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Jan 19, 2023 · The objective of this study is to propose a deep learning architecture to identify scalp High Frequency Oscillations (HFOs). HFOs are brief ...
In this paper, we present a comparative study of two detectors: onedimensional (1D) Convolutional Neural Networks (CNN) running on High-Density ...
High‐frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical ...
In this paper, we present a comparative study of two detectors: onedimensional (1D) Convolutional Neural Networks (CNN) running on High-Density ...
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Since 2002, HFO detection in iEEG was widely studied in. 1D domain (time or frequency domain) and more recently in. 2D time-frequency (TF) domain. Most of the ...
Our proposed detector combines a 1D-CNN+LSTM model to extract time-domain features from the EEG data and an attention-based 2D-CNN model to extract frequency- ...
We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various ...
Methods : An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep ...
Dec 28, 2021 · Specifically, the signal branch is designed as a hybrid network with a 1d-ResNet and a LSTM connected in parallel, while the TFpic branch is ...
We propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar ...