2013 Asilomar Conference on Signals, Systems and Computers 2013
DOI: 10.1109/acssc.2013.6810619
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Online Bayesian change point detection algorithms for segmentation of epileptic activity

Abstract: Epilepsy is a dynamic disease in which the brain transitions between different states. In this paper, we focus on the problem of identifying the time points, referred to as change points, where the transitions between these different states happen. A Bayesian change point detection algorithm that does not require the knowledge of the total number of states or the parameters of the probability distribution modeling the activity of epileptic brain in each of these states is developed in this paper. This algorith… Show more

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Cited by 59 publications
(35 citation statements)
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“…In addition, a simplification was introduced that reduces the algorithm complexity from n 2 to n using a simple approximation. The key idea is to compute the joint probability weights for only a fixed number of nodes, instead of computing these weights at all nfalse(n-1false)2 nodes [7]. …”
Section: Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, a simplification was introduced that reduces the algorithm complexity from n 2 to n using a simple approximation. The key idea is to compute the joint probability weights for only a fixed number of nodes, instead of computing these weights at all nfalse(n-1false)2 nodes [7]. …”
Section: Reviewmentioning
confidence: 99%
“…Research studies investigate change point detection for specific medical issues such as sleep problems, epilepsy, magnetic resonance imaging (MRI) interpretation, and understanding of brain activities [6][7][8][9]. …”
Section: Introductionmentioning
confidence: 99%
“…First works date back to the 1950s (e.g. Page, 1955), but the topic has stayed the subject of active research until today, with methods being further refined and applied to many different domains, such as remote sensing (Touati et al, 2019), audio signal processing (Rybach et al, 2009), or medical condition monitoring (Malladi et al, 2013). Refer to Aminikhanghahi and Cook (2017) for an overview of time series CPD methods.…”
Section: Methods For Change-point Detectionmentioning
confidence: 99%
“…First works date back to the 1950s (e.g. Page (1955)) but the topic has stayed the subject of active research until today, 5 with methods being further refined and applied to many different domains, such as remote sensing (Touati et al (2019)), audio signal processing (Rybach et al (2009)), or medical condition monitoring (Malladi et al (2013)). Refer to Aminikhanghahi and Cook (2017) for an overview on time series CPD methods.…”
Section: Methods For Change-point Detectionmentioning
confidence: 99%