Aims  To quantify the arousal burden (AB) across large cohort studies and determine its association with long-term cardiovascular (CV) and overall mortality in men and women. Methods and results  We measured the AB on overnight polysomnograms of 2782 men in the Osteoporotic Fractures in Men Study (MrOS) Sleep study, 424 women in the Study of Osteoporotic Fractures (SOF) and 2221 men and 2574 women in the Sleep Heart Health Study (SHHS). During 11.2 ± 2.1 years of follow-up in MrOS, 665 men died, including 236 CV deaths. During 6.4 ± 1.6 years of follow-up in SOF, 105 women died, including 47 CV deaths. During 10.7 ± 3.1 years of follow-up in SHHS, 987 participants died, including 344 CV deaths. In women, multivariable Cox proportional hazard analysis adjusted for common confounders demonstrated that AB is associated with all-cause mortality [SOF: hazard ratio (HR) 1.58 (1.01–2.42), P = 0.038; SHHS-women: HR 1.21 (1.06–1.42), P = 0.012] and CV mortality [SOF: HR 2.17 (1.04–4.50), P = 0.037; SHHS-women: HR 1.60 (1.12–2.28), P = 0.009]. In men, the association between AB and all-cause mortality [MrOS: HR 1.11 (0.94–1.32), P = 0.261; SHHS-men: HR 1.31 (1.06–1.62), P = 0.011] and CV mortality [MrOS: HR 1.35 (1.02–1.79), P = 0.034; SHHS-men: HR 1.24 (0.86–1.79), P = 0.271] was less clear. Conclusions Nocturnal AB is associated with long-term CV and all-cause mortality in women and to a lesser extent in men.

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Overnight continuous blood pressure measurement provides simultaneous monitoring of blood pressure and sleep architecture. By this means, we are able to investigate whether different sleep events are associated to blood pressure fluctuations. In this paper, we used the Pulse Transit Time (PTT) to develop and evaluate functions for measurement of blood pressure. We focused on the first and second derivatives of fingertip Photoplethysmography (PPG) recordings to detect PPG critical points. By applying R wave of ECG and PPG critical points, we created two PTT-based models for estimation of systolic and diastolic blood pressure (SBP and DBP). Seven subjects polysomnography datasets that contained PPG, ECG and blood pressure recordings were utilised to validate and compare developed PTT-BP functions. Results found that if the peak of the first derivative of PPG (VPG) was considered as the pulse pressure arrival point, the resulted PTT (PTTV) would more accurately predict both SBP and DBP. The average R-squared coefficient for SBP and DBP were correspondingly 0.593 and 0.416. The obtained mean error for PTTV based functions in SBP was ±3.96 mmHg with standard deviation of 1.41 mmHg and in DBP was ±6.88 mmHg with standard deviation of 3.03 mmHg. We concluded PTT detected from VPG is a reliable and suitable maker for overnight continuous blood pressure monitoring.

This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs. The validation of the algorithm was evaluated by Leave-One-Out-Cross-Validation method. The average accuracy of 73.4% with standard deviation of 6.4 was achieved while the algorithm could distinguish stages 2, 3 of non-rapid eye movement sleep by average sensitivity of almost 80%. The lowest mean sensitivity of 53% was for stage 1. These results demonstrate that an algorithm based on PTT and HRV spectral analysis is able to classify and distinguish sleep stages with high accuracy and sensitivity. In addition the proposed algorithm is capable to be improved and implemented as a wearable, comfortable and cheap instrument for sleep screening.