Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection

Nikolaidis, Konstantinos, Kristiansen, Stein, Goebel, Vera, Plagemann, Thomas, Liestøl, Knut, Kankanhalli, Mohan

arXiv.org Machine Learning 

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.

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