EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

Yogarajan, G.; Alsubaie, Najah; Rajasekaran, G.; Revathi, T.; Alqahtani, Mohammed S.; Abbas, Mohamed; Alshahrani, Madshush M.; Soufiene, Ben Othman

doi:10.1038/s41598-023-44318-w

Cited by 7 publications

(2 citation statements)

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“…An interesting alternative for epilepsy diagnosis is the use of computer vision techniques in EEG signal analysis, which enables the use of machine learning and pattern recognition algorithms to find unique patterns or anomalies in the EEG data ( Bajaj et al, 2017 ; Zhou et al, 2018 ). Computer vision-based algorithms for finding epilepsy in EEG signals depend on how well the temporal information of the EEG is turned into spatial information in the form of images ( Yogarajan et al, 2023 ; Zeng et al, 2023 ). This is because EEG signals change over time.…”

Section: Introductionmentioning

confidence: 99%

Advanced framework for epilepsy detection through image-based EEG signal analysis

Krishnan,

Erramchetty,

Balusa

2024

Front. Hum. Neurosci.

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BackgroundRecurrent and unpredictable seizures characterize epilepsy, a neurological disorder affecting millions worldwide. Epilepsy diagnosis is crucial for timely treatment and better outcomes. Electroencephalography (EEG) time-series data analysis is essential for epilepsy diagnosis and surveillance. Complex signal processing methods used in traditional EEG analysis are computationally demanding and difficult to generalize across patients. Researchers are using machine learning to improve epilepsy detection, particularly visual feature extraction from EEG time-series data.ObjectiveThis study examines the application of a Gramian Angular Summation Field (GASF) approach for the analysis of EEG signals. Additionally, it explores the utilization of image features, specifically the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) techniques, for the purpose of epilepsy detection in EEG data.MethodsThe proposed methodology encompasses the transformation of EEG signals into images based on GASF, followed by the extraction of features utilizing SIFT and ORB techniques, and ultimately, the selection of relevant features. A state-of-the-art machine learning classifier is employed to classify GASF images into two categories: normal EEG patterns and focal EEG patterns. Bern-Barcelona EEG recordings were used to test the proposed method.ResultsThis method classifies EEG signals with 96% accuracy using SIFT features and 94% using ORB features. The Random Forest (RF) classifier surpasses state-of-the-art approaches in precision, recall, F1-score, specificity, and Area Under Curve (AUC). The Receiver Operating Characteristic (ROC) curve shows that Random Forest outperforms Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers.SignificanceThe suggested method has many advantages over time-series EEG data analysis and machine learning classifiers used in epilepsy detection studies. A novel image-based preprocessing pipeline using GASF for robust image synthesis and SIFT and ORB for feature extraction is presented here. The study found that the suggested method can accurately discriminate between normal and focal EEG signals, improving patient outcomes through early and accurate epilepsy diagnosis.

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Section: Introductionmentioning

confidence: 99%

Advanced framework for epilepsy detection through image-based EEG signal analysis

Krishnan,

Erramchetty,

Balusa

2024

Front. Hum. Neurosci.

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“…In addition to time series analysis, migraine clusters can also be found using system studying techniques. Researchers have found clusters of migraine patients with similar indications and symptoms by using clustering techniques, such as ok-means [7].…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

2024

JMC

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This study investigates how well time collection analysis may be used by system-studying algorithms to diagnose migraines. Through the use of various algorithms and current statistical resources, such as EEG activity and affected person histories, the mission will develop a predictive model to identify the start of migraine signs and symptoms, allowing for prompt and early management for sufferers. The results will help to compare how the algorithms affect migraine accuracy predictions and how well they forecast migraine presence early enough for preventative interventions. Furthermore, studies may be conducted to examine the model's ability to be employed in real-time patient monitoring and to identify actionable inputs from the algorithms. This work presents novel machine learning algorithms software for time series analysis of functions such as temperature, heart rate, and EEG indications, which can be used to identify migraines. The paper delves into the idea of utilizing machine learning algorithms to identify migraine styles, examines the pre-processing procedures to accurately arrange the indications, and provides the results of a study conducted to evaluate the efficacy of the solution. The observation's results show that the suggested diagnostic framework is capable of accurately identifying and categorizing migraines, enabling medical professionals to recognize the warning indications of migraine and predict when an attack would begin. The examination demonstrates the possibility of devices learning algorithms to correctly and accurately diagnose migraines, but more research is necessary to obtain more detailed information about this situation.

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Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

Manivannan,

Mani,

Rajaguru

et al. 2024

Sci Rep

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The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

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EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

Cited by 7 publications

References 50 publications

Advanced framework for epilepsy detection through image-based EEG signal analysis

Advanced framework for epilepsy detection through image-based EEG signal analysis

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

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