  In this era, big data applications including biomedical are becoming
attractive as the data generation and storage is increased in the last years.
The big data processing to extract knowledge becomes challenging since the data
mining techniques are not adapted to the new requirements. In this study, we
analyse the EEG signals for epileptic seizure detection in the big data
scenario using Rotation Forest classifier. Specifically, MSPCA is used for
denoising, WPD is used for feature extraction and Rotation Forest is used for
classification in a MapReduce framework to correctly predict the epileptic
seizure. This paper presents a MapReduce-based distributed ensemble algorithm
for epileptic seizure prediction and trains a Rotation Forest on each dataset
in parallel using a cluster of computers. The results of MapReduce based
Rotation Forest show that the proposed framework reduces the training time
significantly while accomplishing a high level of performance in
classifications.
