Selection of optimum frequency bands for detection of epileptiform patterns

Swami, Piyush; Bhatia, M. P. S.; Tripathi, Manjari; Chandra, P. Sarat; Panigrahi, B. K.; Gandhi, Tapan Kumar

doi:10.1049/htl.2018.5051

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…Methods Result [22] Here we used 1000 sample data points for each sample. For all the cases of 2-class problems, the model parameters are described in section (table 5).…”

Section: Iit Delhi (Iitd) Epilepsy Datasetmentioning

confidence: 99%

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

Ghosh,

Vigneswaran,

Rohan

et al. 2024

Preprint

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In this paper, we propose two models of oscillatory neural networks - the Deep Oscillatory Neural Network (DONN) and a convolutional variation of it named Oscillatory Convolutional Neural Network (OCNN) – and apply the models to a variety of problems involving the classification and prediction of Electroencephalogram (EEG) signals. Deep neural networks applied to signal processing problems will have to incorporate various architectural features to remember the history of the input signals e.g., loops between the layers, “gated” neurons, and tapped delay lines. But real brains have rich dynamics expressed in terms of frequency bands like alpha, beta, gamma, delta, etc. To incorporate this aspect of brain dynamics in a Recurrent Neural Network (RNN) we propose to use nonlinear oscillators as dynamic neuron models in the hidden layers. The two oscillatory deep neural networks proposed are applied to the following EEG classification and prediction problems: Prediction of nearby EEG channels, classification of single-channel EEG data (healthy vs. epileptic, different stages of sleep stage classification), and multi-channel EEG data (Epileptic vs. Normal, Left vs. right-hand Motor imagery movement, and healthy vs. Claustrophobic EEG).

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“…Methods Result [22] Here we used 1000 sample data points for each sample. For all the cases of 2-class problems, the model parameters are described in section (table 5).…”

Section: Iit Delhi (Iitd) Epilepsy Datasetmentioning

confidence: 99%

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

Ghosh,

Vigneswaran,

Rohan

et al. 2024

Preprint

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“…In this article, a new robust, end‐to‐end, one‐dimensional atrous conv‐net based architecture for AED using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring, and clinical use. The proposed architecture has been analyzed on three different, open‐source EEG datasets namely Bonn EEG time series dataset (Andrzejak et al, 2001), Single electrode EEG data of healthy and epileptic patients dataset (Panwar et al, 2019) and EEG epilepsy datasets (Swami et al, 2016) to observe the data drifting patterns for the proposed architecture. Feature mapping and cross‐dataset analysis have also been done.…”

Section: Introductionmentioning

confidence: 99%

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

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Precise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

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“…Mormann et al [7], citing studies by Rogowski et al, Salant et al, and Siegal et al, found changes in the autoregressive parameters seconds prior to the onset of a seizure as well as differences in characteristics between one-minute epochs prior to a seizure and control subjects. There are also studies on characterization of EEG time series using spectral and wavelet analysis [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models

Follis

Lai

2020

Signals

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Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA–GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process.

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Selection of optimum frequency bands for detection of epileptiform patterns

Cited by 11 publications

References 47 publications

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models

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