Use this URL to cite or link to this record in EThOS:
Title: Neural anomalies monitoring : applications to epileptic seizure detection and prediction
Author: Juffali, Walid
ISNI:       0000 0004 2732 0346
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2012
Availability of Full Text:
Access from EThOS:
Access from Institution:
There have been numerous efforts in the field of electronics with the aim of merging the areas of healthcare and technology in the form of low power, more efficient hardware. However one area of development that can aid in the bridge of healthcare and emerging technology is in Information and Communication Technology (ICT). Here, databasing and analysis systems can help bridge the wealth of information available (blood tests, genetic information, neural data) into a common framework of analysis. Also, ICT systems can integrate real-time processing from emerging technological solutions, such as developed low-power electronics. This work is based on this idea, merging technological solutions in the form of ICT with the need in healthcare to identify normality in a patients’ health profile. In this work we develop this idea and explain the concept more thoroughly. We then go on to explore two applications under development. The first is a system designed around monitoring neural activity and identifying, through a processing algorithm, what is normal activity, such that we can identify anomalies, or abnormalities in the signal. We explore Epilespy with seizure detection and prediction as an application case study to show the potential of this method. The motivation being that current methods of prediction have proven to be unsuccessful. We show that using our algorithm we can achieve significant success in seizure prediction and detection, above and beyond current methods. The second application explores the link between genetic information and standard tests (blood, urine etc.) and how they link in together to define a personalised benchmark. We show how this could work and the steps that have been made towards developing such a database.
Supervisor: Toumazou, Christofer Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral