A new algorithm for automatically detecting epileptiform spikes and its application in epilepsy models

Abstract

Epilepsy is characterized by abnormally synchronized firing of neuronal populations, which is presented as epileptiform spikes in neural electrical signal recordings. In order to investigate the epileptiform spikes quantitatively, we designed a new window-based algorithm to automatically detect population spikes (PS) in acute epilepsy models in rat hippocampus CA1 region, and to calculate characteristic parameters of PS. Results show that the algorithm could recognize PS waveforms directly in wideband recording signals in epilepsy models induced by 4-aminopyridine (4-AP), a potassium channel blocker, or by picrotoxin (PTX), an antagonist of γ-aminobutyric acid A-type receptor. The PS detection ratios of the two epilepsy models were 94.2%±1.6% ( n=11) and 95.9%±1.9% ( n=12), respectively. The false positive ratios were 3.5%±2.3% ( n=11) and 4.8%±2.3% ( n=12), which were significantly lower than those of the conventional threshold method. Comparisons of the PS patterns between the 4-AP model and the PTX model showed that the PS of the 4-AP model had wider waveforms and fired more dispersedly with intervals mainly in the range of 100-700 ms. The PS of the PTX model fired as Burst with a higher firing rate and with intervals mainly in the range of 2-20 ms, resulting in a larger sum of spike amplitudes per second than the 4-AP model. Thus, the synchronous firing of neuronal populations in the PTX model was more intense than that in the 4-AP model. In conclusion, the new algorithm of PS detection can correctly detect and analyze epileptiform population spikes. It provides a useful tool of data analysis for investigating the underlying mechanism of seizure generation and for evaluating new therapeutics of epilepsy.