BackgroundThe systolic variation of mitral regurgitation (MR) is a pitfall in its quantification. Current recommendations advocate using quantitative echocardiographic techniques that account for this systolic variation. While prior studies have qualitatively described patterns of systolic variation no study has quantified this variation.MethodsThis study includes 41 patients who underwent cardiovascular magnetic resonance (CMR) evaluation for the assessment of MR. Systole was divided into 3 equal parts: early, mid, and late. The MR jets were categorized as holosystolc, early, or late based on the portions of systole the jet was visible. The aortic flow and left ventricular stroke volume (LVSV) acquired by CMR were plotted against time. The instantaneous regurgitant rate was calculated for each third of systole as the difference between the LVSV and the aortic flow.ResultsThe regurgitant rate varied widely with a 1.9-fold, 3.4-fold, and 1.6-fold difference between the lowest and highest rate in patients with early, late, and holosystolic jets respectively. There was overlap of peak regurgitant rates among patients with mild, moderate and severe MR. The greatest variation of regurgitant rate was seen among patients with mild MR.ConclusionCMR can quantify the systolic temporal variation of MR. There is significant variation of the mitral regurgitant rate even among patients with holosystolic MR jets. These findings highlight the need to use quantitative measures of MR severity that take into consideration the temporal variation of MR.

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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.

The exact discovery of R-peak becomes very much crucial while extracting prominent features from Electrocardiogram (ECG) signal. However, identification of R-peaks precisely becomes more challenging due to contamination of noise and fragmented QRS complexes. This paper presents an improved method of marking R-peaks. Initially, an efficient Fourier Decomposition Methodology (FDM) is used for removing noise. The accuracy of finding R-peaks can be improved by enhancing the QRS complexes using Teager Energy Operator. Hilbert Transform and Zero Cross Detector (ZCD) are used for marking the R-peaks. The MIT-BIH arrhythmia database is used for validating the proposed scheme and attained 99.97% accuracy, 99.98% of sensitivity and 99.98% of positive predictivity. The findings proved that proposed method is superior as compared to the proven techniques in the literature.

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