Biomedical Engineering - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/365

Filters

2015 2
Reset filters

Settings
Statistics
Citations
Author: Freestone, Dean × End date: 2015 × Start date: 2015 ×

Search Results

Now showing 1 - 2 of 2
  • Item
    Thumbnail Image
    Analytic synchronization conditions for a network of Wilson and Cowan oscillators
    Ahmadizadeh, S ; Nesic, D ; Grayden, DB ; Freestone, DR (IEEE, 2015)
    We investigate the problem of synchronization in a network of homogeneous Wilson-Cowan oscillators with diffusive coupling. Such networks can be used to model the behavior of populations of neurons in cortical tissue, referred to as neural mass models. A new approach is proposed to address local synchronization for these types of neural mass models. By exploiting the linearized model around a limit cycle, we analyze synchronization within a network for weak, intermediate, and strong coupling. We use two-time scale averaging and the Chetaev theorem to analytically check the absence or presence of synchronization in the network with weak coupling. We also utilize the Chetaev theorem to analytically prove synchronization death in a network with strong coupling. For intermediate coupling, we use a recently proposed numerical approach to prove synchronization in the network. Simulation results confirm and illustrate our results.
  • Item
    Thumbnail Image
    Seizure Prediction: Science Fiction or Soon to Become Reality?
    Freestone, DR ; Karoly, PJ ; Peterson, ADH ; Kuhlmann, L ; Lai, A ; Goodarzy, F ; Cook, MJ (SPRINGER, 2015-11)
    This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.