Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization

Rahman, Ashikur; Faisal, Fahim; Nishat, Mirza Muntasir; Siraji, Muntequa Imtiaz; Khalid, Lamim Ibtisam; Khan, M. R. H.; Reza, Md. Taslim

doi:10.1109/biosmart54244.2021.9677770

Cited by 29 publications

(6 citation statements)

References 24 publications

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“…Available link. [38] The dataset from the reference consisted of 5 folders with 100 files each and each file represents a single subject. The files contain brain activity for 23.6 seconds each.…”

Section: Dataset Of Rhythmicity Spectrogram Based Images Of Seizure A...mentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.

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Section: Dataset Of Rhythmicity Spectrogram Based Images Of Seizure A...mentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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“…However, clinical assessment of mass undergraduate students would be unwieldy and resource-heavy, since there can be numerous factors involved in instigating depression. In this regard, Machine Learning (ML) models [16][17][18][19][20][21][22][23][24][25] can perhaps become valuable for detecting and predicting subsequent health issues [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] as well as depressive episodes. Furthermore, the result can be analyzed to identify depression-related trends revealed among young people which can aid higher education institutions to understand the factors better and develop effective strategies to mitigate these factors.…”

Section: Introductionmentioning

confidence: 99%

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Siraji,

Rahman,

Nishat

et al. 2023

PLoS ONE

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Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

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“…An ample amount of literature exists on the successful executions of the Bio-Inspired Algorithms in the field of power converters professed by many researchers [11][12][13][14][15][16][17][18][19][20]. One of such promising instances of work has been witnessed with Machine learning (ML) algorithms [21][22][23][24][25][26][27][28][29], artificial intelligence [30][31][32][33][34][35][36][37][38][39], and different applications of ML in healthcare sectors [40][41][42][43][44][45][46][47][48][49], and so many other cases [50][51][52][53][54][55][56][57][58][...…”

Section: Introductionmentioning

confidence: 99%

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Shagor,

Faisal,

Nishat

et al. 2023

EAI Endorsed Trans AI Robotics

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This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and, Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances.

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Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization

Cited by 29 publications

References 24 publications

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

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