Publication: Discriminant analysis of multivariate time series using wavelets
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2012-02
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Abstract
In analyzing ECG data, the main aim is to differentiate between the signal patterns
of those of healthy subjects and those of individuals with specific heart conditions.
We propose an approach for classifying multivariate ECG signals based on
discriminant and wavelet analyzes. For this purpose we use multiple-scale wavelet
variances and wavelet correlations to distinguish between the patterns of
multivariate ECG signals based on the variability of the individual components of
each ECG signal and the relationships between every pair of these components.
Using the results of other ECG classification studies in the literature as references,
we demonstrate that our approach applied to 12-lead ECG signals from a particular
database, displays quite favourable performance. We also demonstrate with real
and synthetic ECG data that our approach to classifying multivariate time series out
performs other well-known approaches for classifying multivariate time series. In
simulation studies using multivariate time series that have patterns that are different
from that of the ECG signals, we also demonstrate very favourably performance of
this approach when compared to these other approaches.
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Time series, Wavelet Variances, Wavelet Correlations, Discriminant Analysis