Zebra mussels’ behaviour detection, extraction and classification using wavelets and kernel methods

• We have developed a fully automated method for extraction and analysis of the behaviour of Dreissena polymorpha.
• We have evaluated usefulness of the feature set used for classification.
• We have proposed a framework for the classification of control and stress conditions for the purpose of the risk analysis.

This paper concerns the detection, feature extraction and classification of behaviours of Dreissena polymorpha. A new algorithm based on wavelets and kernel methods that detects relevant events in the collected data is presented. This algorithm allows us to extract elementary events from the behaviour of a living organism. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions.