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Details of Grant 

EPSRC Reference: EP/K005510/1
Title: Sparse Multi-way Digital Signal Processing Approach for Detection of Deep Medial Temporal Discharges from Scalp EEG
Principal Investigator: Sanei, Dr S
Other Investigators:
Valentin, Dr A Alarcon, Dr G Jin, Professor Y
Researcher Co-Investigators:
Project Partners:
Department: Computing Science
Organisation: University of Surrey
Scheme: Standard Research
Starts: 26 June 2013 Ends: 30 June 2016 Value (£): 316,486
EPSRC Research Topic Classifications:
Medical Imaging Medical science & disease
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Nov 2012 Engineering Prioritisation Meeting - 9 Nov 2012 Announced
Summary on Grant Application Form
Detection of deep brain medial temporal discharges is extremely crucial for early detection of epilepsy and plan for a surgical operation to remove very small regions of the brain to avoid recurrent or progress of such very common neurological condition. Currently, this affects more than 1% of the UK population. In addition to clinical history, the scalp EEG is the most popular and accepted test to support the diagnosis of epilepsy. Unfortunately, such discharges (spikes) cannot be seen from the scalp EEG due to their relatively low sensitivity. 45% of awake EEGs and 20% of sleep EEGs recorded from people with epilepsy do not show clear epileptiform abnormalities. This leaves a significant proportion with uncertain diagnosis and delayed treatment. However, although many epileptiform discharges cannot be detected on the scalp EEG, they can be recorded using intracranial EEG electrodes implanted in deep brain structures. A non-invasive method for increasing the probability of detection of epileptiform discharges will be therefore vary crucial and valuable to increase the diagnostic power of scalp EEG.

Detection of deep brain discharges using scalp EEG recordings using advanced digital signal processing (DSP) hasn't been much explored in the literature.

In this proposal new algorithms will be developed to initially use a set of previously recorded data (in their so called training phase) to best model the neural pathways from deep medial temporal source to scalp potential patterns. Solving this problem, we can then perform separation of the weak spikes from noise-like scalp signals, and localize the sources. In this direction, the major problems are nonlinearity of the medium and interference of the cortical potentials which are usually recognised as the scalp EEG of a normal brain.

A large set of simultaneous scalp and intracranial EEG data using Foramen Ovale (FO) electrodes was collected from more than twenty patients have been recorded and analysed. Using some simple methods the signal-to-noise ratio (SNR) was increased by averaging the data over a number of trials synchronized on discharges using intracranial recording. It has been reported by providing many evidences that:

a) Before averaging only 9% of the discharges were detectable when only scalp recordings were used.

b) The majority of the spikes (up to 72.3%) could be detected by using both intracranial and scalp EEGs particularly after averaging.

c) In 18.7% of discharges no scalp signal was observed even after averaging.

From this unique and clinically important setup and the outcome of their analyses it is evident that interictal medial temporal epileptiform discharges, originating from deep medial temporal structure (MTS), can hardly be detected by visual inspection of the scalp signals. On the other hand, intracranial recording is a very inconvenient process and costly to the patients requiring hazardous and timely surgical operations.

From signal processing point of view, the deep sources are sparse, the medium is nonlinear, and the interfering signals are correlated and nonstationary. On the other hand, the number of sources is potentially larger than the number of electrodes which makes the overall system underdetermined.

In this proposal, to identify the underdetermined system when the sources are sparse and nonstationary, we will develop a sparse tensor factorization algorithm. The statistical (such as sparsity), geometrical (such as approximate locations), and physiological a priories (nature/shape of sources and artefacts) will be incorporated into the formulation as constraint terms. Finally, such a multiple constraint problem will be solved by developing a global optimization method.

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