Fig. 7. Feature construction for multi-class classification.

For every patient, the four binary models produce a feature vector of size 8, corresponding to the predictions of the recordings from the 8 anatomical sites. Those feature vectors are concatenated to form a prediction array of size 4 (classes) × 8 (sites). Then, the following operations are applied to the prediction array: (a) Column normalization of the prediction array (b) Flattening to obtain a feature vector of size 32. The final feature vector is than given as input to the multinomial logistic regression.