Fuzzy rule learning with ACO in epilepsy crisis identification

This study is focused on developing models for identifying epilepsy convulsions in order to enhance the anamnesis of the patient. A 3D accelerometer built-in wearable device is placed on the dominant wrist to gather data from participants. Based on the data gathered from the sensor, a Fuzzy Rule Based System is learned. On the one hand, statistical data from a set of patients is used to set up the partition data base; on the other hand, the Fuzzy rule base is learned using Ant Colony Optimization. Results show this approach faster and easier to learn than previous research. Introducing minor changes in the fuzzy reasoning produces even more robust models. The presented study shows a valid research path for the identification of the epilepsy convulsions.