Imaging of focal seizures with Electrical Impedance Tomography and depth electrodes in real time.

Intracranial EEG is the current gold standard technique for localizing seizures for surgery, but it can be insensitive to tangential dipole or distant sources. Electrical Impedance Tomography (EIT) offers a novel method to improve coverage and seizure onset localization. The feasibility of EIT has been previously assessed in a computer simulation, which revealed an improved accuracy of seizure detection with EIT compared to intracranial EEG. In this study, slow impedance changes, evoked by cell swelling occurring over seconds, were reconstructed in real time by frequency division multiplexing EIT using depth and subdural electrodes in a swine model of epilepsy. EIT allowed to generate repetitive images of ictal events at similar time course to fMRI but without its significant limitations. EIT was recorded with a system consisting of 32 parallel current sources and 64 voltage recorders. Seizures triggered with intracranial injection of benzylpenicillin (BPN) in five pigs caused a repetitive peak impedance increase of 3.4 ± 1.5 mV and 9.5 ± 3% (N =205 seizures); the impedance signal change was seen already after a single, first seizure. EIT enabled reconstruction of the seizure onset 9 ± 1.5 mm from the BPN cannula and 7.5 ± 1.1 mm from the closest SEEG contact (p<0.05, n =37 focal seizures in three pigs) and it could address problems with sampling error in intracranial EEG. The amplitude of the impedance change correlated with the spread of the seizure on the SEEG (p <<0.001, n =37). The results presented here suggest that combining a parallel EIT system with intracranial EEG monitoring has a potential to improve the diagnostic yield in epileptic patients and become a vital tool in improving our understanding of epilepsy.

Feasibility of imaging epileptic seizure onset with EIT and depth electrodes.

Imaging ictal and interictal activity with Electrical Impedance Tomography (EIT) using intracranial electrode mats has been demonstrated in animal models of epilepsy. In human epilepsy subjects undergoing presurgical evaluation, depth electrodes are often preferred. The purpose of this work was to evaluate the feasibility of using EIT to localise epileptogenic areas with intracranial electrodes in humans. The accuracy of localisation of the ictal onset zone was evaluated in computer simulations using 9M element FEM models derived from three subjects. 5 mm radius perturbations imitating a single seizure onset event were placed in several locations forming two groups: under depth electrode coverage and in the contralateral hemisphere. Simulations were made for impedance changes of 1% expected for neuronal depolarisation over milliseconds and 10% for cell swelling over seconds. Reconstructions were compared with EEG source modelling for a radially orientated dipole with respect to the closest EEG recording contact. The best accuracy of EIT was obtained using all depth and 32 scalp electrodes, greater than the equivalent accuracy with EEG inverse source modelling. The localisation error was 5.2 ±â€¯1.8, 4.3 ±â€¯0 and 46.2 ±â€¯25.8 mm for perturbations within the volume enclosed by depth electrodes and 29.6 ±â€¯38.7, 26.1 ±â€¯36.2, 54.0 ±â€¯26.2 mm for those without (EIT 1%, 10% change, EEG source modelling, n = 15 in 3 subjects, p < 0.01). As EIT was insensitive to source dipole orientation, all 15 perturbations within the volume enclosed by depth electrodes were localised, whereas the standard clinical method of visual inspection of EEG voltages, only localised 8 out of 15 cases. This suggests that adding EIT to SEEG measurements could be beneficial in localising the onset of seizures.

Simultaneous EIT and EEG using frequency division multiplexing.

OBJECTIVE: Methods have previously been reported for simultaneous EIT and EEG recording, but these have relied on post-hoc signal processing to remove switching artefacts from the EEG signal and require dedicated hardware filters and the use of separate EEG and EIT electrodes. This work aims to demonstrate that an uncorrupted EEG signal can be collected simultaneously with EIT data by using frequency division multiplexing (FDM), and to show that the EIT data provides useful information when compared to EEG source localisation. APPROACH: A custom FDM EIT current source was created and evaluated in resistor phantom and neonatal head tank experiments, where a static and dynamic perturbation was imaged. EEG and EIT source localisation were compared when an EEG dipole was placed in the tank. EEG and EIT data were collected simultaneously in a human volunteer, using both a standard EEG and a visual evoked potential (VEP) paradigms. MAIN RESULTS: Differences in EEG and VEP collected with and without simultaneous EIT stimulation showed no significant differences in amplitude, latency or PSD (p-values >0.3 in all cases). Compared with EEG source localisation, EIT reconstructions were more accurately able to reconstruct both the centre of mass and volume of a perturbation. SIGNIFICANCE: The reported method is suitable for collecting EIT in a clinical setting, without disrupting the clinical EEG or requiring additional measurement electrodes, which lowers the barrier to entry for data collection. EIT collection can be integrated with existing clinical workflows in EEG/ECoG, with minimal disruption to the patient or clinical team.

Multi-frequency electrical impedance tomography and neuroimaging data in stroke patients.

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique, which has the potential to expedite the differentiation of ischaemic or haemorrhagic stroke, decreasing the time to treatment. Whilst demonstrated in simulation, there are currently no suitable imaging or classification methods which can be successfully applied to human stroke data. Development of these complex methods is hindered by a lack of quality Multi-Frequency EIT (MFEIT) data. To address this, MFEIT data were collected from 23 stroke patients, and 10 healthy volunteers, as part of a clinical trial in collaboration with the Hyper Acute Stroke Unit (HASU) at University College London Hospital (UCLH). Data were collected at 17 frequencies between 5 Hz and 2 kHz, with 31 current injections, yielding 930 measurements at each frequency. This dataset is the most comprehensive of its kind and enables combined analysis of MFEIT, Electroencephalography (EEG) and Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) data in stroke patients, which can form the basis of future research into stroke classification.

Optimisation of current injection protocol based on a region of interest.

OBJECTIVE: Electrical impedance tomography has the potential to image fast neural activity associated with physiological or epileptic activity throughout the brain. These applications pose a particular challenge as expected voltage changes on the electrodes are less than 1% and geometrical constraints of the body under investigation mean that electrodes can not be evenly distributed around its boundary. Unlike other applications, however, information regarding the location of expected activity is typically available. An informative method for choosing current paths that maximise sensitivity to specific regions is desirable. APPROACH: Two electrode addressing protocol generation methods based on current density vectors concentrated in a region of interest have been proposed. One focuses solely on maximising its magnitude while the other considers its distribution. The quality of reconstructed images using these protocols was assessed in a simulation study conducted in a human and rat mesh and compared to the protocol that maximises distance between injecting electrodes. MAIN RESULTS: When implementing the protocol that focused on maximising magnitude, the current density concentrated in a region of interest increased by up to a factor of 3. When the distribution of the current was maximised, the spread of current density vectors increased by up to fivefold. For the small conductivity changes expected in the applications explored, image quality was best when implementing the protocol that maximised current density. The average image error when using this protocol was 7% better than when employing other protocols. SIGNIFICANCE: We conclude that for fast neural EIT applications, the protocol that maximises current density is the best protocol to implement.