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Sleep state classification is vital in managing and understanding sleep patterns and is generally the first step in identifying acute or chronic sleep disorders. However, it is essential to do this without affecting the natural environment or conditions of the subject during their sleep. Techniques such as Polysomnography(PSG) are obtrusive and are not convenient for regular sleep monitoring. Fortunately, The rise of novel technologies and advanced computing has given a recent resurgence to monitoring sleep techniques. One such contactless and unobtrusive monitoring technique is Ballistocradiography(BCG), in which vitals are monitored by measuring the body's reaction to the cardiac ejection of blood. In this study, we propose a Multi-Head 1D-Convolution based Deep Neural Network to classify sleep-wake state and predict sleep-wake time accurately using the signals coming from a BCG sensor. Our method achieves a sleep-wake classification score of 95.5%, which is on par with researches based on the PSG system. We further conducted two independent studies in a controlled and uncontrolled environment to test the sleep-wake prediction accuracy. We achieve a score of 94.16% in a controlled environment on 115 subjects and 94.90% in an uncontrolled environment on 350 subjects. The high accuracy and contactless nature of the proposed system make it a convenient method for long term monitoring of sleep states.

Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has privacy costs and resource constraints associated with it. At present, most models focus on generating cross-sectional health data which is not necessarily representative of real data. In reality, medical data is longitudinal in nature, with a single patient having multiple health events, non-uniformly distributed throughout their lifetime. These events are influenced by patient covariates such as comorbidities, age group, gender etc. as well as external temporal effects (e.g. flu season). While there exist seminal methods to model time series data, it becomes increasingly challenging to extend these methods to medical event time series data. Due to the complexity of the real data, in which each patient visit is an event, we transform the data by using summary statistics to characterize the events for a fixed set of time intervals, to facilitate analysis and interpretability. We then train a generative adversarial network to generate synthetic data. We demonstrate this approach by generating human sleep patterns, from a publicly available dataset. We empirically evaluate the generated data and show close univariate resemblance between synthetic and real data. However, we also demonstrate how stratification by covariates is required to gain a deeper understanding of synthetic data quality.