On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Kavitha, K. V. N.; Sharmila, A.; Imoize, Agbotiname Lucky; Ojo, Stephen; Selvan, K. Senthamil; Ahanger, Tariq Ahamed; Alhassan, Musah

doi:10.1155/2022/8928021

Cited by 37 publications

(8 citation statements)

References 45 publications

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“…Several machine learning techniques have been explored [ 16 , 17 , 18 , 19 , 20 , 21 ]. Some of the key ones include random forest (RF) [ 22 , 23 ], support vector machine (SVM), neural networks (NNs), K-nearest neighbor (KNN), and the Gaussian process (GP).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Isabona

Imoize

Kim

2022

Sensors

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Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Isabona

Imoize

Kim

2022

Sensors

Self Cite

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“…The authors in [7] used real-time medical information from the Senthil Multispeciality Hospital in India and EEG from the University of Bonn in Germany for epilepsy detection using time-frequency domain features. Using the DWT, the signals were divided into six frequency subbands, from which 12 statistical functions were obtained.…”

Section: Traditional Approachesmentioning

confidence: 99%

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Alshayeji

2023

Mach. Learn.: Sci. Technol.

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Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person's way of life. Manual diagnosis is a laborious and time-consuming task prone to human error; therefore, automating this task by developing an intelligent system is necessary. Existing deep learning (DL) models require high training time, large datasets, and machines with more memory and processing power. In addition, owing to the black-box nature of DL models, no one can determine the features that the network prefers for classification decisions. To overcome these challenges, this paper proposes an accurate, automatic, and fast intelligent system for epilepsy detection using a computer-aided diagnosis (CAD)-2D machine learning (ML) framework. Existing ML models struggle to produce reliable and acceptable diagnostic results owing to the low amplitude and nonstationary nature of electroencephalograms (EEG), particularly in clinical situations where environmental influences are almost impossible to eliminate. The proposed model was built on the CHB-MIT dataset, and it represents the first study that employs the speeded-up robust feature (SURF) bag of features (BOF) technique for this application, which generates local features from the spectrogram images of the respective 1D EEG signal inputs. In addition, DL features were extracted from the spectrogram images for model performance comparisons. Both features were used separately to train the ML classifiers. Implementing SURF offers fast computation and makes the model invariant to distortions, noise, scaling, and so on. Therefore, the proposed model is more suitable for real-time applications, proposed ML framework provides an enhanced accuracy of 99.78% from the SVM-RBF classifier, along with 99.56% sensitivity, 100% specificity, and an error rate of 0.22%. The higher detection accuracy demonstrated the effectiveness of the proposed framework for medical disease diagnosis applications.

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“…This technique is carried out so that the uneven distribution of EEG data may be corrected. Kavitha et al [24] proposed a different framework for determining whether or not someone is having an epileptic seizure. EEG signals gathered from the University of Bonn in Germany and Senthil Multispecialty Hospital in India are used.…”

Section: Literature Surveymentioning

confidence: 99%

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Prasanna,

Rahman,

Bayleyegn

2023

IEEE Access

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Brain Epilepsy seizure is a critical disorder, which is an uncontrolled burst of electrical activity of brain. The early detection of brain seizure can save the life of humans. The electroencephalogram (EEG) signals may be used to automatically identify brain seizures, which is one of the most prominent solutions for this issue. However, the conventional methods are failed to classify the brain seizure effectively. So, this work implemented the Brain Epilepsy Seizure-Detection-Network (BESD-Net) using deep learning, recurrent learning properties. Initially, the dataset pre-processing is performed, which eliminates the noise, unwanted data from EEG dataset. Then, the deep learning based customized convolution neural network (CCNN) is trained on pre-processed EEG data for precise extraction of disease correlated features. The machine learning based exhaustive random forest (ERF) feature selection is used to optimize features from the CCNN features, which are highly correlated with disease dependent properties. In conclusion, the recurrent neural network (RNN) based bi-directional long short-term memory (BLSTM) is used in order to detect brain seizures from chosen ERF features. Training and testing of suggested methodology had made use of CHB-MIT Scalp EEG Database. The aforementioned model has achieved the values of 98.36%, 97.54%, 97.91%, 98% and 95.08% respectively for precision, sensitivity, F1-Score, accuracy and specificity. The findings of the simulations demonstrate that the suggested BESD-Net led to superior performance when compared to the technologies that are already in use.

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On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Cited by 37 publications

References 45 publications

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

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