Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

ABSTRACT: While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the development of novel ECT seizure quality metrics and treatment guidance systems in particular will require cutting-edge digital seizure analysis. Using artificial intelligence will offer more analytical degrees of freedom and could play a key role in enhancing the precision of currently available procedures. To this end, we developed the first machine learning (ML) framework that can classify ictal and non-ictal EEG segments, accurately identifying seizure endpoints-a critical step in deriving seizure quality parameters-and computing these metrics at lea

SUBMITTER: Kayser M 

PROVIDER: S-EPMC11845479 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature