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

Mustafa Radha,Xi Long,Marco Ross,Arnaud Moreau,Pedro Fonseca,Andreas Cerny,Peter Anderer,Ronald M Aarts

doi:10.1038/s41598-019-49703-y

Mustafa Radha, Xi Long + Show 6 more

Open Access

https://doi.org/10.1038/s41598-019-49703-y

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Abstract

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.

Highlights

Sleep is a reversible state of disconnection from the external environment characterized by reduced vigilance and quiescence
The test performance was obtained per fold by training the model on the remaining folds in a 4-fold cross-validation scheme
The presented model achieves state-of-the-art performance for heart rate variability (HRV)-based sleep stage classification, surpassing all previously published results presented in the introduction and summarized in Table 1, even though a significant part of the dataset used to evaluate this model included participants with disorders and old age, which are rarely included in prior work

Summary

Introduction

Sleep is a reversible state of disconnection from the external environment characterized by reduced vigilance and quiescence. It plays an essential role in the diurnal regulation of mind and body in mammals, and is hypothesized to have a wide array of functions ranging from digestion to memory consolidation. Together with sensors measuring cardiac and respiratory activity, this sensor montage is collectively referred to as polysomnography (PSG). It remains the gold standard for clinical assessment of sleep and diagnosis of sleep disorders, PSG is practically limited to one or two measuring nights, and cannot be effectively performed at home for a prolonged period of time. Most of the studies focused on sleep-wake www.nature.com/scientificreports/

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