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Objective: Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels.
Methods: Fifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model).
Results: The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours.
Significance: Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.
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http://dx.doi.org/10.1111/epi.16470 | DOI Listing |
IEEE J Biomed Health Inform
August 2025
Automated emotion identification via physiological data from wearable devices is a growing field, yet traditional electroencephalography (EEG) and photoplethysmography (PPG) collection methods can be uncomfortable. This research introduces a novel structure of the in-ear wearable device that captures both PPG and EEG signals to enhance user comfort for emotion recognition. Data were collected from 21 individuals experiencing four emotional states (fear, happy, calm, sad) induced by video stimuli.
View Article and Find Full Text PDFSci Data
July 2025
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001, Leuven, Belgium.
The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices hold significant potential for developing automated seizure detection frameworks. In this work, we present SeizeIT2, the first open dataset of wearable data recorded in patients with focal epilepsy.
View Article and Find Full Text PDFJ Clin Neurophysiol
May 2025
Epilepsy Center, Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and.
Purpose: Outpatient seizure monitoring is crucial for optimizing treatment strategies in epilepsy; however, traditional approaches such as seizure diaries and wearables have limitations in accuracy and practicality. This study evaluated the adherence and utility of an implanted subcutaneous EEG monitoring system in patients with focal temporal lobe epilepsy.
Methods: At a tertiary epilepsy center, patients with focal epilepsy received a subcutaneous two-channel EEG system for ultra-long-term monitoring.
IEEE J Biomed Health Inform
March 2025
The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neural networks, taking advantage of EEG's temporal resolution and the computational capabilities of embedded neural networks. The proposed system utilizes behind-the-ear (BTE) EEG signals and on-chip neural networks for mental stress detection. A wearable custom-designed device captures EEG signals from a single BTE channel, performs on-chip signal-to-spectrogram conversion, and integrates a compact convolutional neural network (CNN) for stress classification.
View Article and Find Full Text PDFPLoS One
July 2024
Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models.
View Article and Find Full Text PDF