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Finished first draft of literature review

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\begin{document}
\title{ECS750P --- Final Project}
\subtitle{\LARGE{Extraction and Analysis of Statistical Features from PCG
Signals for the Classification of Heart Abnormalities}}
\subtitle{\LARGE{Extraction of Statistical Features from PCG Signals for the
Classification of Heart Abnormalities}}
\author{Sam Perry --- EC16039}
\maketitle
@@ -80,30 +81,28 @@ 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 segmentation of PCG data, aiming to extract the
structure of the signal over time. This is a key stage in the analysis
of PCG signals as 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 extraction of FHSs. Some rely
on direct extraction of peaks in the time domain to determine the
structure of a signal. These methods perform various transformation in
order to accentuate the transient events with the intention of
isolating them~\parencite{Groch1992, Liang1997}. However, these methods
tend to suffer significantly from background noise and so perform
poorly in sub-optimal conditions.\\
\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
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
extraction of FHSs. Some rely on direct extraction of peaks in the time
domain to determine the structure of a signal. These methods perform
various transformation in order to accentuate the transient events with
the intention of isolating them~\parencite{Groch1992, Liang1997}.
However, these methods tend to suffer significantly from background
noise and so perform poorly in sub-optimal conditions.\\
Other methods rely on spectral representations to assist in the
splitting of the FHSs, in particular using wavelet
decomposition~\parencite{LiangHuiying1997, Vepa2008}. This allows for
the separation of components based on their frequency content in
place of, or in addition to their temporal characteristics.\\
In addition, Machine learning algorithms have been employed, such as k
Nearest Neighbour~\parencite{Gupta2007} and Neural
the separation of components based on their frequency content in place
of, or in addition to their temporal characteristics.\\
In addition, Machine learning algorithms have been employed, such as
$k$-Nearest Neighbour~\parencite{Gupta2007} and Neural
Networks~\parencite{Oskiper2002} to improve segment classification.
More recently, particular success has been observed in Springer's use
of logistic regression and Hidden semi-Markov
models~\citeyearpar{Springer2016}.
\item Signal Pre-processing?
Removal of ectopic beats in RR estimation~\parencite{Dash2009}
\item A wide variety of methods exist for the extraction of statistical
features from PCG data. These features are used for the creation of
@@ -129,7 +128,7 @@ the most relevant of which are:
and extract RR values from the signal allows for their statistical
analysis, both in the time and frequency domain, for use as features.\\
Dash et al.\ use a number of time-based statistical analysis on the RR
time series for the detection of atrial fibrilation. Statistical
time series for the detection of atrial fibrillation. Statistical
analyses such as RMSSD, Shannon Entropy and Turning-point Ratio are
used as feature vectors for classification of
signals~\citeyearpar{Dash2009}. A similar approach is used by Yaghouby
@@ -139,22 +138,61 @@ the most relevant of which are:
Frequency domain analysis of RR values are also used by calculating the
PSD of the RR values via approaches such as VFCDM.\ This form of
approach allows for higher resolution time-frequency representations of
the RR data than approach such as the FFT or wavelet transform~\parencite{Wang2006}.
the RR data than approaches such as the FFT or wavelet transform~\parencite{Wang2006}.
From a spectral representations such as this, Yaghouby et al.\
demonstrate the use of such descriptors for the discrimination between
sympathetic and parasympathetic contents of the signal, not directly
detectable through time domain analysis~\citeyearpar{Yaghouby2009}.\\
Further in depth analysis of statistical features for HRV can be found
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
\item Classification of signals for diagnostic purposes. The aim being to
distinguish healthy signals from those with certain heart
conditions/abnormality. Machine learning techniques are commonly used
in order to distinguish between signals automatically, based on prior
feature extraction.\
it is noted in that there is a lack of research into other machine
learning techniques such as bayesian classification and
SVMs~\citeyearpar{}.
conditions/abnormality. This is most commonly achieved by extracting
sets of features vectors from PCG signals, followed by their
classification, most commonly using machine learning algorithms for
automatic classification. The features extracted and classification
algorithms applied vary across the literature based on factors such as
the diagnostic aims of the classification and computing performance
requirements.\\
Artificial neural networks and support vector machines have proven to
be popular choices for classification. Much success has been seen in
employing these machine learning techniques for classification across
both PCG and ECG data for conditions such as chronic heart failure,
atrial fibrillation and flutter, diastolic murmurs, and for general
pathology detection~\parencite{Cathers1995, Wu1995, Bung2000,
Lubaib2016, Maji2014, Ari2010, Maglogiannis2009}. Results do vary based
on the combination of features and exact classification methods used.
However, encouraging results are presented with highly accurate
classifications for general abnormality detection and for more specific
pathological condition detection.\\
However, there is a lack of research into other machine learning
techniques such as bayesian classification~\parencite{Lubaib2016},
$k$-Nearest Neighbour~\parencite{Quiceno-Manrique2010a, Lubaib2016} and
Linear Regression~\parencite{Orhan2013}. Studies that utilize these
methods for classification have generated promising results. There is
therefore the potential for further research into exploiting the
benefits of these techniques for heart abnormality detection.\\
The selection of features used for classification also depends
predominantly on the aims for the classification. For general
abnormality classification, spectral representations such as wavelet
transformations, VFCMD, FFTs and MFCCs are a popular
choice~\parencite{Bung2000, Wu1995, Yaghouby2009, Dash2009}. Their
multi-dimensional representation of the data reveals details in the
signal that cannot be seen through a 1 dimensional time series alone,
allowing for more accurate classification. Higher-level statistical
methods are also widely used for both time and spectral
representations~\parencite{Bung2000, Quiceno-Manrique2010a,
Schmidt2015, Dash2009, Yaghouby2009}. These allow for the
classification based on more specific statistical properties of the
data. It is highlighted by Orhan that Higher level statistical methods
may add considerable complexity to computations, and so care should be
taken, particularly when considering systems in a real-time
context~\citeyearpar{Orhan2013}.
\end{itemize}
\pagebreak{}