Automatic detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy

2015 ◽  
Author(s):  
Maria Anastasiadou ◽  
Manolis Christodoulakis ◽  
Eleftherios S. Papathanasiou ◽  
Savvas S. Papacostas ◽  
Georgios D. Mitsis
Keyword(s):  
Automatic Detection ◽  
Scalp Eeg ◽  
Eeg Recordings ◽  
Patients With Epilepsy

scholarly journals Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  
Keyword(s):  
Normal Subjects ◽  
Intracranial Eeg ◽  
Zero Crossing ◽  
Temporal Precision ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Detection and Removal of Muscle Artifacts from Scalp EEG Recordings in Patients with Epilepsy

2014 ◽  
Author(s):  
Maria Anastasiadou ◽  
Avgis Hadjipapas ◽  
Manolis Christodoulakis ◽  
Eleftherios S. Papathanasiou ◽  
Savvas S. Papacostas ◽  
...  
Keyword(s):  
Scalp Eeg ◽  
Eeg Recordings ◽  
Patients With Epilepsy

scholarly journals Automatic detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome using a bi-modal EEG time-frequency feature

2020 ◽  
Author(s):  
Amir Omidvarnia ◽  
Aaron E.L. Warren ◽  
Linda J. Dalic ◽  
Mangor Pedersen ◽  
John S. Archer ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Time Frequency ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Fast Activity ◽  
Brain Maps ◽  
Detection Approach ◽  
Interictal Eeg ◽  
Eeg Recordings ◽  
Frequency Feature

AbstractObjectiveMark-up of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG mark-up is a time-consuming, subjective, and highly specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. The objective of this study was to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), which is a characteristic type of generalized IED seen in scalp EEG recordings of patients with Lennox-Gastaut syndrome (LGS), a severe form of drug-resistant generalized epilepsy.MethodsWe studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient’s interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings.ResultsGPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This ‘bi-modal’ spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified likely epileptic events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED mark-up.ConclusionGPFA events have a characteristic bi-modal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. Validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS.SignificanceThis study provides a novel methodology that paves the way for quick, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making. For example, automated quantification of generalized discharges may permit faster assessment of treatment response and estimation of future seizure risk.

scholarly journals Automatic detection of fast oscillations (40–200Hz) in scalp EEG recordings

2012 ◽  
Vol 123 (4) ◽  
pp. 670-680 ◽  
Author(s):  
Nicolás von Ellenrieder ◽  
Luciana P. Andrade-Valença ◽  
François Dubeau ◽  
Jean Gotman
Keyword(s):  
Automatic Detection ◽  
Scalp Eeg ◽  
Eeg Recordings ◽  
Fast Oscillations

scholarly journals Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Baocan Zhang ◽  
Wennan Wang ◽  
Yutian Xiao ◽  
Shixiao Xiao ◽  
Shuaichen Chen ◽  
...  
Keyword(s):  
Binary Classification ◽  
Seizure Detection ◽  
Model Parameters ◽  
Time Frequency ◽  
Scalp Eeg ◽  
Original Dataset ◽  
Input Dataset ◽  
Eeg Recordings ◽  
Short Time ◽  
Patients With Epilepsy

Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consuming. Therefore, automatic detection of seizure is of great importance. But the huge diversity of EEG signals belonging to different patients makes the task of seizure detection much challenging, for both human experts and automation methods. We propose three deep transfer convolutional neural networks (CNN) for automatic cross-subject seizure detection, based on VGG16, VGG19, and ResNet50, respectively. The original dataset is the CHB-MIT scalp EEG dataset. We use short time Fourier transform to generate time-frequency spectrum images as the input dataset, while positive samples are augmented due to the infrequent nature of seizure. The model parameters pretrained on ImageNet are transferred to our models. And the fine-tuned top layers, with an output layer of two neurons for binary classification (seizure or nonseizure), are trained from scratch. Then, the input dataset are randomly shuffled and divided into three partitions for training, validating, and testing the deep transfer CNNs, respectively. The average accuracies achieved by the deep transfer CNNs based on VGG16, VGG19, and ResNet50 are 97.75%, 98.26%, and 96.17% correspondingly. On those results of experiments, our method could prove to be an effective method for cross-subject seizure detection.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Important Benefit ◽  
Eeg Recordings ◽  
Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

Detection of Intracranial Signatures of Interictal Epileptiform Discharges from Concurrent Scalp EEG

2016 ◽  
Vol 26 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Loukianos Spyrou ◽  
David Martín-Lopez ◽  
Antonio Valentín ◽  
Gonzalo Alarcón ◽  
Saeid Sanei
Keyword(s):  
Detection Algorithm ◽  
The Other ◽  
Intracranial Eeg ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Scalp Electroencephalogram ◽  
Patients With Epilepsy ◽  
Electrical Activities ◽  
The Brain

Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject’s detection algorithm is based on the other patients’ data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

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