Tom Croonenborghs

JBHI Journal 2014 Journal Article

Accelerometry-Based Home Monitoring for Detection of Nocturnal Hypermotor Seizures Based on Novelty Detection

• Kris Cuppens
• Peter Karsmakers
• Anouk Van de Vel
• Bert Bonroy
• Milica Milosevic
• Stijn Luca
• Tom Croonenborghs
• Berten Ceulemans

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency, and wavelet-based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and nonepileptic movements. This classification is only based on a nonparametric estimate of the probability density function of normal movements. Such approach allows us to build patient-specific models to classify movement data without the need for seizure data that are rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure; otherwise, it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95. 24% and a positive predictive value of 60. 04%. However, there is a noticeable interpatient difference.

AIIM Journal 2014 Journal Article

Detecting rare events using extreme value statistics applied to epileptic convulsions in children

• Stijn Luca
• Peter Karsmakers
• Kris Cuppens
• Tom Croonenborghs
• Anouk Van de Vel
• Berten Ceulemans
• Lieven Lagae
• Sabine Van Huffel

Objective Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. Methods Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. Results When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. Conclusions A person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties.

AAAI Conference 2008 Conference Paper

Bayes-Relational Learning of Opponent Models from Incomplete Information in No-Limit Poker

• Marc Ponsen
• Tom Croonenborghs

We propose an opponent modeling approach for No- Limit Texas Hold’em poker that starts from a (learned) prior, i. e. , general expectations about opponent behavior and learns a relational regression tree-function that adapts these priors to specific opponents. An important asset is that this approach can learn from incomplete information (i. e. without knowing all players’ hands in training games).

ECAI Conference 2008 Conference Paper

Using Decision Trees as the Answer Networks in Temporal Difference-Networks

• Laura-Andreea Antanas
• Kurt Driessens
• Jan Ramon
• Tom Croonenborghs

IJCAI Conference 2007 Conference Paper

• Tom Croonenborghs
• Jan Ramon
• Hendrik Blockeel
• Maurice Bruynooghe

In recent years, there has been a growing interest in using rich representations such as relational languages for reinforcement learning. However, while expressive languages have many advantages in terms of generalization and reasoning, extending existing approaches to such a relational setting is a non-trivial problem. In this paper, we present a first step towards the online learning and exploitation of relational models. We propose a representation for the transition and reward function that can be learned online and present a method that exploits these models by augmenting Relational Reinforcement Learning algorithms with planning techniques. The benefits and robustness of our approach are evaluated experimentally.