Dealing with Imbalanced Sleep Apnea Data Using DCGAN

Abstract

Data in the health sector are often lacking and unbalanced. It is because collecting data takes time and many resources. One example is sleep apnea data which takes about 8–10 hours to get data and uses specialized hardware like polysomnography (PSG). This study proposes a data augmentation technique to handle unbalanced data using DCGAN and several deep learning models such as 1D-CNN, ANN, LSTM, and 1D-CNN+LSTM as a classifier for apnea detection. The DCGAN architecture used is CNN on the generator and discriminator. DCGAN will create new synthetic data by mimicking the original dataset. This experiment uses a dataset from PhysioNet, the Apnea-ECG, and the MIT-BIH PSG Database. Furthermore, the dataset is preprocessed to remove noise, and the features are extracted manually. The test scenario is to create 10% synthetic data and 50% sleep apnea data to be added to the original dataset. Then compare the performance of multiple deep learning models before and after adding data. The results indicate that augmentation with DCGAN can improve the performance of almost all models, with the highest increase of 1.78% on the 1D-CNN+LSTM model and 4.80% on the LSTM model for the Apnea-ECG and MIT-BIH datasets, respectively.

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

“…Moreover, recent advancements in biometricbased human identification show great promise for accurate recognition based on ECG data [20,21]. In addition, ECG analysis can also be utilized for detecting emotions and stress [22], pain [23], sleep-apnea [24,25], identification of COVID-19 infections [26][27][28][29], assessment of signal quality [30,31], and many other potential applications. In this study, we considered all applications as long as they investigated on DA of ECG via AI techniques.…”

“…In the context of ECG DA, the authors of [15,17,18,24,94,100,105,[107][108][109][110][111][112][114][115][116][117][118][119]122,123,126] used GAN to augment the samples of the minor classes of the MIT-BIH AD. The augmented samples were then fed to a DL model for ECG beat classification, which demonstrated a notable improvement ranging from 0.24-32% compared to the unaugmented samples.…”