Forecasting of breathing events during nocturnal sleep using encoder-decoder recurrent neural network based on a sensors data of consumer smartwatches

Respiratory disorders during nocturnal sleep are the states of abnormal and difficult breathing, including snoring, hypopnea and different apnea types. Some of them have a negligible effect on health, while others can lead to a serious consequences. Therefore, the development of low-cost, portable, user-friendly devices and corresponding algorithms for diagnosis and forecasting of such events is of particular importance. In the current paper, an encoder-decoder recurrent neural network was developed for respiratory pattern forecasting. The system is based on a physiological sensors (accelerometer and photoplethysmography) data gathered from the consumer smartwatches during nocturnal sleep. The influence of the length of time series in the encoder part (available history for forecasting), and the length of time series at the output of decoder (forecasting length) is studied. The average achieved f1 score and Cohen's Kappa agreement of the proposed model varies in the range from 0.35 to 0.5 and from 0.25 to 0.4, respectively, depending on forecasting length. The efficiency of the forecasting largely depends on the model complexity, presence or absence of respiration events in the encoder part, and forecasting length.