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Sam Perry
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@@ -247,7 +247,7 @@ for the duration of each state in the HMM~\citeyearpar{Gill2005}. This is
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handled through the extraction of 6 duration features based primarily on peaks,
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which are then used as feature vectors for the HMM. Results of 98.6\%
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sensitivity, 96.9\% positive predictivity for S1 sounds and 98.3\% sensitivity,
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96.5\% positive predictivity were reported.
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96.5\% positive predictivity for S2 sounds were reported.
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The issue of state duration is further addressed by Schmidt et.\ al through use
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of a duration-dependent hidden Markov (DHMM)~\citeyearpar{Schmidt2015}. The
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DHMM is a modified HMM that considers the duration of the current state when
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@@ -325,15 +325,79 @@ been produced with regards to general abnormality detection, with many projects
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choosing to focus on specific conditions such as murmurs, atrial fibrillation
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and flutter, and heart valve disease. This section outlines some key research
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into these areas, alongside initial research into general abnormality
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detection.
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detection.\\
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- SVM classifier for heart valve diseas~\parencite{Maglogiannis2009}
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- Threshold classifier for atrial fibrillation and flutter~\parencite{Dash2009}
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- k-NN Classifier for murmur detection~\parencite{Quiceno-Manrique2010a}
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- Feature analysis specifically for coronary artery
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diseas~\parencite{Schmidt2015}
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- GDA and MLP Neural-net classification of general abnormalities~\parencite{Yaghouby2009}
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- SVM, k-NN and Bayesian classifier of general abnormalities~\parencite{Lubaib2016}
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Maglogiannis et.\ al present a classifier for discrimination of heart valve
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disease from regular heart sounds using an SVM
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classifier~\citeyearpar{Maglogiannis2009}.
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Roughly 100 features were extracted from the signal, based on direct analysis
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of each heart cycle component (S1, Systole, S2, Diastole) and the average
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shannon energy envelope of these components.
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A database of 198 heart sounds was curated for the project, acquired from 8
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sources, such as medical CDs and pre-existing databases.
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An accuracy of 91.43\% is reported using 10-fold stratified cross-validation.
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In addition, the project aimed to classify individual abnormalities in a 3 step
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process, by distinguishing between systolic or diastolic murmurs, and then
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distinguishing between aortic or mitral diseases. The classifier achieved
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accuracy between 90-97\% for these classifications.\\
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Ari et.\ al also propose an SVM based method for abnormality
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classification~\citeyearpar{Ari2010}.\\
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Quiceno-Manrique et.\ al demonstrate the use of various time frequency
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representations (TFR) such as short-time fourier transform, wavelet transforms,
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Wigner-Ville distribution etc\ldots, with a $k$-nearest neighbour classifier
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(k-NN) for systolic murmur detection~\citeyearpar{Quiceno-Manrique2010a}. This
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work highlights the effectiveness of alternative TFRs to traditional fourier
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methods. This method also employs Principle Component Analysis (PCA) for the
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mapping of a high dimensional feature space to a lower dimension, for the
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benefit of computational performance. Features were evaluated using a dataset
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of of 22 patients, 6 of which were labeled as having a systolic murmur. The
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highest reported accuracy was achieved using MFCCs as the primary feature
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vector achieving a 98\% accuracy on 10-fold cross validation.\\
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Schmidt et.\ al aim to find features that can be used for classification of
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coronary artery disease through detection of small
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murmurs~\citeyearpar{Schmidt2015}. A large number of features are then
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calculated to provide vectors for classification. Parametric spectral features
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such as ARMA are used, alongside instantaneous frequency and octave power
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measurements. These are combined with complexity features such as sample
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entropy and simplicity. Complexity features are chosen in an attempt to exploit
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the likely stochastic nature of murmurs, when compared to normal heart sounds.
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Given the large number of features calculated, PCA is used to retain only the
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most relevant information. Quadratic discriminant analysis (QDA) is then used
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as a classifier to provide a final accuracy score of 73\%.\\
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General abnormality detection algorithms are significantly less common prior to
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the challenge. Reed et.\ al implement a simple classification using artificial
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neural networks (ANNs) and wavelet decomposition~\citeyearpar{Reed2004}.
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However, due to the comparitively small sample size used for training (1
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patient per abnormality, 4 cycles per patient), a reported accuracy of 100\%
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would likely generalise poorly.
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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}{LLLLLL}
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\dtoprule
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Author & Pre-processing/segmentation & Features & Classification Method & Dataset & Reported Accuracy \\ \midrule
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Maglogiannis et.\ al & Wavelet decomposition, Shannon energy peak picking & Features derived from wavelet decomposition and PCG segmentations & SVM & 198 recordings, 38 normal, 41 AS systolic murmur, 43 MR systolic murmur, 38 AR diastolic murmur, 38 MS diastolic murmur & $91.43\%\;Ac$ \\
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Ari et.\ al & Amplitude envelope peak picking~\parencite{Ari2007} & Wavelet based features & LSSVM & 64 patients, 64 recordings, 512 cycles & $88.750-100\%\;Ac$ (dependant on abnormality type) \\
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Quiceno-Manrique et.\ al & Downsampled to 4KHz, Normalised to maximum of signal, ECG assisted QRS complex detection algorithm used for segmentation & Spectral features derived from STFT, Wavelet decomposition and quadratic energy distributions & $k$-NN & 22 patients, 16 normal, 6 abnormal, 8 recordings (12s) per patient & $98\%\;Ac$ \\
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Schmidt et.\ al & Signal filtered into frequency bands, Segmented by HMM based method+hand corrected, removal of high variance sub-segments to remove noise & Parametric spectral features (AR, ARMA and Music), Instantaneous frequency and amplitude, Power in octave bands & QDA & 435 Recordings, 133 patients, 70 normal, 63 abnormal & $73\%\;Ac$ \\
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Reed et.\ al & & Wavelet decomposition coefficients, PCA feature reduction & ANN & 5 patients, 4 cycles per patient & $100\%\;Ac$ \\
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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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\subsubsection{Physionet challenge entries}
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scoring method
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@@ -348,31 +412,6 @@ K-NN~\parencite{Bobillo2016}
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- Convolutional neural networks, MFCCs~\parencite{Rubin2016}
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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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