Predicting Daytime Sleepiness from Electrocardiography Based Respiratory Rate Using Deep Learning

Abstract

Daytime sleepiness impairs the activities of daily living, especially in chronic disease patients. Typically, daytime sleepiness is measured with subjective patient reported outcomes (PROs), which could be prone to recall bias. Objective measures of daytime sleepiness, which are sensitive to change, would benefit disease state assessment and novel therapies that impact the quality of life. The presented study aimed to predict daytime sleepiness from two hours of continuously measured respiratory rate using a 1-dimensional convolutional neural network. A wearable biosensor was used to continuously measure electrocardiography (ECG) based respiratory rate, while the participants (N=82) were asked to fill in Karolinska Sleepiness Scale three times a day. Considering the need for a sleepiness measure for chronic diseases, neurodegenerative disease (NDD, N=14) patients, immune-mediated inflammatory disease (IMID, N=42) patients, as well as healthy participants (N=26) were included in the study. The diseaseagnostic model achieved an accuracy of 63% between nonsleepy and sleepy states. The result demonstrates the potential of using respiratory rate with deep learning for an objective measure of daytime sleepiness.