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\begin{document}
\title{Machine Learning Algorithms for Epileptic Seizure}
\author[Δεσποινα Μιχαηλιδη]
{Δ.~Μιχαηλιδη\inst{1}}
\institute[ΠΙ]
{

   \inst{1} Πανεπιστημιο Ιωαννινων \\
    Τμημα Μαθηματικων \\
    E:ma12522@uoi.gr
 }
\date{}
\logo{\includegraphics[height=1cm]{logo.png}}
\maketitle
\begin{frame}
\frametitle{Περιεχομενα Παρουσιασης}
\tableofcontents
\begin{itemize}
\item Εισαγωγη
\item Περιγραφη του προβληματος και περιγραφη του αλγοριθμου και των δεδομενων εφαρμογης του
\item Πειραματικο Σεναριο
\item Συμπερασματα
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Εισαγωγη}
\begin{itemize}
\item Epileptic Seizure associated with abnormal activities caused by a disorder of the brain central nervous system.
\pause
\item Epilepic seizure occurs at unpredictable time and usually without warning.
\pause
\item People with epilepsy have many symptoms, such us:
\begin{enumerate}
	\item loss of awareness
	\item unusual behavior
	\item confusion
\end{enumerate}
\pause
\item One of 10 people will have at least one epileptic seizure during a normal lifespan and a third of them will develop epilepsy.
\pause
\item Epilepsy accounts for 1\% of the global burden of disease
\end{itemize}
\end{frame}


\begin{frame}
\frametitle{Περιγραφη του προβληματος} 
\begin{itemize}
\item The most common way to detect the epileptic seizure is through analysis of the EEG( electroencephalogram), that shows the brain activity.
\pause
\item EEG signals show information for the frequency.
\pause
\item The frequency makes EEG curves that show the brain activity with the time parameter.
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Περιγραφη των δεδομενων}
\begin{itemize}
\item The dataset consists of 11500 samples(pieces of information), each with 178 features and the samples are normally distributed and are categorized into five different classes.
\end{itemize}
\end{frame}

\begin{frame}
\centering
\includegraphics[scale=0.5]{photo3}
\end{frame}


\begin{frame}
\frametitle{Περιγραφη του αλγοριθμου}
\begin{itemize}
\item The frequency components of the EEG are extracted by using the discrete wavelet transform(DWT), which is a method of analysis for non-stationary signals. It is used for disintegrate the EEG signals into specific subbands.
\pause
\item This method is applied to the input of machine learning algorithms (ANN), (SVM), (K-NN)
\pause
\item Artificial Neural Network classifier is simulating working principle of the human brain
\pause
\item Support Vector Machine algorithms gives results in signal processing
\pause
\item K- Nearest Neighbor is a non parametric classification method
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Εφαρμογη των δεδομενων}
\begin{itemize}
\item Χρησιμοποιωντας τα παραπανω δεδομενα, δημιουργουμε εναν αλγοριθμο σε python , ο οποιος  θα δινει απαντηση για το αν ενας ασθενης εχει 'η θα εμφανισει επιληπτικη κριση
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Πειραματικο Σεναριο}
\begin{itemize}
\item We have 200 EEG data belong to epileptic seizure patients and 200 EEG data belong to normal patients
\pause
\item We use the extraction method DWT
\pause
\item According to ANN, SVM, K-NN algorithms we have the results:
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Πειραματικο σεναριο: Αποτελεσματα}
\begin{center}
\begin{tabular}{|c|c|}
True Epilepsy & True Normal \\
\hline
200 & 1\\
\hline
0 & 199 \\
\end{tabular}
\end{center}

\begin{center}
\begin{tabular}{|c|c|}
True Epilepsy & True Normal \\
\hline
200 & 2 \\
\hline
0 & 198 \\
\end{tabular}
\end{center}

\begin{center}
\begin{tabular}{|c|c|}
True Epilepsy & True Normal \\
\hline
200 & 0\\
\hline
0 & 200\\
\end{tabular}
\end{center}
\end{frame}

\begin{frame}
\frametitle{Συμπερασματα}
\begin{itemize}
\item The epileptic seizure data set was classified using different classification.
\pause
\item Epileptic seizure is a serious disorder of the brain , which must be prevented in time
\end{itemize}
\end{frame}

\end{document}
