Abstract
Missing values are very prevalent in real world; they are caused by various reasons such as user mistakes or device failures. They often cause critical problems especially in medical and healthcare application since they can lead to incorrect diagnosis or even cause system failure. Many of recent imputation techniques have adopted machine learning-based generative methods such as generative adversarial networks (GANs) to deal with missing values in medical data. They are, however, incapable of reproducing realistic time-series signals preserving important latent features such as sleep stages that are important context in many medical applications using electroencephalogram (EEG). In this study, we propose a novel GAN-based technique generating realistic EEG signal sequences which are not only shown natural but also correctly classified with sleep stages by implanting the latent features in the synthetic sequence. By experiments, we confirm that our model generates not only more realistic EEG signals than a recent GAN-based model but also preserve auxiliary information such as sleep stages. Furthermore, we demonstrate that existing machine learning methods based on EEG data still work well without sacrificing performance using the imputed data by using our method.
Highlights
In most of time series data analysis, missing values coming up by various reasons such as user mistakes or device failures lead to performance degradation or even cause system failure
In our experiments, we assume automatic sleep stage scoring for such application and utilize two deep learning-based classifiers, which are DeepSleepNet [49] and SleepEEGNet [55]. These classifiers and the generative adversarial networks (GANs)-based EEG signal generators, SIGGAN and EEGGAN, are trained separately and their synthetic signals are input to the classifiers to test if they still work well
To see if we can obtain the signals using SIGGAN showing the characteristics of sleep stages appropriately, we show real and synthetic signals labelled as W and N2
Summary
In most of time series data analysis, missing values coming up by various reasons such as user mistakes or device failures lead to performance degradation or even cause system failure. Recent imputation techniques have adopted traditional statistical imputation and machine learning based generative method to deal with missing values. These methods, are incapable of generating realistic timeseries signals involving important latent information which is necessary for being exploited in the target application such as sleep disorder diagnosis based on electroencephalogram (EEG). Most of applications utilizing such medical datasets suffer from missing values so that they may make wrong alerts or incorrect diagnoses [6], [7]. Existing imputing methods cannot handle such cases effectively even if they can reconstruct for a single or short-term missing values by interpolation based on adjacent non-missing values
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