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

## TL;DR

This paper introduces a recurrent GAN to generate realistic, personalized, and balanced synthetic physiological data, significantly improving sleep apnea detection accuracy across multiple classifiers.

## Contribution

It presents a novel recurrent GAN approach for data augmentation, balancing, and personalization in physiological time series data for health applications.

## Key findings

- Increased sensitivity across classifiers
- Improved kappa statistic by 0.007 to 0.182
- Effective data augmentation for sleep apnea detection

## Abstract

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.