Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

Carrault, Guy; Cordier, Marie-Odile; Quiniou, René; Wang, F.

doi:10.1016/s0933-3657(03)00066-6

Cited by 41 publications

(36 citation statements)

References 41 publications

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“…The proposed approaches include automata-based learning [16], frequency-based analysis of events [14], and inductive logic programming (ILP) [4,3]. ILP is well-suited to the construction of HLE definitions expressed in the CRS language, as these definitions can be straightforwardly translated into first-order logic descriptions used by ILP systems, and vice-versa.…”

Section: Machine Learningmentioning

confidence: 99%

“…CRS is a purely temporal reasoning system that allows for very efficient and scalable event recognition. CRS has been used in various domains, ranging from medical applications to computer network managementsee, for example, [12,4,3]. EC, which has also been used for event recognition (see, for instance, [5,30,31,29,1]), allows for the representation of temporal as well as atemporal constraints.…”

Section: Introductionmentioning

confidence: 99%

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Logic-based representation, reasoning and machine learning for event recognition

Artikis¹,

Παλιούρας²,

Portet³

et al. 2010

Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems

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Today's organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. This requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field.

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Section: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Logic-based representation, reasoning and machine learning for event recognition

Artikis¹,

Παλιούρας²,

Portet³

et al. 2010

Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems

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show abstract

“…There is a wide literature about temporal abstraction in medical domains [12,26]. The Calicot system [1], dedicated to cardiac arrhythmias detection, and the one proposed by Guimaraes [9] which is focused to sleep-related respiratory disorders are two examples. Collaborative knowledge discovery approaches exploiting the properties of multi-agent systems (open character, autonomy of its components) have been proposed recently [10] for the exploration of mechanical ventilation asynchronies.…”

Section: Application Domain: Monitoring Cardiac Patientsmentioning

confidence: 99%

Intelligent Adaptive Monitoring for Cardiac Surveillance

Quiniou¹,

Callens²,

Carrault³

et al. 2010

Computational Intelligence in Healthcare 4

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Abstract. Monitoring patients in intensive care units is a critical task. Simple condition detection is generally insufficient to diagnose a patient and may generate many false alarms to the clinician operator. Deeper knowledge is needed to discriminate among alarms those that necessitate urgent therapeutic action. We propose an intelligent monitoring system that makes use of many artificial intelligence techniques: artificial neural networks for temporal abstraction, temporal reasoning, model based diagnosis, decision rule based system for adaptivity and machine learning for knowledge acquisition. To tackle the difficulty of taking context change into account, we introduce a pilot aiming at adapting the system behavior by reconfiguring or tuning the parameters of the system modules. A prototype has been implemented and is currently experimented and evaluated. Some results, showing the benefits of the approach, are given.

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“…Inductive Logic Programming has been applied in a wide variety of domains of the Life Sciences, ranging over domains as diverse as Medical Support Systems (Carrault et al, 2003) and Computational Biochemistry (Srinivasan et al, 1994;Page & Craven, 2003).…”

Section: Life Sciencesmentioning

confidence: 99%

Induction as a Search Procedure

Konstantopoulos

Camacho

Fonseca

et al. 2008

Artificial Intelligence for Advanced Problem Solving Techniques

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This chapter introduces Inductive Logic Programming (ILP) from the perspective of search algorithms in Computer Science. It first briefly considers the Version Spaces approach to induction, and then focuses on Inductive Logic Programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors hope that this presentation of the theory and applications of Inductive Logic Programming will help the reader understand the theoretical underpinnings of ILP, and also provide a helpful overview of the State-of-the-Art in the domain.

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Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

Cited by 41 publications

References 41 publications

Logic-based representation, reasoning and machine learning for event recognition

Logic-based representation, reasoning and machine learning for event recognition

Intelligent Adaptive Monitoring for Cardiac Surveillance

Induction as a Search Procedure

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