A Time-Frequency Based Method for the Detection of Epileptic Seizures in EEG Recordings

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

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Classification of Motor Imagery EEG recordings with Subject Specific Time-Frequency Patterns

2006 IEEE 14th Signal Processing and Communications Applications ◽

10.1109/siu.2006.1659763 ◽

2006 ◽

Author(s):

N.F. Ince ◽

S. Arica ◽

A. Tewfik

Keyword(s):

Motor Imagery ◽

Specific Time ◽

Time Frequency ◽

Subject Specific ◽

Eeg Recordings

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Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Entropy ◽

10.3390/e24010102 ◽

2022 ◽

Vol 24 (1) ◽

pp. 102

Author(s):

Michele Lo Giudice ◽

Giuseppe Varone ◽

Cosimo Ieracitano ◽

Nadia Mammone ◽

Giovanbattista Gaspare Tripodi ◽

...

Keyword(s):

Neural Network ◽

Discriminant Analysis ◽

Convolutional Neural Network ◽

Epileptic Seizures ◽

Permutation Entropy ◽

Diagnostic Tools ◽

Support Vector ◽

Feature Maps ◽

Time Frequency ◽

Interictal Eeg

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.

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Functional topography of auditory areas derived from the combination of electrophysiological recordings and cortical electrical stimulation.

10.31219/osf.io/fxdg5 ◽

2021 ◽

Author(s):

Agnès Trébuchon ◽

F.-Xavier Alario ◽

Catherine Liégeois-Chauvel

Keyword(s):

Language Processing ◽

Hemispheric Specialization ◽

Functional Characterization ◽

Posterior Part ◽

Epileptic Seizures ◽

Superior Temporal Gyrus ◽

Time Frequency ◽

Depth Electrodes ◽

Electrophysiological Recordings

The posterior part of the superior temporal gyrus (STG) has long been known to be a crucial hub for auditory and language processing, at the crossroad of the functionally defined ventral and dorsal pathways. Anatomical studies have shown that this “auditory cortex” is composed of several cytoarchitectonic areas whose limits do not consistently match macro-anatomic landmarks like gyral and sulcal borders. The functional characterization of these areas derived from brain imaging studies has some limitations, even when high field functional magnetic resonance imaging (fMRI) is used, because of the variability observed in the extension of these areas between hemispheres and individuals. In patients implanted with depth electrodes, in vivo recordings and direct electrical stimulations of the different sub-parts of the posterior STG allow to delineate different auditory sub-fields in Heschl’s gyrus (HG), Planum Temporale (PT), the posterior part of the superior temporal gyrus anterior to HG, the posterior superior temporal sulcus (STS), and the region at the parietal-temporal boundary commonly labelled “Spt”. We describe how this delineation can be achieved using data from electrical cortical stimulation combined with local field potentials and time frequency analysis recorded as responses to pure tones and syllables. We show the differences in functional roles between the primary and non-primary auditory areas, in the left and the right hemispheres. We discuss how these findings help understanding the auditory semiology of certain epileptic seizures and, more generally, the neural substrate of hemispheric specialization for language.

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Anticipation of epileptic seizures from standard EEG recordings

The Lancet ◽

10.1016/s0140-6736(00)03591-1 ◽

2001 ◽

Vol 357 (9251) ◽

pp. 183-188 ◽

Cited By ~ 244

Author(s):

Michel Le Van Quyen ◽

Jacques Martinerie ◽

Vincent Navarro ◽

Paul Boon ◽

Michel D'Havé ◽

...

Keyword(s):

Epileptic Seizures ◽

Eeg Recordings

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High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy

Journal of Neurophysiology ◽

10.1152/jn.00320.2018 ◽

2018 ◽

Vol 120 (3) ◽

pp. 1451-1460 ◽

Cited By ~ 13

Author(s):

Sigge Weisdorf ◽

Sirin W. Gangstad ◽

Jonas Duun-Henriksen ◽

Karina S. S. Mosholt ◽

Troels W. Kjær

Keyword(s):

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Temporal Dynamics ◽

Real Life ◽

High Similarity ◽

Time Frequency ◽

Scalp Eeg ◽

Eeg Recordings ◽

The Impact

Subcutaneous recording using electroencephalography (EEG) has the potential to enable ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient increased mobility and discreteness. This study is the first to compare physiological and epileptiform EEG signals from subcutaneous and scalp EEG recordings in epilepsy patients. Four patients with probable or definite temporal lobe epilepsy were monitored with simultaneous scalp and subcutaneous EEG recordings. EEG recordings were compared by correlation and time-frequency analysis across an array of clinically relevant waveforms and patterns. We found high similarity between the subcutaneous EEG channels and nearby temporal scalp channels for most investigated electroencephalographic events. In particular, the temporal dynamics of one typical temporal lobe seizure in one patient were similar in scalp and subcutaneous recordings in regard to frequency distribution and morphology. Signal similarity is strongly related to the distance between the subcutaneous and scalp electrodes. On the basis of these limited data, we conclude that subcutaneous EEG recordings are very similar to scalp recordings in both time and time-frequency domains, if the distance between them is small. As many electroencephalographic events are local/regional, the positioning of the subcutaneous electrodes should be considered carefully to reflect the relevant clinical question. The impact of implantation depth of the subcutaneous electrode on recording quality should be investigated further. NEW & NOTEWORTHY This study is the first publication comparing the detection of clinically relevant, pathological EEG features from a subcutaneous recording system designed for out-patient ultra-long-term use to gold standard scalp EEG recordings. Our study shows that subcutaneous channels are very similar to comparable scalp channels, but also point out some issues yet to be resolved.

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Spectrum of Nonepileptic Paroxysmal Events in Children from Southern India

Journal of Neurosciences in Rural Practice ◽

10.1055/s-0039-3399472 ◽

2019 ◽

Vol 10 (04) ◽

pp. 608-612

Author(s):

Vykuntaraju K Gowda ◽

Raghavendraswami Amoghimath ◽

Naveen Benakappa ◽

Sanjay K Shivappa

Keyword(s):

Children And Adolescents ◽

Age Of Onset ◽

Age Groups ◽

Epileptic Seizures ◽

Breath Holding ◽

Home Video ◽

Psychogenic Nonepileptic Seizures ◽

Wrong Diagnosis ◽

Nonepileptic Seizures ◽

Eeg Recordings

Abstract Background Nonepileptic paroxysmal events (NEPEs) present with episodes similar to epileptic seizures but without abnormal electrical discharge on electroencephalogram (EEG). NEPEs are commonly misdiagnosed as epilepsy. Epilepsy is diagnosed on the basis of a detailed history and examination. Emphasis during history to rule out the possibility of NEPE is important. The wrong diagnosis of epilepsy can lead to physical, psychological, and financial harm to the child and the family. Hence, this study was planned. Objective The objective of the study is to evaluate clinical profile, frequency, and spectrum of NEPE in children. Materials and Methods This is a prospective observational study. Patients with NEPE between January 2014 and August 2016 aged < 18 years were enrolled. NEPEs were diagnosed on the basis of history, home video, and EEG recordings. Patients were divided into different categories according to age, specific type of disorder, and system responsible. Patients were followed for their NEPE frequency and outcome. Results A total of 3,660 children presented with paroxysmal events; of them 8% were diagnosed with NEPE. Patients diagnosed with NEPE were classified into three age groups on the basis of their age of onset of symptom; of the total 285 patients, there were 2 neonates (0.7%), 160 infants (56%), and 123 children and adolescents (43.1%). Fifty-eight percent patients were boys. The most common diagnoses were breath-holding spells 113 (39%), followed by syncope 38 (13.3%) and psychogenic nonepileptic seizures 37 (12.9%). About 9 and 5% of patients had concomitant epilepsy and developmental delay, respectively. Conclusions NEPEs account for 8% of paroxysmal events. Most common NEPEs were breath-holding spells among infants and syncope and “psychogenic nonepileptic seizures” in children and adolescents.

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Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

Brain Topography ◽

10.1007/bf01464480 ◽

1997 ◽

Vol 9 (4) ◽

pp. 249-270 ◽

Cited By ~ 91

Author(s):

Jan Pieter M. Pijn ◽

Demetrios N. Velis ◽

Marcel J. van der Heyden ◽

Jaap DeGoede ◽

Cees W. M. van Veelen ◽

...

Keyword(s):

Nonlinear Dynamics ◽

Epileptic Seizures ◽

Intracranial Eeg ◽

Eeg Recordings

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Analysis of Seizure EEG in Kindled Epileptic Rats

Computational and Mathematical Methods in Medicine ◽

10.1080/17486700701528970 ◽

2007 ◽

Vol 8 (4) ◽

pp. 225-234 ◽

Cited By ~ 5

Author(s):

A. K. Sen ◽

M. J. Kubek ◽

H. E. Shannon

Keyword(s):

Epileptic Seizures ◽

Wavelet Spectrum ◽

Eeg Signal ◽

Eeg Signals ◽

Time Frequency ◽

Frequency Representation ◽

Frequency Changes ◽

Global Wavelet Spectrum ◽

Frequency Variations ◽

Dominant Frequencies

Using wavelet analysis we have detected the presence of chirps in seizure EEG signals recorded from kindled epileptic rats. Seizures were induced by electrical stimulation of the amygdala and the EEG signals recorded from the amygdala were analyzed using a continuous wavelet transform. A time–frequency representation of the wavelet power spectrum revealed that during seizure the EEG signal is characterized by a chirp-like waveform whose frequency changes with time from the onset of seizure to its completion. Similar chirp-like time–frequency profiles have been observed in newborn and adult patients undergoing epileptic seizures. The global wavelet spectrum depicting the variation of power with frequency showed two dominant frequencies with the largest amounts of power during seizure. Our results indicate that a kindling paradigm in rats can be used as an animal model of human temporal lobe epilepsy to detect seizures by identifying chirp-like time–frequency variations in the EEG signal.

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