Begun classification method lit review section
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Sam Perry
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@@ -133,12 +133,12 @@ I'd like to thanks anyone and everyone...
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\section{Related Work}
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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 can be divided into 4 areas,
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each of which are combined to create full classification system. These areas
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are: signal preprocessing, signal segmentation, feature extraction methods,
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and classification methods.
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The performance and evaluation of complete systems are also discussed in
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classification of PCG signals. Current methods can typically be divided into 3
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areas, each of which are combined to create full classification system. These
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areas are: signal preprocessing, signal segmentation, and classification. The
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performance and evaluation of complete systems are also discussed in
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section~\ref{performance}
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% TODO: Make flow diagram of 3 stages
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\subsection{Signal Preprocessing}
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@@ -177,8 +177,6 @@ temporal events in the resulting decomposition~\parencite[p.93]{Ari2008}.
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This may be used for analysis of transient events such as murmurs, that may
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consist of higher frequency components than normal heart sounds.
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% TODO: Add reference to table of methods
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\subsection{Signal Segmentation}
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Algorithms for the segmentation of PCG data aim to extract the structure of
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the signal over time. This is a key stage in the analysis of PCG signals as the
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@@ -285,7 +283,7 @@ segmentation, please refer to Liu et.\ al~\citeyearpar{Liu2016}
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\doublespacing
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\begin{tabulary}{\linewidth}{LLLLL}
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\dtoprule
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Author & Method & Datasets & \mbox{Reported} Results & Notes \\ \midrule
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Author & Method & Datasets & \mbox{Reported} Results & Notes \\ \bottomrule
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Springer et.\ al \citeyearpar{Springer2016} & HSMM, Logistic regression & 10,172s of recordings from 112 patients. 12,181 first and 11,627 second heart sounds. & $95.63\pm0.85\%$ & Supervised algorithm. \\
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Huiying et.\ al \citeyearpar{Liang1997b} & Normalised average Shannon energy envelope, peak picking & 37 recordings, 14 pathological murmurs and 23 physiological murmurs. 515 cycles & $91.03\%\;Ac$ & Unsupervised Algorithm. Dataset consists entirely of child recording. Optimized on full dataset \\
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Vepa et.\ al \citeyearpar{Vepa2008} & Wavelet decomposition, energy and simplicity measurement & 160 heart cycles collected from a variety of sources (training CDs, web resources) & $84\%\;Ac$ & Unsupervised Algorithm, Optimized on full dataset \\
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@@ -305,7 +303,9 @@ Gupta et.\ al \citeyearpar{Gupta2007} & Homomorphic filtering, $k$-means clus
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\doublespacing
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\subsection{Feature Extraction}
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\subsection{Classification Models}
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A wide variety of methods exist for the extraction of statistical
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features from PCG data. These features are used for the creation of
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robust, meaningful representations of the data.\\
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@@ -326,9 +326,10 @@ successfully employed for extracting spectral data for purposes such
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as heart valve disease identification and heart murmur
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detection~\parencite{Quiceno-Manrique2010a, Maglogiannis2009}.\\
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In addition to direct analysis on the signal, the ability to segment
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and extract RR values from the signal allows for their statistical
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analysis, both in the time and frequency domain, for use as features.\\
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In addition to direct analysis, the ability to segment and extract RR values
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from the signal allows for their statistical analysis, both in the time and
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frequency domain, for use as features.\\
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- Basic physionet challenge features
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Dash et al.\ use a number of time-based statistical analysis on the RR
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time series for the detection of atrial fibrillation. Statistical
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analyses such as RMSSD, Shannon Entropy and Turning-point Ratio are
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@@ -348,8 +349,6 @@ detectable through time domain analysis~\citeyearpar{Yaghouby2009}.\\
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Further in-depth analysis of statistical features for HRV can be found
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in~\parencite{Electrophysiology1996}
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\subsection{Classification Models}
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% TODO: Revise to include physionet entries
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% TODO: Add section for parameter optimization/feature selection methods
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Classification of signals for diagnostic purposes. The aim being to
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@@ -401,8 +400,59 @@ context~\citeyearpar{Orhan2013}.
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\subsection{System Performance}\label{performance}
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\subsubsection{Work prior to the Physionet Challenge}
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\subsubsection{Physionet Challenge 2016 Entries}
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\newgeometry{margin=1cm} % modify this if you need even more space
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\begin{table}[htbp]
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\captionof{table}{Summary of research prior to the Physionet Challenge 2016} \label{PriorWorkTable}
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\scriptsize
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%\centering
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\rowcolors{1}{gray!15}{white}
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\doublespacing
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\begin{tabulary}{\linewidth}{LLLLL}
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\dtoprule
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Author & Method & Datasets & \mbox{Reported} Results & Notes \\ \bottomrule
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Springer et.\ al \citeyearpar{Springer2016} & HSMM, Logistic regression & 10,172s of recordings from 112 patients. 12,181 first and 11,627 second heart sounds. & $95.63\pm0.85\%$ & Supervised algorithm. \\
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Huiying et.\ al \citeyearpar{Liang1997b} & Normalised average Shannon energy envelope, peak picking & 37 recordings, 14 pathological murmurs and 23 physiological murmurs. 515 cycles & $91.03\%\;Ac$ & Unsupervised Algorithm. Dataset consists entirely of child recording. Optimized on full dataset \\
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Vepa et.\ al \citeyearpar{Vepa2008} & Wavelet decomposition, energy and simplicity measurement & 160 heart cycles collected from a variety of sources (training CDs, web resources) & $84\%\;Ac$ & Unsupervised Algorithm, Optimized on full dataset \\
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Sun et.\ al \citeyearpar{Sun2014} & Viola integral envelope extraction, short-time modified Hilbert transform, peak picking & 6949s of recordings, from 121 patients & $97.37\%\;Ac$ & Supervised algorithm. Tolerance for segmentation accuracy not specified \\
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Sepehri et.\ al \citeyearpar{Sepehri2010} & Spectral density estimation, auto-regressive parameters, multi-layer perceptron neural network & 120 recording, from 60 patients & $93.6\%\;Ac$ & Supervised algorithm \\
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Ricke et.\ al \citeyearpar{Ricke2005} & Shannon energy (and related features), HMM & 9 recordings, from 9 patients & $98\%\;Ac$ & Supervised algorithm \\
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Schmidt et.\ al \citeyearpar{Schmidt2015} & DHMM, Auto-correlation duration features, Homomorphic envelogram & 113 recordings, from 113 patients. 8s per recording. 15 abnormal recordings & $98.8\;Se,\;98.6\;P_+$ on test set & All data recorded ``lateral to the sternum in the fourth intercostal space on the left side''. Mix of noisy and clean recordings. 40 recording used for training, 73 for testing \\
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Gill et.\ al \citeyearpar{Gill2005} & Homomorphic envelogram, Embedded HMMs & 44 recording, 17 subjects. 30-60s per recording & $98.6\%\;Ac, 96.9\;P_+$ for S1. $98.3\;Ac,\;96.5\;P_+$ for S2 & Recording taken in sub-optimal environments (noisy hospitals, offices etc...) \\
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Gupta et.\ al \citeyearpar{Gupta2007} & Homomorphic filtering, $k$-means clustering & 41 patients, 340 heart cycles. 110 normal, 124 systolic murmur, 106 diastolic murmur & $90.29\%\;Ac$ & Unsupervised Algorithm. \\ \hline
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\dbottomrule\\
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% TODO: Add footnote explanation for Ac = Accuracy
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% TODO: Add citeyearpar references to authors
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\end{tabulary}
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\end{table}
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\restoregeometry
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\newgeometry{margin=1cm} % modify this if you need even more space
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\begin{landscape}
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\begin{table}[htbp]
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\captionof{table}{Summary of research prior to the Physionet Challenge 2016} \label{PriorWorkTable}
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\scriptsize
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%\centering
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\rowcolors{1}{gray!15}{white}
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\doublespacing
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\begin{tabulary}{\linewidth}{LLLLL}
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\dtoprule
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Author & Method & Datasets & \mbox{Reported} Results & Notes \\ \bottomrule
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Springer et.\ al \citeyearpar{Springer2016} & HSMM, Logistic regression & 10,172s of recordings from 112 patients. 12,181 first and 11,627 second heart sounds. & $95.63\pm0.85\%$ & Supervised algorithm. \\
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Huiying et.\ al \citeyearpar{Liang1997b} & Normalised average Shannon energy envelope, peak picking & 37 recordings, 14 pathological murmurs and 23 physiological murmurs. 515 cycles & $91.03\%\;Ac$ & Unsupervised Algorithm. Dataset consists entirely of child recording. Optimized on full dataset \\
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Vepa et.\ al \citeyearpar{Vepa2008} & Wavelet decomposition, energy and simplicity measurement & 160 heart cycles collected from a variety of sources (training CDs, web resources) & $84\%\;Ac$ & Unsupervised Algorithm, Optimized on full dataset \\
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Sun et.\ al \citeyearpar{Sun2014} & Viola integral envelope extraction, short-time modified Hilbert transform, peak picking & 6949s of recordings, from 121 patients & $97.37\%\;Ac$ & Supervised algorithm. Tolerance for segmentation accuracy not specified \\
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Sepehri et.\ al \citeyearpar{Sepehri2010} & Spectral density estimation, auto-regressive parameters, multi-layer perceptron neural network & 120 recording, from 60 patients & $93.6\%\;Ac$ & Supervised algorithm \\
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Ricke et.\ al \citeyearpar{Ricke2005} & Shannon energy (and related features), HMM & 9 recordings, from 9 patients & $98\%\;Ac$ & Supervised algorithm \\
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Schmidt et.\ al \citeyearpar{Schmidt2015} & DHMM, Auto-correlation duration features, Homomorphic envelogram & 113 recordings, from 113 patients. 8s per recording. 15 abnormal recordings & $98.8\;Se,\;98.6\;P_+$ on test set & All data recorded ``lateral to the sternum in the fourth intercostal space on the left side''. Mix of noisy and clean recordings. 40 recording used for training, 73 for testing \\
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Gill et.\ al \citeyearpar{Gill2005} & Homomorphic envelogram, Embedded HMMs & 44 recording, 17 subjects. 30-60s per recording & $98.6\%\;Ac, 96.9\;P_+$ for S1. $98.3\;Ac,\;96.5\;P_+$ for S2 & Recording taken in sub-optimal environments (noisy hospitals, offices etc...) \\
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Gupta et.\ al \citeyearpar{Gupta2007} & Homomorphic filtering, $k$-means clustering & 41 patients, 340 heart cycles. 110 normal, 124 systolic murmur, 106 diastolic murmur & $90.29\%\;Ac$ & Unsupervised Algorithm. \\ \hline
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\dbottomrule\\
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% TODO: Add footnote explanation for Ac = Accuracy
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% TODO: Add citeyearpar references to authors
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\end{tabulary}
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\end{table}
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\end{landscape}
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\restoregeometry
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% TODO: Insert table of previous research methods, datasets and results
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