Detection of ictal patterns in electroencephalogram signals using 3D phase trajectories

Swami, Piyush; Gandhi, Tapan Kumar; Panigrahi, B. K.; Bhatia, M. P. S.; Anand, Sneh

doi:10.1109/indicon.2015.7443540

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…The visualization of phase space representations (PSRs) is useful for studying the dynamics and state of biomedical signals such as EEG ( Sharma and Pachori, 2015 ; Swami et al, 2015b , c ; Anuragi et al, 2022 ). The 3D PSRs are calculated by using equation (1) .…”

Section: Methodsmentioning

confidence: 99%

“…Additional objectives to consider may include improving statistical performance ( Mormann et al, 2007 ; Tiwari et al, 2016 ; Swami et al, 2019 ; Anuragi et al, 2022 ) while further reducing the number of channels required for diagnosis. This could be extended with deep learning (DL) methods ( Tang et al, 2024 ) and/or localizing the foci of epileptic seizures, thus addressing long-standing inverse problems ( Swami et al, 2016c , d ; Gandhi et al, 2024 ). This study was conducted using three datasets (as described in section 3.1); however, the total number of participants across all three datasets was 35 and the brain signals as annotated by clinicians and thereby classified using the proposed method were inter-ictal vs. ictal pattern recognition.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

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Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals

Swami,

Maheshwari,

Kumar

et al. 2024

Front. Hum. Neurosci.

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The visual scrutinization process for detecting epileptic seizures (ictal patterns) is time-consuming and prone to manual errors, which can have serious consequences, including drug abuse and life-threatening situations. To address these challenges, expert systems for automated detection of ictal patterns have been developed, yet feature engineering remains problematic due to variability within and between subjects. Single-objective optimization approaches yield less reliable results. This study proposes a novel expert system using the non-dominated sorting genetic algorithm (NSGA)-II to detect ictal patterns in brain signals. Employing an evolutionary multi-objective optimization (EMO) approach, the classifier minimizes both the number of features and the error rate simultaneously. Input features include statistical features derived from phase space transformations, singular values, and energy values of time–frequency domain wavelet packet transform coefficients. Through evolutionary transfer optimization (ETO), the optimal feature set is determined from training datasets and passed through a generalized regression neural network (GRNN) model for pattern detection of testing datasets. The results demonstrate high accuracy with minimal computation time (<0.5 s), and EMO reduces the feature set matrix by more than half, suggesting reliability for clinical applications. In conclusion, the proposed model offers promising advancements in automating ictal pattern recognition in EEG data, with potential implications for improving epilepsy diagnosis and treatment. Further research is warranted to validate its performance across diverse datasets and investigate potential limitations.

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Section: Methodsmentioning

confidence: 99%

Section: Conclusion and Future Scopementioning

confidence: 99%

Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals

Swami,

Maheshwari,

Kumar

et al. 2024

Front. Hum. Neurosci.

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show abstract

“…Then, they clustered the wavelet coefficients using K-means, and probability distributions of wavelet coefficients to the clusters were used as input to the MLP [16]. Gandhi [21]. Jaiswal and Banka used Local Neighbor Descriptive Pattern and One-dimensional Local Gradient Pattern for feature extraction and classified the features with different machine learning algorithms [22].…”

Section: Introductionmentioning

confidence: 99%

EEG Sinyallerinden Epileptik Nöbet Tahmini Üzerine Bir Çalışma

Akyol¹,

Atila²

2020

Academic Platform Journal of Engineering and Science

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Epilepsy disease, a neurological disorder that causes recurrent and sudden crises, occurs at unforeseen times. This study presents the classification of electroencephalogram signals for epileptic seizure prediction. The performances of the machine learning algorithms were evaluated on the dataset extracted from electroencephalogram signals. The dataset consists of brain activities for 23.5 seconds of 500 individuals with each has 178 data points for one second, and totally of 11500 pieces of information. In this study, since the aim was to develop a model to predict epileptic seizure, the problem was transformed into a two-class problem by combining target categories except than epileptic seizure. Since combined target categories made the dataset unbalanced, Random Under Sampling and Random Over Sampling methods were applied to prevent the machine learning algorithms from overfitting the dominant class. Thus, each of the three datasets was divided into training and test sets by ratios of 60/40, 70/30, 80/20. The performance of the several machine learning algorithms were evaluated and discussed through three different scenarios. Overall results showed us that Random Forest algorithm offered superior performance than others for all scenarios in terms of accuracy, sensitivity and specificity metrics.

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Probability-Based Approach for Epileptic Seizure Detection Using Hidden Markov Model

Dash

Kolekar

2019

Communications in Computer and Information Science

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Detection of ictal patterns in electroencephalogram signals using 3D phase trajectories

Cited by 5 publications

References 28 publications

Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals

Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals

EEG Sinyallerinden Epileptik Nöbet Tahmini Üzerine Bir Çalışma

Probability-Based Approach for Epileptic Seizure Detection Using Hidden Markov Model

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