Topalidis, P.I.; Baron, S.; Heib, D.P.J.; Eigl, E.-S.; Hinterberger, A.; Schabus, M. From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability. Sensors2023, 23, 9077.
Topalidis, P.I.; Baron, S.; Heib, D.P.J.; Eigl, E.-S.; Hinterberger, A.; Schabus, M. From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability. Sensors 2023, 23, 9077.
Topalidis, P.I.; Baron, S.; Heib, D.P.J.; Eigl, E.-S.; Hinterberger, A.; Schabus, M. From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability. Sensors2023, 23, 9077.
Topalidis, P.I.; Baron, S.; Heib, D.P.J.; Eigl, E.-S.; Hinterberger, A.; Schabus, M. From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability. Sensors 2023, 23, 9077.
Abstract
More and more people quantify their sleep using wearables and are getting obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and even can lead to negative daytime consequences. Acknowledging these facts, we here aim to extend previous findings [] in a new sample of 136 self-reported poor sleepers by implementing optimization procedures to minimize erroneous classification when ambulatory sensing sleep. Firstly, here, 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 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. Results reveal high overall accuracy for the new model for ECG (86.3 %, κ=.79), H10 (84.4%, κ=.76), and VS (84.2%, κ=.75) with intended 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 for use also in high age groups and/or with common disorders. Further improving and validating sleep stage classification algorithms with affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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