Can Artificial Intelligence Diagnose Transient Global Amnesia Using Electroencephalography Data?

ABSTRACT:

Background and purpose

This study aimed to determine the ability of deep learning using convolutional neural networks (CNNs) to diagnose transient global amnesia (TGA) based on electroencephalography (EEG) data, and to differentiate between patients with recurrent TGA events and those with a single TGA event.

Methods

We retrospectively enrolled newly diagnosed patients with TGA and healthy controls. All patients with TGA and the healthy controls underwent EEG. The EEG signals were converted into images using time-frequency analysis with short-time Fourier transforms. We employed two CNN models (AlexNet and VGG19) to classify the patients with TGA and the healthy controls, and for further classification of patients with recurrent TGA events and those with a single TGA event.<

SUBMITTER: Seo YD 

PROVIDER: S-EPMC9833880 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature