2006 International Conference of the IEEE Engineering in Medicine and Biology Society 2006
DOI: 10.1109/iembs.2006.259694
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Detection of High Frequency Oscillations with Teager Energy in an Animal Model of Limbic Epilepsy

Abstract: High frequency oscillations (HFO) in limbic epilepsy represent a marked difference between abnormal and normal brain activity. Faced with the difficult of visually detecting HFOs in large amounts of intracranial EEG data, it is necessary to develop an automated process. This paper presents Teager Energy as a method of finding HFOs. Teager energy is an ideal measure because unlike conventional energy it takes into account the frequency component of the signal as well as signal amplitude. This greatly aids in th… Show more

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Cited by 17 publications
(8 citation statements)
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“…To analyze HFOs over longer periods, an automatic detection of the oscillations would be necessary. Methods to detect distinct HFOs have been proposed (Staba et al., 2002; Nelson et al., 2006; Gardner et al., 2007), but we did not find them adequate for our requirements; an automatic detector should not only be able to detect HFOs within variable baselines, but also to distinguish between HFOs co‐occurring with spikes and those outside spikes. Visual analysis and manual marking of HFOs in this study had two advantages over automated methods.…”
Section: Discussionmentioning
confidence: 96%
“…To analyze HFOs over longer periods, an automatic detection of the oscillations would be necessary. Methods to detect distinct HFOs have been proposed (Staba et al., 2002; Nelson et al., 2006; Gardner et al., 2007), but we did not find them adequate for our requirements; an automatic detector should not only be able to detect HFOs within variable baselines, but also to distinguish between HFOs co‐occurring with spikes and those outside spikes. Visual analysis and manual marking of HFOs in this study had two advantages over automated methods.…”
Section: Discussionmentioning
confidence: 96%
“…Given that HFOs are short oscillatory events that stand out from the baseline, a logical approach to identify them is applying an energy‐based detector. Automatic detection methods are being developed based on the comparison of the local energy of the signal with the whole EEG epoch (including HFOs or with marked baseline segments 11, 24, 64–67. When comparing the performance of these detectors, the behavior is similar in channels where HFOs are rare events that can be clearly distinguished from the surrounding background.…”
Section: How Can Hfos Be Visualized and Detected?mentioning
confidence: 99%
“…As automatic detection programs are developed, longer time frames can be studied. 5, 38 We chose not to use automatic detection because it has not been validated enough (on our data) and because separation of HFOs with and without spikes by such methods is not possible yet. Second, the limited number of patients did not allow distinction between different kinds of epilepsies (different locations or etiologies) and medication types.…”
mentioning
confidence: 99%