AUTOMATED PATIENT-SPECIFIC SEIZURE DETECTION SYSTEM WITH SELF-PARAMETERS ADAPTATION

2017 ◽  
Vol 45 (4) ◽  
Author(s):  
Sabrina Ammar ◽  
Omar Trigui ◽  
Mohamed Senouci
Keyword(s):  
Detection System ◽  
Seizure Detection ◽  
Patient Specific

scholarly journals Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Fabio Pisano ◽  
Giuliana Sias ◽  
Alessandra Fanni ◽  
Barbara Cannas ◽  
António Dourado ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frontal Lobe ◽  
Detection System ◽  
Seizure Detection ◽  
Specific Model ◽  
Patient Specific ◽  
Frontal Lobe Epilepsy ◽  
Clinical Approach ◽  
Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.

Improved Patient-Independent Seizure Detection System Using Novel Feature Extraction Techniques

2021 ◽  
pp. 879-888
Author(s):  
Durgesh Nandini ◽  
Jyoti Yadav ◽  
Asha Rani ◽  
Vijander Singh
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Seizure Detection ◽  
Extraction Techniques

scholarly journals Signal regularity-based automated seizure detection system for scalp EEG monitoring

2010 ◽  
Vol 46 (6) ◽  
pp. 922-935 ◽  
Author(s):  
Deng-Shan Shiau ◽  
J. J. Halford ◽  
K. M. Kelly ◽  
R. T. Kern ◽  
M. Inman ◽  
...  
Keyword(s):  
Detection System ◽  
Seizure Detection ◽  
Eeg Monitoring ◽  
Scalp Eeg

A low-power, low-noise, high-accurate epileptic-seizure detection system for wearable applications

2019 ◽  
Vol 92 ◽  
pp. 104600
Author(s):  
Mohammad Tohidi ◽  
Jens Kargaard Madsen ◽  
Farshad Moradi
Keyword(s):  
Low Power ◽  
Epileptic Seizure ◽  
Detection System ◽  
Seizure Detection ◽  
Low Noise ◽  
Epileptic Seizure Detection
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