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\newacronym{COPD}{COPD}{Chronic Obstructive Pulmonary Disease}
\newacronym{SVM}{SVM}{Support Vector Machine}
\newacronym{CNN}{CNN}{Convolutional Neural Network}
\newacronym{CHF}{CHF}{Congestive heart failure}
\newacronym{CO2}{CO2}{carbon dioxide}
\newacronym{EEG}{EEG}{Electroencephalogram}

The electroencephalogram (EEG) is the preferred scientific approach for recording brain electrical activity. The EEG can assist doctors in diagnosing a variety of neurological diseases, most significantly epilepsy. Seizure is an unexpected event of the brain which occurs due to a rapid change in the electrical activity of the neurons. The repeated unprovoked seizures are evidence of the most common neurological disease known as epilepsy. Electroencephalogram (EEG) is used as a bio-marker to diagnose epilepsy and it allow highly trained neurological experts to recognize the occurrence of seizures. It is a time-consuming process for a specialist. In comparison with a visual inspection of EEG signals by doctors, machine learning techniques are more consistent. Thus, automatic EEG seizure detection has a great deal of interest in neuro-informatics among researchers. This work has introduced different automatic seizure classification models with machine learning and deep learning techniques. Initially, a machine learning model with EMD-based dynamic features is proposed to classify seizure and healthy subjects. Subsequently, various deep learning architectures such as a 1D CNN-FCM, a 1-D CNN-FSVM, a hierarchical attention mechanism and a shrinkage based CNN-SVM are proposed to classify seizure subjects directly from the raw EEG signals. Automated quantitative analysis to differentiate between healthy and ictal, between healthy and inter-ictal, and between ictal and inter-ictal has been implemented. This study was done on two datasets, one is publicly available Bonn university database and another is a real-time data collected in Ramesh hospitals, India. Furthermore, the experimental results were clinically validated. Lower computation time and better accuracy achieved depict the effectiveness of the proposed methods to be used in the classification of healthy and seizure subjects.

     
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\textbf{Keywords:} {\it Seizure}, {\it EEG}, {\it EMD}, {\it CNN}, {\it Attention mechanism}. 
