  Supervised machine learning applications in the health domain often face the
problem of insufficient training datasets. The quantity of labelled data is
small due to privacy concerns and the cost of data acquisition and labelling by
a medical expert. Furthermore, it is quite common that collected data are
unbalanced and getting enough data to personalize models for individuals is
very expensive or even infeasible. This paper addresses these problems by (1)
designing a recurrent Generative Adversarial Network to generate realistic
synthetic data and to augment the original dataset, (2) enabling the generation
of balanced datasets based on heavily unbalanced dataset, and (3) to control
the data generation in such a way that the generated data resembles data from
specific individuals. We apply these solutions for sleep apnea detection and
study in the evaluation the performance of four well-known techniques, i.e.,
K-Nearest Neighbour, Random Forest, Multi-Layer Perceptron, and Support Vector
Machine. All classifiers exhibit in the experiments a consistent increase in
sensitivity and a kappa statistic increase by between 0.007 and 0.182.
