Introduction: Epileptic seizure detection is a key step for both researchers and epilepsy specialists for epilepsy assessment due to the non-stationariness and chaos in the electroencephalogram (EEG) signals. Current research is directed toward the development of an efficient method for epilepsy or seizure detection based the limited penetrable visibility graph (LPVG) algorith...

Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detec...

Sleep is an essential integrant in everyone's daily life. Thereby, it important but challenging problem to construct a reliable and stable system, that can monitor user's sleep quality automatically. In this brief, we combine complex network deep learning propose novel Graph-Temporal fused dual-input Convolutional Neural Network (CNN) method detect stages by using the Sleep-EDF database. Firstl...

We study a new class of visibility problems based on the notion of α-visibility. Given an angle α and a collection of line segments S in the plane, a segment t is said to be α-visible from a point p, if there exists an empty triangle with one vertex at p and the side opposite to p on t such that the angle at p is α. In this model of visibility, we study the classical variants of point visibilit...

Visibility computations are central in any computer graphics application. The most common way to reduce this expense is the use of approximate approaches using spatial subdivision. More recently analytic approaches efficiently encoding visibility have appeared for 2D (the visibility complex) and for certain limited cases in 3D (aspect graph, discontinuity meshes). In this paper we propose a new...