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An example of mildly elevated ST segments in V1 to V3 that are concave down An ST elevation is considered significant if the vertical distance inside the ECG trace and the baseline at a point 0.04 seconds after the J-point is at least 0.1 mV (usually representing 1 mm or 1 small square) in a limb lead or 0.2 mV (2 mm or 2 small squares) in a ...
It is diagnosed based on an elevated J-point / ST elevation with an end-QRS notch or end-QRS slur and where the ST segment concave up. It is believed to be a normal variant. [2] Benign early repolarization that occurs as some patterns is associated with ventricular fibrillation. The association, revealed by research performed in the late 2000s ...
A graph of the bivariate convex function x 2 + xy + y 2. Convex vs. Not convex. In mathematics, a real-valued function is called convex if the line segment between any two distinct points on the graph of the function lies above or on the graph between the two points.
A function f is concave over a convex set if and only if the function −f is a convex function over the set. The sum of two concave functions is itself concave and so is the pointwise minimum of two concave functions, i.e. the set of concave functions on a given domain form a semifield.
The preferred initial diagnostic testing is the ECG, which may demonstrate a 12-lead electrocardiogram with diffuse, non-specific, concave ("saddle-shaped"), ST-segment elevations in all leads except aVR and V1 [11] and PR-segment depression possible in any lead except aVR; [11] sinus tachycardia, and low-voltage QRS complexes can also be seen ...
The strictly convex curves again have many equivalent definitions. They are the convex curves that do not contain any line segments. [21] They are the curves for which every intersection of the curve with a line consists of at most two points. [20] They are the curves that can be formed as a connected subset of the boundary of a strictly convex ...
Thus, the collection of −∞-convex measures is the largest such class, whereas the 0-convex measures (the logarithmically concave measures) are the smallest class. The convexity of a measure μ on n-dimensional Euclidean space R n in the sense above is closely related to the convexity of its probability density function. [2]
The following are among the properties of log-concave distributions: If a density is log-concave, so is its cumulative distribution function (CDF). If a multivariate density is log-concave, so is the marginal density over any subset of variables. The sum of two independent log-concave random variables is log-concave. This follows from the fact ...