Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals

Elizabeth Ann Maharaj, Andres Modesto Alonso

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In analysing ECG data, the main aim is to differentiate between the signal patterns 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 analyses. 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 on 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 compares favourably. 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.
Original languageEnglish
Pages (from-to)67 - 87
Number of pages21
JournalComputational Statistics and Data Analysis
Volume70
DOIs
Publication statusPublished - 2014

Cite this

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title = "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals",
abstract = "In analysing ECG data, the main aim is to differentiate between the signal patterns 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 analyses. 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 on 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 compares favourably. 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.",
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Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals. / Maharaj, Elizabeth Ann; Alonso, Andres Modesto.

In: Computational Statistics and Data Analysis, Vol. 70, 2014, p. 67 - 87.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

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AU - Maharaj, Elizabeth Ann

AU - Alonso, Andres Modesto

PY - 2014

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AB - In analysing ECG data, the main aim is to differentiate between the signal patterns 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 analyses. 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 on 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 compares favourably. 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.

U2 - 10.1016/j.csda.2013.09.006

DO - 10.1016/j.csda.2013.09.006

M3 - Article

VL - 70

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JO - Computational Statistics and Data Analysis

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SN - 0167-9473

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