Sleep stage classification from heart-rate variability using long short-term memory neural networks

• Published in: Scientific reports Vol. 9; no. 1; pp. 14149 - 11
• Main Authors: Radha, Mustafa, Fonseca, Pedro, Moreau, Arnaud, Ross, Marco, Cerny, Andreas, Anderer, Peter, Long, Xi, Aarts, Ronald M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02-10-2019; Nature Publishing Group
• Subjects: 639/166/985; 639/705/117; 692/699/375/1816; 9/10; Adult; Aged; Classification; Female; Heart Rate; Humanities and Social Sciences; Humans; Long short-term memory; Male; Middle Aged; Models, Neurological; multidisciplinary; Neural networks; Neural Networks, Computer; Science; Science (multidisciplinary); Short term; Sleep; Sleep Stages - physiology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-49703-y

Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.