Semi-supervised training data selection improves seizure forecasting in canines with epilepsy
Objective Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm
training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is …
training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is …
New horizons in ambulatory electroencephalography
E Waterhouse - IEEE Engineering in Medicine and Biology …, 2003 - ieeexplore.ieee.org
Discusses improving the quality of life for epilepsy patients with a lower-cost, convenient
alternative to in-hospital epilepsy monitoring. Since its inception 30 years ago, ambulatory …
alternative to in-hospital epilepsy monitoring. Since its inception 30 years ago, ambulatory …
Noninvasive detection of focal seizures in ambulatory patients
Reliably detecting focal seizures without secondary generalization during daily life activities,
chronically, using convenient portable or wearable devices, would offer patients with active …
chronically, using convenient portable or wearable devices, would offer patients with active …
Prediction for high risk clinical symptoms of epilepsy based on deep learning algorithm
M Sun, F Wang, T Min, T Zang, Y Wang - IEEE access, 2018 - ieeexplore.ieee.org
Accurate forecasting of high-risk clinical symptoms, like epileptic seizures, has the potential
to transform clinical epilepsy care and to create new therapeutic strategies for individuals in …
to transform clinical epilepsy care and to create new therapeutic strategies for individuals in …
Ensembling crowdsourced seizure prediction algorithms using long‐term human intracranial EEG
Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction
clinically viable across a large group of patients. Recent work crowdsourced state‐of‐the‐art …
clinically viable across a large group of patients. Recent work crowdsourced state‐of‐the‐art …
Epileptic seizure prediction from EEG signals using unsupervised learning and a polling-based decision process
Epilepsy is a central nervous system disorder defined by spontaneous seizures and may
present a risk to the physical integrity of patients due to the unpredictability of the seizures. It …
present a risk to the physical integrity of patients due to the unpredictability of the seizures. It …
An automatic patient-specific seizure onset detection method using intracranial electroencephalography
Y Zheng, J Zhu, Y Qi, X Zheng, J Zhang - Neuromodulation: Technology at …, 2015 - Elsevier
Objective This study presents a multichannel patient-specific seizure detection method
based on the empirical mode decomposition (EMD) and support vector machine (SVM) …
based on the empirical mode decomposition (EMD) and support vector machine (SVM) …
The performance evaluation of the state-of-the-art EEG-based seizure prediction models
Z Ren, X Han, B Wang - Frontiers in Neurology, 2022 - frontiersin.org
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and
even death. The rapid development of electroencephalogram (EEG) and Artificial …
even death. The rapid development of electroencephalogram (EEG) and Artificial …
High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy
S Weisdorf, SW Gangstad… - Journal of …, 2018 - journals.physiology.org
Subcutaneous recording using electroencephalography (EEG) has the potential to enable
ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient …
ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient …
Improving long‐term management of epilepsy using a wearable multimodal seizure detection system
Background: The effective management of epilepsy necessitates reliable long‐term (over
days and months) monitoring of seizures. Although visual inspection of …
days and months) monitoring of seizures. Although visual inspection of …