In the application of remote cardiovascular monitoring, the computational complexity and power consumption need to be maintained in a considerable level in order to prevent the limitations introduced by the computationally constrained equipment’s that perform the process of continuous
monitoring and analysis. In this paper, a Circulant Matrix-based Continuous Wavelet Transform (CM-CWT)-based feature extraction mechanism is contributed to minimizing the computational complexity incurred during the process of feature extraction from the input ECG signals. This proposed CM-CWT
mechanism derives the advantages of the Circulant Matrix-based Continuous Wavelet Transform and Gradient-based filtering design for achieving excellent feature extraction from ECG signals with low computational complexity. The experimental investigation of the proposed CM-CWT mechanism is
conducted using the factors of computational complexity, sensitivity, prediction accuracy and error rate for estimating its predominance over the compared DWT-HAAR and HIFEA approaches used for ECG feature extraction. The experiments of the proposed CM-CWT mechanism on an average is estimated
to reduce the error rate to the maximum of 21% compared to the existing DWT-HAAR and HIFEA approaches used for ECG feature extraction.
