bp/QMUL_Final_Project
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Finished features literature review section

Browse Source
This commit is contained in:
Sam Perry
2017-01-07 17:37:24 +00:00
parent 8da8bd7fdb
commit 44e056c00d
1 changed files with 17 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Project_Writeup.tex
+17 -27
View File
@@ -95,7 +95,7 @@ the most relevant of which are:
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 addition to their temporal characteristics.\\
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.
@@ -128,45 +128,35 @@ the most relevant of which are:
In addition to direct analysis on the signal, the ability to segment
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
Dash et al.\ use a number of time-based statistical analysis on the RR
time series for the detection of atrial fibrilation. Statistical
analyses such as RMSSD, Shannon Entropy and Turning-point Ratio are
used as feature vectors for classification of
signals~\parencite{Dash2009}. A similar approach is used by Yaghouby
et al. for the generalized classification of heart abnormality. Here,
other features such as HR Mean, Standard deviation, pNN50 and
Triangular Index are used for classification with promising
results~\parencite{Yaghouby2009}.
Frequency domain analysis of RR values can also be considered by
calculating the PSD of the RR values via similar methods for spectral
analysis as with the direct signal.
RR Frequency Domain Features
RR Time-frequency domain features VFCDM~\parencite{Dash2009}
RR Non-linear features
~\parencite{Yaghouby2009}
signals~\citeyearpar{Dash2009}. A similar approach is used by Yaghouby
et al.\ for the generalized classification of heart abnormality. Here,
a selection of linear and non-linear features are used for
classification with promising results~\citeyearpar{Yaghouby2009}.\\
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}.
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
in~\parencite{Electrophysiology1996}
\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.
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{}.
\end{itemize}
A variety of machine learning techniques trained on these extracted
features. From this, a great deal of progress has been made in classifying a
variety of cardiac abnormalities such as.
\pagebreak{}
\printbibliography{}