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Objective
Home monitoring of 3‐Hz spike–wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment.
Methods
Thirteen participants (median age = 22 years, 11...
Objective
Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug‐resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of “electrode reconstruction,” which involves the labeling, registration, and assignment of iEEG electrode coordinates...
Objective
To deconstruct the epileptogenic networks of patients with drug‐resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.
Methods
We analyzed scalp EEG from 50 patients with DRE who had surgery...
Objective
Structural–functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features.
Methods
This study analyzed presurgical diffusion and functional magnetic resonance imaging data...
Computer vision (CV) shows increasing promise as an efficient, low‐cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases...
Objective
Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm.
Methods
We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the...
Objective
The aim of this study was to develop a machine learning algorithm using an off‐the‐shelf digital watch, the Samsung watch (SM‐R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy.
Methods
This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained...
Objective
This study was undertaken to develop and evaluate a machine learning‐based algorithm for the detection of focal to bilateral tonic–clonic seizures (FBTCS) using a novel multimodal connected shirt.
Methods
We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video‐electroencephalographic...
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