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Danny Flanagan

Publications and source records attributed to Danny Flanagan.

2 recordsLinked to original sources
PubMed2002

Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign.

PURPOSE: To determine the prevalence of heart rate changes and ECG abnormalities during epileptic seizures and to determine the timing of heart rate changes compared to the first electrographic and clinical signs. To assess the risk factors for the occurrence of ECG abnormalities. METHODS: We analyzed retrospectively 281 seizures in 81 patients with intractable epilepsy who had prolonged video-EEG and two-channel ECG. The nature and timing of heart rate changes compared to the electrographic and clinical seizure onset was determined. The ictal period (including one minute preictally and three minutes postictally) was analyzed for cardiac arrhythmias, conduction and repolarization abnormalities. Risk factors for cardiac abnormalities were investigated using parametric and non-parametric statistics. RESULTS: There was an increase in heart rate of at least 10 beats/minute in 73% of seizures (93% of patients) and this occurred most often around seizure onset. In 23% of seizures (49% of patients) the rate increase preceded both the electrographic and the clinical onset. ECG abnormalities were found in 26% of seizures (44% of patients). One patient had an asystole for 30 seconds. Long seizure duration increased the occurrence of ECG abnormalities. No other risk factor was found. CONCLUSIONS: Heart rate changes occur frequently and occur around the time or even before the earliest electrographic or clinical change. The change can clarify the timing of seizure onset and the specific rate pattern may be useful for seizure diagnosis and for automatic seizure detection. ECG abnormalities occur often and repeatedly in several seizures of the same patient.

Adult↗
PubMed2002

Computer-aided spatial classification of epileptic spikes.

The authors present a method that can be used to identify exemplar spikes from prolonged EEG recordings. To achieve this they have calculated single dipole source models for each automatically detected spike-like waveform. They used a dipole source algorithm that is computationally light and can be run on-line during EEG acquisition. Although a single dipole source model may not provide anatomically accurate information about the location of generators of all epileptiform abnormalities, it does provide a novel spatial parameter that may be useful in its own right. The authors use this spatial parameter and present the relative spatial density of the dipole locations in the form of three planar projections of the spherical model (a view from above, a view from the right, and a view from behind) and allow users to define the x-, y-, and z-coordinates of points of interest within the spherical model. They then present 10 example waveforms of events that have dipole source model locations that occur close to that seed coordinate. Overall, they found that this method performs very well for frequent events, but does not perform well for rare events or for diffuse EEG abnormalities.

Computer-Aided Design↗