Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

Publications associées (28)

À propos
Confidentialité
Mentions légales

Graph Chatbot

Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdim ...

2022

Disruption of layer-specific visual processing in a model of focal neocortical epilepsy

Silvestro Micera, Matteo Vissani

The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and ...

OXFORD UNIV PRESS INC2022

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Alexandre Schmid, Keyvan Farhang Razi

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

IEEE2022

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale, Renato Zanetti

Objective: Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, ...

2022

Recent Developments on the Use of Nanomaterials for the Treatment of Epilepsy

Sandrine Gerber, Sara María Garcia Pedrero

Epilepsy affects more than 40 million people worldwide, constituting one of the most debilitating disorders of the Central Nervous System (CNS). It results from an imbalance in the electrical activity of neurons, which is primarily mediated by calcium ions ...

2022

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo, Renaud Marquis

Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless ...

IEEE2022

Epileptic Seizure Detection With Patient-Specific Feature and Channel Selection for Low-power Applications

Alexandre Schmid, Keyvan Farhang Razi

An accurate epileptic seizure detector using intracranial electroencephalography (iEEG) recordings, suitable for low-power wearable/implantable applications, is presented. Eleven time-domain features with low hardware complexity are employed in the feature ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, Anthony Hitchcock Thomas

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in w ...

2022

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Saleh Baghersalimi

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automati ...

2021

An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure

Amir Hossein Omidvarnia

This study presents a new methodology for obtaining functional brain networks (FBNs) using multichannel scalp EEG recordings. The developed methodology extracts pair-wise phase synchrony between EEG electrodes to obtain FBNs at delta, theta, and alpha-band ...

ELSEVIER SCI LTD2021

Page 2 sur 3

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo, Renaud Marquis

IEEE2022

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale, Renato Zanetti

2022

An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure

Amir Hossein Omidvarnia

ELSEVIER SCI LTD2021