squash commit

Project_Writeup.tex

@@ -1,5 +1,4 @@
-\documentclass[titlepage]{scrartcl}
-\usepackage{enumitem}
+\documentclass[titlepage, 12pt]{scrartcl} \usepackage{enumitem}
 \usepackage[british]{babel}
 \usepackage[style=apa, backend=biber]{biblatex}
 \DeclareLanguageMapping{british}{british-apa}
@@ -11,32 +10,36 @@
 \MakePerPage{footnote}
 \usepackage{abstract}
 \usepackage{graphicx}
+\usepackage{setspace}
 % Create hyperlinks in bibliography
 \usepackage{hyperref}
 \usepackage{amsmath}
 
+\usepackage[pass]{geometry}
+\usepackage{graphicx}
+
 \usepackage[T1]{fontenc}
 \usepackage[utf8]{inputenc}
 \usepackage{blindtext}
 \setkomafont{disposition}{\normalfont\bfseries}
 
+\usepackage{etoolbox}
 \graphicspath{{./resources/}}
 \addbibresource{~/Documents/library.bib}
 
-\newsavebox{\abstractbox}
-\renewenvironment{abstract}
-  {\begin{lrbox}{0}\begin{minipage}{\textwidth}
-   \begin{center}\normalfont\sectfont\abstractname\end{center}\quotation}
-  {\endquotation\end{minipage}\end{lrbox}%
-   \global\setbox\abstractbox=\box0 }
+%\newsavebox{\abstractbox}
+%\renewenvironment{abstract}
+%  {\begin{lrbox}{0}\begin{minipage}{\textwidth}
+%   \begin{center}\normalfont\sectfont\abstractname\end{center}\quotation}
+%  {\endquotation\end{minipage}\end{lrbox}%
+%   \global\setbox\abstractbox=\box0 }
 
-\usepackage{etoolbox}
-\makeatletter
-\expandafter\patchcmd\csname\string\maketitle\endcsname
-  {\vskip\z@\@plus3fill}
-  {\vskip\z@\@plus2fill\box\abstractbox\vskip\z@\@plus1fill}
-  {}{}
-\makeatother
+%\makeatletter
+%\expandafter\patchcmd\csname\string\maketitle\endcsname
+%  {\vskip\z@\@plus3fill}
+%  {\vskip\z@\@plus2fill\box\abstractbox\vskip\z@\@plus1fill}
+%  {}{}
+%\makeatother
 
 \DeclareCiteCommand{\citeyearpar}
     {}
@@ -67,22 +70,64 @@
    showspaces=false,
    showstringspaces=false}
 
+
 \begin{document}
-\title{ECS750P --- Final Project}
-\subtitle{\LARGE{Extraction of Statistical Features from PCG Signals for the
-Classification of Heart Abnormalities}}
+\newgeometry{lmargin=1.5cm}
+\begin{titlepage}
 
-\author{Sam Perry --- EC16039}
+    \begingroup
 
-\maketitle
+    \setlength{\tabcolsep}{1.5cm}
     
-\section{Literature Review}
-There are currently a wide variety of methods are employed for the analysis and
-classification of PCG signals. Current research focuses on a number of areas,
-the most relevant of which are:
-\begin{itemize}
-    \item Algorithms for the pre-processing and segmentation of PCG data,
-        aiming to extract the structure of the signal over time. This is a key
+    \begin{tabular}[c]{p{0.30\textwidth} | p{0.4\textwidth}}
+    
+    {\vspace{1.2cm} \Large School of Electronic Engineering and Computer Science \par} 
+    & 
+    {\vspace{1.2cm} \large Sound and Music Computing \newline Project Report \the\year \par}\\
+    
+    & {\vspace{0.5cm} \Large \textbf{Extraction of Statistical Features from PCG Signals for the
+Classification of Heart Abnormalities} \par}\\
+    
+    \vspace{0.4\textheight}
+    \includegraphics[width=5cm]{qmul_logo}
+    &
+    {\vspace{1cm} \large \textbf{Samuel Perry}}\\
+    
+    &
+    \multicolumn{1}{|r}{August \the\year}
+    
+    \end{tabular}
+
+    \endgroup
+
+\end{titlepage}
+\restoregeometry
+
+\doublespacing
+\begin{abstract}
+   Things and stuff and words...
+\end{abstract}
+
+\renewcommand{\abstractname}{Acknowledgements}
+\begin{abstract}
+I'd like to thanks anyone and everyone...
+\end{abstract}
+
+\tableofcontents
+\newpage
+
+\section{Related Work}
+There are currently a wide variety of methods employed for the analysis and
+classification of PCG signals. Current research can be divided into 3 areas,
+each of which are combined to create full classification system. These areas
+are: signal preprocessing and segmentation, feature extraction methods and
+classification methods.
+
+\subsection{Signal Preprocessing and Segmentation}
+Due to factors such as recording conditions and 
+
+Algorithms for the pre-processing and segmentation of PCG data
+aim to  extract the structure of the signal over time. This is a key
 stage in the analysis of PCG signals as the structure and relationships between the
 fundamental heart sounds (FHSs) form the basis for much of the further
 analysis performed on PCG data. A number of methods exist for the
@@ -104,7 +149,8 @@ the most relevant of which are:
 of logistic regression and Hidden semi-Markov
 models~\citeyearpar{Springer2016}.
 
-    \item A wide variety of methods exist for the extraction of statistical
+\subsection{Statistical Feature Extraction}
+A wide variety of methods exist for the extraction of statistical
 features from PCG data. These features are used for the creation of
 robust, meaningful representations of the data.\\
 The use of spectral representations for PCG data are prominent in the
@@ -146,7 +192,8 @@ the most relevant of which are:
 Further in-depth analysis of statistical features for HRV can be found
 in~\parencite{Electrophysiology1996}
 
-    \item Classification of signals for diagnostic purposes.  The aim being to
+\subsection{Signal Classification}
+Classification of signals for diagnostic purposes.  The aim being to
 distinguish healthy signals from those with certain heart
 conditions/abnormality. This is most commonly achieved by extracting
 sets of features vectors from PCG signals, followed by their
@@ -193,7 +240,25 @@ the most relevant of which are:
 taken, particularly when considering systems in a real-time
 context~\citeyearpar{Orhan2013}.
 
-\end{itemize}
+\section{Dataset}
+
+\section{Design}
+The system aims to provide robust heart abnormality detection for PCG signals,
+such that use of the system could reliably recommend further medical attention
+when neccesary. 
+\subsection{Signal Segmentation}
+\subsection{Choice of features}
+\subsection{Feature selection method}
+dimensionality reduction
+\subsection{Classification Algorithm}
+
+\section{Implementation}
+\section{Evaluation}
+Group cross-validation
+Weighted specificity and weighted Accuracy measures
+\section{Conclusion}
+
+
 
 \pagebreak{}
 \printbibliography{}