Seizure detection using wavelet decomposition of the prediction error signal from a single channel of intra-cranial EEG

2014 ◽  
Author(s):  
Zisheng Zhang ◽  
Keshab K. Parhi
Keyword(s):  
Prediction Error ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Seizure Detection ◽  
Error Signal

Pilot study of a single-channel EEG seizure detection algorithm using machine learning

2021 ◽  
Author(s):  
Seungjun Ryu ◽  
Seunghyeok Back ◽  
Seongju Lee ◽  
Hyeon Seo ◽  
Chanki Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Single Channel ◽  
Seizure Detection ◽  
Detection Algorithm

WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG

2017 ◽  
Vol 5 (4) ◽  
pp. 99-102
Author(s):  
Vandana Roy ◽  
Anand Prakash ◽  
Shailja Shukla
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Chi Square ◽  
Wavelet Features ◽  
Stage Classification ◽  
Selection Of

The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

scholarly journals Engineering Nonlinear Epileptic Biomarkers Using Deep Learning and Benford’s Law

2021 ◽  
Author(s):  
Joseph Caffarini ◽  
Klevest Gjini ◽  
Brinda Sevak ◽  
Roger Waleffe ◽  
Mariel Kalkach-Aparicio ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unique Feature ◽  
Single Channel ◽  
Seizure Detection ◽  
Intracranial Eeg ◽  
Feature Spaces ◽  
Frequency Domains ◽  
High Gamma ◽  
Leave One Out ◽  
Early Seizure

Abstract In this study we designed two deep neural networks to encode 16 feature latent spaces for early seizure detection in intracranial EEG and compared them to 16 widely used engineered metrics: Epileptogenicity Index (EI), Phase Locked High Gamma (PLHG), Time and Frequency Domain Cho Gaines Distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, gamma, and high gamma bands. The deep learning models were pretrained for seizure identification on the time and frequency domains of one second single channel clips of 127 seizures (from 25 different subjects) using “leave-one-out” (LOO) cross validation. Each neural network extracted unique feature spaces that were used to train a Random Forest Classifier (RFC) for seizure identification and latency tasks. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted from the UPenn and Mayo Clinic's Seizure Detection Challenge to train another RFC for the contest. They obtained an AUC score of 0.93, demonstrating a transferable method to identify interpretable biomarkers for seizure detection.

scholarly journals A quantitative reward prediction error signal in the ventral pallidum

2020 ◽  
Vol 23 (10) ◽  
pp. 1267-1276 ◽  
Author(s):  
David J. Ottenheimer ◽  
Bilal A. Bari ◽  
Elissa Sutlief ◽  
Kurt M. Fraser ◽  
Tabitha H. Kim ◽  
...  
Keyword(s):  
Prediction Error ◽  
Ventral Pallidum ◽  
Error Signal ◽  
Reward Prediction Error ◽  
Reward Prediction
Sign in / Sign up

Export Citation Format

Share Document