A case of atypical absence seizures after electroconvulsive treatment in a patient with treatment-resistant depression

Tardive Seizures After Electroconvulsive Therapy

OBJECTIVES

Seizures that occur spontaneously after termination of an electroconvulsive therapy (ECT) seizure are termed tardive seizures. They are thought to be a rare complication of ECT, influenced by risk factors that affect seizure threshold. However, there has been limited review of tardive seizures with modified ECT. We aimed to review the literature to provide clinical guidance for the use of ECT after tardive seizures.

METHODS

PubMed, EMBASE, PsycInfo, and CINAHL databases were searched from inception to May 2021 to identify cases of modified ECT, with evidence of a seizure occurring within 7 days of a terminated ECT seizure. Data for demographic, medical, pharmacological, anesthetic, and ECT variables as well as management strategies were collected.

RESULTS

There have been 39 episodes of modified ECT-related tardive seizures published over a period of 40 years. In 97.4% of cases, there was at least 1 identified potential risk factor for seizures, including use of a seizure-lowering medication and/or preexisting neurological injury. Major complications were uncommon (<15% of cases); however, 1 fetal death and 1 subsequent suicide were reported. No case was diagnosed with epilepsy, although around 20% continued on antiepileptic medications. More than half of the included patients were retrialed on ECT, with only 15% developing further tardive seizures.

CONCLUSIONS

Seizures that occurred spontaneously after the termination of an ECT seizure are a rare complication of modified ECT. Recommencing ECT after a tardive seizure may occur after review of modifiable seizure risk factors and with consideration of antiepileptic medication and extended post-ECT monitoring.

Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases

We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguish between different activity states in human absence epilepsy. EEG recordings from 10 patients with absence epilepsy are sampled, filtered, selected, and dissected from seizure-free, preseizure, and seizure phases. A total of 112 two-second 19-channel EEG epochs from 10 patients were selected from each phase. For each epoch, multiscale permutation entropy of the EEG data was calculated. The feature dimensionality was reduced by linear discriminant analysis to obtain a more discriminative and compact representation. Finally, a relevance feedback technique, that is, direct biased discriminant analysis, was applied to 68 randomly selected queries over nine iterations. This study is a first attempt to apply the statistical analysis of relevance feedback to the distinction of different EEG activity states in absence epilepsy. The average precision in the top 10 returned results was 97.5%, and the standard deviation suggested that embedding relevance feedback can effectively distinguish different seizure phases in absence epilepsy. The experimental results indicate that relevance feedback may be an effective tool for the prediction of different activity states in human absence epilepsy. The simultaneous analysis of multichannel EEG signals provides a powerful tool for the exploration of abnormal electrical brain activity in patients with epilepsy.