifacts, and high sensitivity and specificity to name a few important criteria. Subcutaneous needle electrodes are commonly used in clinical electromyography (EMG), which are inserted into the muscles of interest. These minimally-invasive measurements offer higher SNRs with lower susceptibility of standard measurement artifacts when compared with traditional surface measurements. We believe that clinical EEG and EPs such as the VEP could adopt the subcutaneous measurement setup, thus placing the lead on the skull bypassing the artifact-prone skin. Our aim is to compare the bipolar subcutaneous EEG measurement with the well-documented surface electrode [Malmivuo and Plonsey, 1995; Malmivuo et al., 1997] and to investigate the influence of age [Wendel and Malmivuo, 2006]. In the present study we apply the concepts of the half-sensitivity volume (HSV) and region of interest sensitivity ratio (ROISR) in analyzing the effects of EEG electrode implantation on the measurement sensitivity distribution within the brain. We previously correlated the decrease in skull conductivity with age – most especially showing the difference between adolescence and adults, when the calvarial ossification process is completed [Hoekema et al., 2003; Moore and Dalley, 2005; Wendel and Malmivuo, 2006]. Specifically, we aim to identify the measurement sensitivity and specificity associated with age and electrode placement – on the scalp or subcutaneously on the skull.
2. Material and Methods The sensitivity distributions of measurement leads in an inhomogeneous volume conductor can be illustrated with reciprocal gradient potential fields i.e. current fields, as defined by [Plonsey, 1963]. The lead vectors define the relationship between the measured signal in the lead and the current sources in the volume conductor such that ;
(1)
, where VLE(x) is the voltage, e.g. measured ECG voltage, in lead x, H is the volume conductor, is the impressed source Φ y; x is the sensitivity vector of lead x in source location y, current density vector in source location y. The sensitivity distribution in the volume conductor can be established by applying the reciprocity theorem of Helmholtz with Poisson’s equation (Equation 2) applied to describe bioelectric quasistatic source-field problems. A source distribution, , containing only reciprocal source currents at the measurement electrodes raises a gradient potential distribution, , i.e. sensitivity. Φ
(2)
2.1. The Half Sensitivity Volume In [Malmivuo et al., 1997] the concept of half-sensitivity volume (HSV) was applied to define the volume in which the sensitivity of the measurement lead is concentrated. The HSV is the size of the volume within the source region of the volume conductor, where the magnitude of the sensitivity is at least half of its maximum value. The smaller the HSV is, the smaller the region from which the lead measures bioelectric activity. The half-sensitivity volume is thus applied to compare the ability to concentrate its measurement sensitivity. 2.2. The Region of Interest Sensitivity Ratio The concept introduced in [Väisänen et al., 2008] provides a parameter to analyze the specificity of a measurement system. Equation. 3 defines ROISR as a ratio between the average sensitivity of a predefined region-of-interest (ROI) volume and the average sensitivity in the rest of the source volume, hereafter called as a nonROI volume. |
ROISR |
| |
; ;
(3)
, HROI is the ROI source volume [cm3] and HnonROI is the nonROI source volume [cm3]. In the case of EEG the nonROI volume consists of the entire brain source volume excluding the ROI volume. ROISR thus defines how well the measurement sensitivity is concentrated within the selected ROI, i.e. how specific the measurement is to the signals generated within the ROI. In the present study the brain source volume, B, contains gray and white matter. We define ROI as the intersection between B and S, where S is a sphere with a 20 mm radius from the cortical electrode located on the occipital cortex surface (10/20 location, OZ) Consequently, our ROI contains both gray and white matter. We selected this location due to its relevance in visually evoked studies [Sörnmo and Laguna, 2005]. 2.3. Models and Computations We calculate the sensitivity distributions in a realistically shaped male head model based on the Visible Human Project man dataset [Sachse et al., 1998]. The tissues and their corresponding resistivities are listed in Table 1 [Ramon et al., 2006]. Calculations of the sensitivity distribution are based on the principle of reciprocity and the numerical FDM solution of EEG electrode sensitivity. In the present study we apply the scalp-to-skull resistivity ratios of 1:5, 1:8, 1:15 and 1:30 [Oostendorp et al., 2000; Wendel and Malmivuo, 2006]. We calculate the sensitivity distributions of the brain for bipolar electrode pair located on the scalp and the skull. The surface (i.e. scalp) and subcutaneous
(SubQ) electrodes measure 1 mm x 1 mm x 1 mm, which reflects the size of one pixel. Our bipolar leads reflect a visually evoked measurement over the occipital cortex (10/20 location OZ) referenced against an apex electrode (10/20 location CZ). A sagittal view of the model (Fig. 1.) shows the two bipolar EEG locations: surface i.e. on the scalp and subcutaneous i.e. on the skull.
Figure 1 Sagittal view of head model and electrode locations Table 1. Our realistic head model included these tissues, using the resistivity (Ωcm) listed [Ramon et al., 2006]. Tissue Resistivity (Ωcm) Tissue Resistivity (Ωcm) Bone marrow 2180 Scalp 230 Fat 2500 Eye 198 Skull/Bones 1150, 1840, 3450, 6900 Muscles 900 White matter 700 Blood 100 Grey matter 300 CSF 65 Other neural tissue 624
3. Results Figure 1 presents the sensitivity distributions of both scalp and subcutaneous leads with different resistivity ratios. Clearly, resistivity ratio has minor effect on the distribution compared to the implantation of the electrodes. Also the smearing effect of the scalp disappears with subcutaneous leads since the recording locations are closer to the target region (Fig.2). Tables 2 and 3 show that the subcutaneous lead's HSV decrease to nearly one-seventh, one-nineth, one-eighth and one-fourth the size of the scalp lead's HSV. Similarly, we find a 35% to 37% improvement in the subcutaneous lead's ROISR over the surface lead's ROISR. Fig. 2 illustrates that the subcutaneous measurement distributions on the cortical surface clearly concentrates the measurement sensitivity more efficiently to the target region as the skull conductivity increases. The smearing effect of the scalp is reduced with the subcutaneous leads, and nearly the entire scalp and skull smearing is eliminated when the skull has its highest conducting value. Precisely, the subcutaneous leads measure neuroelectric activity on or near the gyral cortical surface rather than sulcal or deep sources.
Figure 1. Sensitivity distributionsin logarithmic scale for surface leads(a-d) and subQ leads(e-h). Resistivity ratios 1:5 (a,e), 1:8 (b,f), 1:15 (c.g),1:30(d,h).
Table 2. Results of the visually evoked bipolar measurement for the surface. All parameters within grey and white matter. scalp 1/5 1/8 1/15 1/30 Max sens. 0.42 0.45 0.38 0.34 HSV 4999 5239 4002 2446 ROISR 2.41 2.29 2.06 1.77 Table 3. Results of the visually evoked bipolar measurement for the subcutaneous (SubQ) leads. All parameters within grey and white matter. subQ 1/5 1/8 1/15 1/30 Max sens. 0.85 0.83 0.72 0.54 HSV 706 585 516 610.00 ROISR 3.26 3.07 2.76 2.42
4. Discussion In the past studies we have correlated aging with decreasing skull conductivity. Physiologists report that the calvarial bone completes the ossification process between the ages of 18 and 20 [Moore and Dalley, 2005]; therefore, we find the largest change in conductivity after adolescence. The present study shows that the ratio between scalp and subcutaneous measurements in HSV is smallest with the lowest skull resistivity. Correlation indicates that measurements will be more localized i.e. increased sensitivity, with higher specificity. At present there is no data to correlate the change in skin conductivities with age although we speculate the aging process of the skin slows during childhood before adolescence after the rapid growth phases of the body have been completed. Therefore, the skin conductivity from adolescents onwards should not affect this study. In the previous studies [Malmivuo et al., 1997] with spherical models the HSV and maximum sensitivities have linearly changed due to the change of resistivity ratio. It is surprising to see that the realistic model changes this. It can be seen that in surface case when resistivity ratio is changed from 1/5 to 1/8 the HSV is increased although in other cases is decreased. These results show that the multiple models should be applied when these phenomena are studied. As a conclusion the implantation of EEG electrode on the skull increase the sensitivity and specificity of EEG measurement. The scalp/skull resistivity ratio and individual geometry determine how large is the improvement gained with the implantation. References Hoekema R., Wieneke G.H., Leijten F.S.S., van Veelen C.W.M., van Rijen P.C., Huiskamp G.J.M., Ansems J., and van Huffelen A.C. "Measurement of the conductivity of skull, temporarily removed during epilepsy surgery," Brain Topogr, vol. 16, pp. 29-38, 2003. Malmivuo J, Plonsey R. Bioelectromagnetism: Principles and Application of Bioelectric and Biomagnetic Fields. Oxford University Press, New York, 1995. Malmivuo J., Suihko V., Eskola H. March 1997Sensitivity distributions of EEG and MEG measurements. IEEE Trans. on Biomed. Eng. 44:196–208. Moore K.L. and Dalley A.F., Clinically Oriented Anatomy, 5th ed, 2005. Oostendorp T.F., Delbeke J., Stegeman D.F.. December 2000 The Conductivity of the Human Skull: Results of In Vivo and In Vitro Measurements. IEEE Trans. on Biomed. Eng. 47:1487–1492. Plonsey R.. (1963) Reciprocity applied to volume conductors and the EEG. IEEE Trans. Biomed. Electron. 10:9–12. Ramon C., Schimpf P.H., Haueisen J. February 2006Influence of head models on EEG simulations and inverse source localizations. BioMedical Engineering Online . Sachse F.B., Werner C.D., Meyer-Waarden K., Dössel O.. (1998). Applications of the Visible man dataset in electrocardiology: Calculation and visualization of body surface potential maps of a complete heart cycle. Proc. of the Second Users Conference of the National Library of Medicine’s Visible Human Project, 47–48. Sörnmo L., Laguna P. Bioelectrical Signal Processing in Cardiac and Neurological Applications. Academic Press, 2005. Väisänen J., Väisänen O., Malmivuo J., Hyttinen J.. (2008) New Method for Analysing Sensitivity Distributions of Bioelectric Measurements. Med. Biol. Eng. Comput. 46:101–108. Wendel K. and Malmivuo J., “Correlation between live and post mortem skull conductivity measurements,” in 28th Ann. Int. Conf. IEEE Eng. Medicine and Biology Soc., Aug. 2006. Wendel K., Narra N.G., Hannula M., Kauppinen P., Malmivuo J. The Influence of CSF on EEG Sensitivity Distributions of Multilayered Head Models. IEEE Transactions on Biomedical Engineering, April, 2008, 55:1454-1456.
