Interictal spike analysis using stochastic point process

2004 ◽  
Author(s):  
L. Pon ◽  
Mingui Sun ◽  
M.L. Scheuer ◽  
Ching-Chung Li ◽  
R.J. Sclabassi
Keyword(s):  
Point Process ◽  
Interictal Spike ◽  
Stochastic Point Process ◽  
Spike Analysis

scholarly journals Interictal spike analysis of high-density EEG in patients with partial epilepsy

2011 ◽  
Vol 122 (6) ◽  
pp. 1098-1105 ◽  
Author(s):  
Gang Wang ◽  
Gregory Worrell ◽  
Lin Yang ◽  
Christopher Wilke ◽  
Bin He
Keyword(s):  
Partial Epilepsy ◽  
High Density ◽  
Interictal Spike ◽  
Spike Analysis

scholarly journals A Variational Point Process Model for Social Event Sequences

2020 ◽  
Vol 34 (01) ◽  
pp. 173-180
Author(s):  
Zhen Pan ◽  
Zhenya Huang ◽  
Defu Lian ◽  
Enhong Chen
Keyword(s):  
Point Process ◽  
Real World ◽  
Process Model ◽  
Classification Model ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Modeling ◽  
Stochastic Point Process ◽  
Feature Based ◽  
Real World Datasets

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

A CONTINUOUS WAVELET-BASED APPROACH TO DETECT ANISOTROPIC PROPERTIES IN SPATIAL POINT PROCESSES

2013 ◽  
Vol 11 (02) ◽  
pp. 1350017 ◽  
Author(s):  
ROBERTO D'ERCOLE ◽  
JORGE MATEU
Keyword(s):  
Point Process ◽  
Point Processes ◽  
Unitary Operator ◽  
Random Measure ◽  
Continuous Wavelet ◽  
Two Dimensional ◽  
Mother Wavelet ◽  
Spatial Point Processes ◽  
Stochastic Point Process ◽  
Dirac Measures

A two-dimensional stochastic point process can be regarded as a random measure and thus represented as a (countable) sum of Delta Dirac measures concentrated at some points. Integration with respect to the point process itself leads to the concept of the continuous wavelet transform of a point process. Applying then suitable translation, rotation and dilation operations through a non unitary operator, we obtain a transformed point process which highlights main properties of the original point process. The choice of the mother wavelet is relevant and we thus conduct a detailed analysis proposing three two-dimensional mother wavelets. We use this approach to detect main directions present in the point process, and to test for anisotropy.

Stochastic point process modelling of rainfall. I. Single-site fitting and validation

1996 ◽  
Vol 175 (1-4) ◽  
pp. 17-46 ◽  
Author(s):  
P.S.P. Cowpertwait ◽  
P.E. O'Connell ◽  
A.V. Metcalfe ◽  
J.A. Mawdsley
Keyword(s):  
Point Process ◽  
Process Modelling ◽  
Single Site ◽  
Stochastic Point Process

Stochastic point process modelling of rainfall. II. Regionalisation and disaggregation

1996 ◽  
Vol 175 (1-4) ◽  
pp. 47-65 ◽  
Author(s):  
P.S.P. Cowpertwait ◽  
P.E. O'Connell ◽  
A.V. Metcalfe ◽  
J.A. Mawdsley
Keyword(s):  
Point Process ◽  
Process Modelling ◽  
Stochastic Point Process

On a class of parabolic differential equations driven with stochastic point processes

1975 ◽  
Vol 12 (01) ◽  
pp. 98-106
Author(s):  
K. Gopalsamy ◽  
A. T. Bharucha-Reid
Keyword(s):  
Differential Equation ◽  
Differential Equations ◽  
Boundary Value Problem ◽  
Point Process ◽  
Point Processes ◽  
Boundary Value ◽  
Parabolic Differential Equation ◽  
Parabolic Differential Equations ◽  
Stochastic Point Process ◽  
Stochastic Point Processes

This paper is concerned with the solution of an initial and boundary value problem for a parabolic differential equation driven by a stochastic point process.

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