title: Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring.
abstract: High quality, wireless ambulatory EEG (AEEG) systems that can operate over extended periods of time are not currently feasible due to the high power consumption of wireless transmitters. Previous work has thus proposed data reduction by only transmitting sections of data that contain candidate epileptic activity. This paper investigates algorithms by which this data selection can be carried out. It is essential that the algorithm is low power and that all possible features are identified, even at the expense of more false detections. Given this, a brief review of spike detection algorithms is carried out with a view to using these algorithms to drive the data reduction process. A CWT based algorithm is deemed most suitable for use and an algorithm is described in detail and its performance tested. It is found that over 90% of expert marked spikes are identified whilst giving a 40% reduction in the amount of data to be transmitted and analysed. The performance varies with the recording duration in response to each detection and this effect is also investigated. The proposed algorithm will form the basis of new a AEEG system that allows wireless and longer term epilepsy monitoring.
authors.affiliation: Electrical and Electronic Engineering Department, Imperial College London, SW7 2AZ. alexander.casson06@imperial.ac.uk
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Casson
authors.forename: David C
authors.initials: DC
authors.lastname: Yates
authors.forename: Shyam
authors.initials: S
authors.lastname: Patel
authors.forename: Esther
authors.initials: E
authors.lastname: Rodriguez-Villegas
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003195:Computer Communication Networks; D003201:Computers; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000628:therapy*; D004867:Equipment Design; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D009462:Neurology / Q000379:methods; D015588:Observer Variation; D012640:Seizures / Q000517:prevention & control; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013997:Time Factors