Detection of epileptical seizures based on alpha band statistical features

Sameer, Mustafa; Gupta, Bharat

doi:10.1007/s11277-020-07542-5

Cited by 45 publications

(11 citation statements)

References 40 publications

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“…The reproducible acquisition of EEG signals is a basic requirement for an effective analysis and for the monitoring of brain activity and abnormalities that are formed [ 30 , 31 ]. Over the years, many methods and techniques have been reported based on the size, frequency and shape of EEG signals and associated signals for the diagnosis of various diseases and other applications [ 32 , 33 ]. This paper uses Fast Fourier Transform (FFT) which decreases the number of computations required for N points from 2N 2 to 2NlogN and it is an excellent algorithm for computing Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT) [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications

Ali

Afridi

Soomro

et al. 2022

Wireless Pers Commun

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Electroencephalography (EEG) is a technique of Electrophysiology used in a wide variety of scientific studies and applications. Inadequately, many commercial devices that are available and used worldwide for EEG monitoring are expensive that costs up to thousands of dollars. Over the past few years, because of advancements in technology, different cost-effective EEG recording devices have been made. One such device is a non-invasive single electrode commercial EEG headset called MindWave 002 (MW2), created by NeuroSky Inc that cost less than 100 USD. This work contributes in four distinct ways, first, how mental states such as a focused and relaxed can be identified based on EEG signals recorded by inexpensive MW2 is demonstrated for accurate information extraction. Second, MW2 is considered because apart from cost, the user's comfort level is enhanced due to non-invasive operation, low power consumption, portable small size, and a minimal number of detecting locations of MW2. Third, 2 situations were created to stimulate focus and relaxation states. Prior to analysis, the acquired brain signals were pre-processed to discard artefacts and noise, and band-pass filtering was performed for delta, theta, alpha, beta, and gamma wave extraction. Fourth, analysis of the shapes and nature of extracted waves was performed with power spectral density (PSD), mean amplitude values, and other parameters in LabVIEW. Finally, with comprehensive experiments, the mean values of the focused and relaxed signal EEG signals were found to be 30.23 µV and 15.330 µV respectively. Similarly, average PSD values showed an increase in theta wave value and a decrease in beta wave value related to the focus and relaxed state, respectively. We also analyzed the involuntary and intentional number of blinks recorded by the MW2 device. Our study can be used to check mental health wellness and could provide psychological treatment effects by training the mind to quickly enter a relaxed state and improve the person's ability to focus. In addition, this study can open new avenues for neurofeedback and brain control applications. Supplementary Information The online version contains supplementary material available at 10.1007/s11277-022-09731-w.

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Section: Methodsmentioning

confidence: 99%

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications

Ali

Afridi

Soomro

et al. 2022

Wireless Pers Commun

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“…For preictal phase prediction, the RF is used. Sameer et al [127] proposed a novel automated seizures detection technique using the alpha band (…”

Section: Daoud Et Al [115] Dcnnmentioning

confidence: 99%

“…Here a benchmark dataset named CBH-MIT was used. Sameer et al [127] presented a new approach for detecting seizures using the alpha band (8 Hz-12 Hz). Using the RF classifier, they obtained an accuracy of 98% and an AUC score of 1 in distinguishing healthy and seizure patients.…”

Section: Specif Icity = T N F P + T Nmentioning

confidence: 99%

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

et al. 2023

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Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. So, it is the most used test for Epileptic Seizure detection and prediction. This study examines the contributions that have so far been made utilizing Electroencephalography technology to detect, predict, and monitor Epileptic Seizures. We have reviewed around 56 research articles, and those papers are selected from different academic databases. The studies explored various approaches, including Machine Learning, Deep Learning, and the Internet of Things framework. A comprehensive discussion of different classification algorithms is analyzed, and their performances are explored. Furthermore, various open issues of the stated approach are discussed, and potential future works are addressed.

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“…Banu Priya Prathaban et al [12] introduced Grey Wolf Optimized Model Driven (GWO-MD) method which reduced the time needed for computation and less complexity but it has slow rate of convergence. Mustafa Sameer and Bharat Gupta [13] worked with combination of Alpha features, KNN, NB, DT, SVM, Adaboost, Random forest classi er to enhance the accuracy and high classi cation capability. However, it need the optimization of results in case of larger datasets.…”

Section: Motivation For the Researchmentioning

confidence: 99%

Ensemble Modelling for Predicting the Relation between Bio-psychosocial Signals and Seizures using the Gradient Boosting Method

Kapoor

Nagpal

2022

Preprint

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In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Ensemble Learnings has become popular research area among the researchers related to the field of automatic classification of electroencephalograph signals for predication of mental health issues like seizures. However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and Ensemble Learnings based EEG signals classification method for seizures using Extreme Gradient Boosting Model.Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method (XGBoost) and Random Forest method. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals. In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy, specificity and sensitivity.

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Detection of epileptical seizures based on alpha band statistical features

Cited by 45 publications

References 40 publications

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

Ensemble Modelling for Predicting the Relation between Bio-psychosocial Signals and Seizures using the Gradient Boosting Method

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