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Child Seizure Detection
Over the past few decades, electroencephalogram (EEG) has become a vital tool for physicians to diagnose several neurological disorders of the brain. However, its use in pediatric patients is limited due to the complexity of the EEG recording setup and the need for the patient to undergo surgery to implant electrodes in the brain.
There are a variety of EEG-based seizure detection systems available. Some of these devices alert a caregiver when a patient has a seizure and others can provide audio or video monitoring of a person’s seizures.
The effectiveness of EEG-based seizure detection relies on the ability to extract features from the raw EEG signals and classify the signal as an epileptic activity. This involves two key steps: (1) feature extraction, and (2) constructing and training a classification system to utilize these extracted features for the detection of seizures.
Most automated seizure detection techniques involve the use of a deep learning approach to perform these two steps, which improves the performance and sophistication of the developed automatic system. In this work, we propose a novel deep-learning approach for detecting seizures in multichannel EEG signals recordings that uses a two-dimensional supervised deep convolutional autoencoder (2D-SDCAE) to perform both feature extraction and classification tasks.
This study will evaluate the inter-rater reliability of QEEG seizures detected by bedside clinical nurses (using a smartphone app that extracts features and classesify seizures) compared to cEEG seizures detected offline by experts blinded to the EEG data (primary hypothesis). The analysis will also compare event duration marked on QEEG with event duration marked on cEEG. seizure monitor
Website: https://epilepsyseizure.co.nz/
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