Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals |
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Affiliation: | 1. Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea;2. Department of Agricultural Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan;3. Department of Civil Engineering, Inha University, Incheon 22212, Republic of Korea;4. Department of Civil and Environmental Engineering, Hanyang University, Ansan 15588, Republic of Korea |
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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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Keywords: | Discriminant analysis Wavelet variances Wavelet correlations ECG signals |
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