Objective: The term sleep quality is widely used by researchers and clinicians despite the lack of a definitional consensus, due to different assumptions on quality quantification. It is usually assessed using subject self-reporting, a method that has a major limitation since the subject is a poor self-observer of their sleep behaviors. A more precise method requires the estimation of physiological signals through polysomnography, a procedure that has high costs, is uncomfortable for the subjects and it is unavailable to a large group of the world population. To address these issues, a sleep quality prediction method was developed based on the analysis of the cyclic alternating pattern rate estimated using a single-lead electrocardiogram.
Approach: The algorithm analyzes the causality, entropy of the variability and connection of respiratory volume and the N-N interbeat intervals as features for a classifier to assess the cyclic alternating pattern and non-rapid eye movement periods. This information was then combined to estimate the cyclic alternating pattern rate and define the quality of sleep by considering the age-related cyclic alternating pattern rate percentages as a reference threshold.
Main results: The best results were achieved using a deep stacked autoencoder as a classifier and employing the minimal-redundancy-maximal-relevance as feature selection algorithm. Data collected from three databases and one hospital were used for training and testing the algorithms, achieving an average accuracy of, respectively, 76% and 77% for the cyclic alternating pattern and non-rapid eye movement sleep classification. The predicted sleep quality achieved a high agreement when considering either the cyclic alternating pattern rate, the arousal index, apnea-hypopnea index or the sleep efficiency as quantification for sleep quality. A moderate correlation was achieved with the Epworth sleepiness score and Pittsburgh sleep quality index. Total sleep time presented a higher variation on the correlation analysis.
Significance: The developed method is capable of estimating the sleep quality and is characterized by a low intra-individual variability. It only requires a small number of sensors that can easily be self-assembled, and could possibly lead to new developments in sleep quality estimation by home monitoring devices.