AIMC Topic: Scalp

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A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

Scientific reports Feb 17, 2025
In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms h...
Female Adult Male Neural Networks, Computer Algorithms Epilepsy Signal Processing, Computer-Assisted Scalp Deep Learning Humans Electroencephalography
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Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 29, 2024
Seizure localization is important for managing drug-resistant focal epilepsy. Here, the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging seizure activities from electroencephalogram (EEG) recordings in drug-res...
Computer Simulation Female Adult Humans Neural Networks, Computer Seizures Scalp Epilepsies, Partial Drug Resistant Epilepsy Deep Learning Male Electroencephalography Middle Aged
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A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.

IEEE transactions on bio-medical engineering May 20, 2024
OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment ...
Signal Processing, Computer-Assisted Pain Scalp Pain Measurement Electroencephalography Male Deep Learning Child, Preschool Child Adolescent Humans Female
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Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 19, 2024
Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have diff...
Epilepsy Reproducibility of Results Scalp Humans Deep Learning Electroencephalography
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Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.

Journal of biomedical optics Jan 27, 2024
SIGNIFICANCE: Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelat...
Regional Blood Flow Scalp Hemodynamics Spectroscopy, Near-Infrared Deep Learning
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Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

Medical physics Nov 13, 2023
BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used...
Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Scalp
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Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.

Scientific reports Apr 25, 2023
Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalogr...
Epilepsies, Partial Humans Epilepsy Deep Learning Scalp Electroencephalography
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Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 27, 2022
Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for au...
Epilepsy Scalp Deep Learning Humans Electroencephalography Neural Networks, Computer
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Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Journal of neural engineering Oct 19, 2022
Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED...
Neural Networks, Computer Reproducibility of Results Scalp Electroencephalography Deep Learning
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Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

IEEE journal of biomedical and health informatics Apr 14, 2022
Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting...
Brain Waves Scalp Algorithms Electroencephalography Humans Deep Learning Wavelet Analysis
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