Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
This addresses data scarcity and imbalance issues for sleep apnea detection, but it is incremental as it applies an existing GAN framework to a specific domain.
The paper tackled the problem of insufficient and unbalanced labeled data in health applications by designing a recurrent Generative Adversarial Network to generate synthetic data for augmentation, applied to sleep apnea detection, resulting in classifiers showing a consistent increase in sensitivity and kappa statistic improvements between 0.007 and 0.182.
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.