CHARACTERISTIC ABNORMALITIES IDENTIFIED USING AUTOMATED ANALYSIS OF ECG DURING SEIZURES
Abstract number :
1.120
Submission category :
3. Neurophysiology
Year :
2013
Submission ID :
1750719
Source :
www.aesnet.org
Presentation date :
12/7/2013 12:00:00 AM
Published date :
Dec 5, 2013, 06:00 AM
Authors :
D. Goldenholz, M. Seyal
Rationale: Prior investigation has shown evidence that about 1/3 of seizures result in oxygen desaturations. Prolonged and shortened QTc and reduced heart-rate variability occur with seizures. Some of these ECG changes are associated with peri-ictal hypoxemia. These changes have been shown to increase non-seizure related cardiac mortality and may also be relevant to the pathophysiology of sudden unexpected death in epilepsy (SUDEP). Patients with refractory convulsive seizures are at increased risk for SUDEP. It would be valuable to develop techniques for rapid identification of potentially life-threatening peri-ictal ECG abnormalities during inpatient video-EEG telemetry (VET). This may help with identification of patients at increased risk for SUDEP. Methods: Data were analyzed in 273 seizures (224 clinical and 49 electrographic) from 26 patients in the epilepsy monitoring unit with confirmed temporal lobe epilepsy. We employed validated automated ECG segmentation software and computed the corrected QT intervals (QTc) as well as instantaneous heart rates.Results: With these seizures, a pattern was noted at the time of seizures involving pulse-oximeter desaturation (59%), increase in baseline heart rate (61%), increase in baseline QTc (45%), and increase in QTc variability (45%). These findings would occur often together (31%). Conclusions: Using straightforward automated analytical techniques applied to the ECG channels acquired during VET, patients and seizures can be rapidly stratified into potential risk categories on the basis of identification of key abnormal ECG patterns. This stratification may help identify patients at risk for SUDEP.
Neurophysiology