Based on the results of our study, we concluded that IVRa velocity is an important pw-TDI parameter in the evaluation of LVDDF, especially in differentiating pseudonormal LVDDF type from normal LV inflow.

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In obese patients of older ages, there is evidence of left and right ventricular dysfunction.

The question whether DHF is a reality or all heart failures are systolic may be answered as follows. Subtle systolic dysfunction may be associated with the tendency to develop DHF in patients with preserved LV ejection fraction. As in systolic heart failure (EF < 45%), in patients with preserved systolic function (EF > or = 45%), systolic and diastolic functions may impair together. The pw-TDI method may be more sensitive than standard echocardiography parameters in detection of systolic dysfunction in cases with preserved EF.

Detecting outliers in real time from multivariate streaming data is a vital and challenging research topic in many areas. Recently introduced the incremental Local Outlier Factor (iLOF) approach and its variants have received considerable attention as they achieve high detection performance in data streams with varying distributions. However, these iLOF-based approaches still have some major limitations: i) Poor detection in high-dimensional data; ii) The difficulty of determining the proper nearest neighbor number k; iii) Instead of labeling the outlier, assigning a score to each sample that indicates the probability to be an outlier; iv) Inability to detect a long sequence (small cluster) of outliers. This article proposes a new robust outlier detection method (RiLOF) based on iLOF that can effectively overcome these limitations. In the RiLOF method, a novel metric called Median of Nearest Neighborhood Absolute Deviation (MoNNAD) has been developed that uses the median of the local absolute deviation of the samples LOF values. Unlike the previously reported LOF-based approaches, RiLOF is capable of achieving outlier detection in different data stream applications using the same hyperparameters. Extensive experiments performed on 15 different realworld data sets demonstrate that RiLOF remarkably outperforms 12 different state-of-the-art competitors.INDEX TERMS Incremental learning, local outlier factor (LOF), new metric, outlier detection, robustness.

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IVRa velocity exhibits a positive correlation with LV geometry changes indicating that IVRa velocity is affected by LV geometry like the other parameters influenced by LV geometry.

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