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Finished segmentation table

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Sam Perry
2017-08-02 18:39:56 +01:00
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@@ -139,10 +139,10 @@ section~\ref{performance}
\subsection{Signal Preprocessing}
There are a large number of factors that lead to variation in quality of PCG
recordings: stethescope type, make and model, it's microphone/sensors used for
recording of the data, the position used to record (i.e. lower left sternal
border, apex, pulmonic area, aortic area), built in filters/signal processing
used by the stethescope (i.e. noise filters, anti-tremor filters), medication that
recordings: stethescope type, make and model, its microphone/sensors used for
recording of the data, the position used to record (i.e.\ lower left sternal
border, apex, pulmonic area, aortic area), built in filters/signal processing
used by the stethescope (i.e.\ noise filters, anti-tremor filters), medication that
a pacient may be taking, as well as many other factors that may influence the
recorded signal~\parencite[p.4]{Pavlopoulos2004}. This presents a significant
issue when attempting to analyse and compare a dataset of signals, as
@@ -188,37 +188,41 @@ representations to assist in the splitting of the FHSs, in particular using
wavelet decomposition~\parencite{Liang1997a, Vepa2008}. These methods tend to
perform more robustly on signals of varying conditions\\ In addition, Machine
learning algorithms have been employed, such as $k$-Nearest
Neighbour~\parencite{Gupta2007} and Neural Networks~\parencite{Oskiper2002} to
improve segment classification. Particular success has been observed in
Springer's use of logistic regression and Hidden semi-Markov models
(HSMM)~\citeyearpar{Springer2016}.
Neighbour classifiers~\parencite{Gupta2007}, Neural
Networks~\parencite{Sepehri2010}, and Hidden Markov
Models (HMMs)~\parencite{Ricke2005} to improve segment classification. Particular
success has been observed in Springer's use of logistic regression and Hidden
semi-Markov models (HSMM)~\citeyearpar{Springer2016}.
Table~\ref{SegmentationTable} provides a brief overview of significant research
into PCG segmentation. For a more complete summary of the current state of PCG
segmentation, please refer to Liu et.\ al~\citeyearpar{Liu2016}
% TODO: Insert table of segmentation methods and results
\newgeometry{margin=1cm} % modify this if you need even more space
\begin{landscape}
\begin{table}[htbp]
\captionof{table}{Summary of Segmentation Algorithms} \label{SegmentationTable}
\small
\footnotesize
%\centering
\rowcolors{1}{gray!15}{white}
\doublespacing
\begin{tabulary}{\linewidth}{LLLLL}
\dtoprule
Author &
Method & Datasets
& Reported Metrics and
Results & Notes
\\ \bottomrule
Springer, D. B., Tarassenko, L., \& Clifford, G. D. (2016) & HSMM/Logistic regression & 10,172s of recordings from 112 patients. 12,181 first and 11,627 second heart sounds. & F1 score of 95.630.85\% & Supervised algorithm. \\
Huiying, Sakari, \& Iiro, (1997b) & Normalised Average Shannon Energy Envelope/Peak Picking & 37 recordings, 14 pathological murmurs and 23 physiological murmurs. 515 cycles & 91.03\% correct, 5.83\% missing, 1.17\% incorrect & Unsupervised Algorithm. Dataset consists entirely of child recording. Optimized on entire dataset \\
Gupta, C. N., Palaniappan, R., Swaminathan, S., \& Krishnan, S. M. (2007) &
Homomorphic Filtering\slash K\=/means clustering & 41 recordings (340 cycles). Mix of normal (32\%), systolic (36\%) and diastolic murmurs (32\%) & 90.29\% Ac. & Unsupervised Algorithm. \\
\dbottomrule \\
Author & Method & Datasets & \mbox{Reported} Results & Notes \\ \midrule
Springer et.\ al (2016) & HSMM/Logistic regression & 10,172s of recordings from 112 patients. 12,181 first and 11,627 second heart sounds. & $95.63\pm0.85\%\;Ac$ & Supervised algorithm. \\
Huiying et.\ al (1997b) & 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 \\
Vepa et.\ al (2008) & 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 \\
Sun et.\ al & 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 \\
Sepehri et.\ al & Spectral density estimation, auto-regressive parameters, multi-layer perceptron neural network & 120 recording, from 60 patients & $93.6\%\;Ac$ & Supervised algorithm \\
Ricke et.\ al (2005) & Shannon energy (and related features), HMM & 9 Recordings, from 9 patients & $98\%\;Ac$ & Supervised algorithm \\
Gupta et.\ al (2007) & Homomorphic filtering/K-means clustering & 41 recordings (340 cycles). Mix of normal (32\%), systolic (36\%) and diastolic murmurs (32\%) & $90.29\%\;Ac$ & Unsupervised Algorithm. \\ \bottomrule
\dbottomrule\\
\end{tabulary}
\end{table}
\end{landscape}
\restoregeometry
\subsection{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
@@ -327,6 +331,8 @@ 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}
Choice of springer algorithm allows for direct comparison with Physionet
entries
\subsection{Choice of features}
Augmentation of features using 2nd order polynomial features