Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

The Internet of Things (IoT) is an indispensable part of the healthcare system since it creates a link between the doctor and the patient for remote medical consultations. IoT-based seizure prediction detects the seizures and monitors the health of patients remotely. The disease seizure is categorized with the sudden and repeated malfunction of the neurons of the brain. To protect patient's lives, it's critical to recognise the risk of an epileptic seizure. In this research a hybrid cuckoo finch optimization is proposed tuned Deep-CNN (Deep-Convolutional Neural Network) classifier recognize and predict the occurrence of epileptic seizure using the electroencephalogram (EEG) signal data obtained through IoT. Initially, the gathered data is pre-processed and subjected to frequency band generation. Then there are the notable characteristics, such as Statistical features, Wavelet features, Entropy-based features, Spectral features, CPR (Common Spatial Patterns) and Logarithmic band power are extracted and concatenated. The optimal electrode selection is done by using the proposed hybrid cuckoo finch optimization that inherits characteristics of the intrusive and attentive search agents. The data is finally normalized and fed to proposed hybrid cuckoo finch optimization tuned Deep-CNN to classify the seizure disease. The specificity, accuracy and sensitivity of the proposed model is attained as 92.5212%, 97.7648%, and 95.6324%, which demonstrates efficient performance of the proposed seizure prediction model.

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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.