Multivariate autoregressive model for ECG signal forecasting
by Sarita Kansal; Prashant P. Bansod; Abhay Kumar
International Journal of Multivariate Data Analysis (IJMDA), Vol. 1, No. 2, 2017

Abstract: In this paper, multivariate autoregressive modelling is used to analyse the correlation between diagnostic components of an ECG signal. The value of diagnostic components is identified in every beat, and is measured by wavelet transform. The diagnostic components are considered as ECG variables for modelling and it represents the time series signals. The forecasting of ECG variable 'IHR' is evaluated by using multivariate autoregressive model. The model is characterised by different number of ECG variables and past values of each variable. It affects the forecasting accuracy, which is measured by mean absolute error (MAE). The results show that as the number of diagnostic components is increasing in terms of ECG variables, the forecasting accuracy is enhanced by reduction in the value of MAE. The forecasting accuracy is calculated for the forecasting horizon of 80 ECG beats.

Online publication date: Fri, 20-Oct-2017

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