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From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability
More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674316/ https://www.ncbi.nlm.nih.gov/pubmed/38005466 http://dx.doi.org/10.3390/s23229077 |
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author | Topalidis, Pavlos I. Baron, Sebastian Heib, Dominik P. J. Eigl, Esther-Sevil Hinterberger, Alexandra Schabus, Manuel |
author_facet | Topalidis, Pavlos I. Baron, Sebastian Heib, Dominik P. J. Eigl, Esther-Sevil Hinterberger, Alexandra Schabus, Manuel |
author_sort | Topalidis, Pavlos I. |
collection | PubMed |
description | More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, [Formula: see text] = 0.79), as well as the H10 (84.4%, [Formula: see text] = 0.76), and VS (84.2%, [Formula: see text] = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice. |
format | Online Article Text |
id | pubmed-10674316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106743162023-11-09 From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability Topalidis, Pavlos I. Baron, Sebastian Heib, Dominik P. J. Eigl, Esther-Sevil Hinterberger, Alexandra Schabus, Manuel Sensors (Basel) Article More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, [Formula: see text] = 0.79), as well as the H10 (84.4%, [Formula: see text] = 0.76), and VS (84.2%, [Formula: see text] = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice. MDPI 2023-11-09 /pmc/articles/PMC10674316/ /pubmed/38005466 http://dx.doi.org/10.3390/s23229077 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Topalidis, Pavlos I. Baron, Sebastian Heib, Dominik P. J. Eigl, Esther-Sevil Hinterberger, Alexandra Schabus, Manuel From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability |
title | From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability |
title_full | From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability |
title_fullStr | From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability |
title_full_unstemmed | From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability |
title_short | From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability |
title_sort | from pulses to sleep stages: towards optimized sleep classification using heart-rate variability |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674316/ https://www.ncbi.nlm.nih.gov/pubmed/38005466 http://dx.doi.org/10.3390/s23229077 |
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