Sam Perry
122 lines
4.3 KiB
TeX
122 lines
4.3 KiB
TeX
\documentclass[titlepage]{scrartcl}
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\begin{document}
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\title{ECS750P --- Final Project}
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\subtitle{\LARGE{Extraction and Analysis of RRi from PCG Signals for the
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Classification of Heart Abnormalities}}
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\author{Sam Perry --- EC16039}
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\maketitle
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\section{Literature Review}
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There are currently a wide variety of methods employed for the analysis and
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classification of PCG signals. Current research focuses on a number of areas,
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the most relevant of which are:
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\begin{itemize}
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\item Algorithms for the segmentation of PCG data, aiming to extract the
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structure of the signal over time. This is a key stage in the analysis
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of PCG signals as relationships between the fundamental heart sounds
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(FHSs) form the basis for much of the further analysis performed on PCG
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data. A number of methods exist for the extraction of FHSs. Some rely on direct extraction of
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peaks in the time domain to determine the structure of a
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signal. These methods perform various transformation in order to
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accentuate the transient events.~\parencite{Groch1992, Liang1997}. However, these methods
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tend to suffer significantly from background noise and so perform
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poorly in sub-optimal conditions.\\
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Other methods rely on spectral representations to
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assist in the splitting of the FHSs, in particular using wavelet
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decomposition ~\parencite{}. Machine learning
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algorithms have also been widely employed, such as k Nearest
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Neighbour~\parencite{} and Neural Networks~\parencite{} for
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predictions. Particular success has been observed in Springer's use of
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logistic regression and Hidden semi-Markov models~\citeyearpar{Springer2016}
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\item Methods for the extraction of statistical features from PCG data in
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order to create robust, meaningful representations of the data.
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\item Classification of signals for diagnostic purposes. The aim being to
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distinguish healthy signals from those with certain heart
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conditions/abnormality. Machine learning techniques are commonly used
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in order to distinguish between signals automatically, based on prior
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feature extraction.
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it is noted in that there is a lack of research into other machine
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learning techniques such as bayesian classification and
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SVMs~\citeyearpar{}.
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\end{itemize}
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A variety of machine learning techniques trained on these extracted
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features. From this, a great deal of progress has been made in classifying a
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variety of cardiac abnormalities such as.
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\printbibliography{}
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\end{document}
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