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, 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 %, κ = 0.79), as well as the H10 (84.4%, κ = 0.76), and VS (84.2%, κ = 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

Real world data from a multi-centre study on the effects of cilostazol on pain symptoms and walking distance in patients with peripheral arterial disease

Objective: to assess the effects of cilostazol on pain-free walking distance in PAD patients with IC at 3 and 6 months in a real world, prospective, observational study. We included 1015 PAD patients presenting with IC (71.3% men, 93.5% white, mean age 69.2 ± 8.7 years). Patients were followed up for 6 months by their physicians. Results: Cilostazol significantly increased pain-free walking distance by a median of 285 and 387 m at 3 and 6 months, respectively (p < 0.01 for all comparisons). This effect was significant for patients 50–74 years (but not for those aged ≥ 75 years) and independent of smoking status, changes in physical activity, comorbidities and concomitant medication for PAD (i.e., acetylsalicylic acid and clopidogrel). Furthermore, significant reductions were observed in systolic (from 139 ± 16 to 133 ± 14 mmHg; p < 0.001) and diastolic blood pressure (from 84 ± 9 mmHg to 80 ± 10 mmHg; p < 0.001). Smoking cessation and increased physical activity were reported by the majority of participants. In conclusion, cilostazol was shown to safely decrease pain symptoms and improve pain-free walking in PAD patients with IC in a real world setting. Benefits also occurred in terms of BP and lifestyle changes. © 2022, The Author(s)