Cyclic alternating patterns (CAPs) occur during normal sleep, but higher CAP rates are associated with abnormal conditions, such as epilepsy. Efficient automatic classification of CAP A-phase sub-types would be of remarkable importance for the consideration of CAP as a disease bio-marker. This paper reports a multi-step methodology for the classification of A-phases subtypes. The methodology encompasses: feature extraction, feature ranking, and classification (Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Discriminant Analysis (DA)). The study was carried out on 30 subjects with nocturnal frontal lobe epilepsy. The best classifier is based on a SVM that achieved an accuracy of 71%. For each Aphase subtype, i.e. A1, A2, and A3, the sensitivities were 55%, 37% and 25%, respectively. The classifiers developed are an innovation compared to what is found on literature, because they are designed to detect all subtypes and achieved better performance values. However, the performance values still need to be improved to achieve a reliable classifier that would not need a human technician supervision.