Daniel Kelleher

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  • Ying Jiang, Chris Kaiser, Duane Jenness, Kendall Knight, Mary Munson, Reid Gilmore +8 others
  • 2015
ACKNOWLEDGEMENT I would like to express my deepest gratitude to my mentor, Dr. Reid Gilmore. His insightful advice and constant encouragement guided me through my graduate research. I was always inspired by his intelligence and dedication to the work. I want to thank specially my husband, Zhiliang Cheng, for his love and support. He has been my best friend(More)
The EEG signal is very often contaminated by electrical activity external to the brain. These artefacts make the accurate detection of epileptiform activity more difficult. A scheme developed to improve the detection of these artefacts (and hence epileptiform event detection) is introduced. A structure of parallel Support Vector Machine classifiers is(More)
Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work(More)
— Ambulatory EEG requires signal processing algorithms which are efficient in terms of how much power they use in their computation, while at the same time providing accurate decision-making capabilities. Two methods of achieving this are to downsample the EEG and to perform bitwidth reduction on the data. The effect of performing both of these techniques(More)
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