Optical wrist-worn device for sleep monitoring

Sleep Pattern Analysis in Unconstrained and Unconscious State

Sleep accounts for one-third of an individual's life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals, such as frequency of tosses and turns, snoring, and body temperature, as well as environmental data, such as room temperature, humidity, illuminance, carbon dioxide concentration, and ambient noise. The sleep state was classified into two stages: nonrapid eye movement and rapid eye movement sleep, rather than the general four stages. Furthermore, to verify the validity of the sleep state classifications, we compared them with heart rate.

Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression

In this work, we present a photoplethysmography-based blood pressure monitoring algorithm (PPG-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP). The proposed algorithm extracts morphological features from the PPG signal and maps them to SBP and DBP values using a multiple linear regression (MLR) model. The performance of the algorithm is evaluated on the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC I) database. We utilize 28 data-sets (records) from the MIMIC I database that contain both PPG and brachial arterial blood pressure (ABP) signals. The collected PPG and ABP signals are synchronized and divided into intervals of 30 seconds, called epochs. In total, we utilize 47153 \textit{clean} 30-second epochs for the performance analysis. Out of the 28 data-sets, we use only 2 data-sets (records 041 and 427 in the MIMIC I) with a total of 2677 \textit{clean} 30-second epochs to build the MLR model of the algorithm. For the SBP, a standard deviation of error (SDE) of 8.01 mmHg and a mean absolute error (MAE) of 6.10 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.90, . For the DBP, an SDE of 6.22 mmHg and an MAE of 4.65 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.85, . We also use a binary classifier for the BP values with the positives indicating SBP ≥ 130 mmHg and/or DBP ≥ 80 mmHg and the negatives indicating otherwise. The classifier results generated by the PPG-based SBP and DBP estimates achieve a sensitivity and a specificity of 79.11% and 92.37%, respectively.

Photoplethysmography Based Blood Pressure Monitoring Using the Senbiosys Ring

In this work, we evaluate the accuracy of our cuffless photoplethysmography based blood pressure monitoring (PPG-BPM) algorithm. The algorithm is evaluated on an ultra low power photoplethysmography (PPG) signal acquired from the Senbiosys Ring. The study involves six male subjects wearing the ring for continuous finger PPG recordings and non-invasive brachial cuff inflated every two to ten minutes for intermittent blood pressure (BP) measurements. Each subject performs the required recordings two to three times with at least two weeks difference between any two recordings. In total, the study includes 17 recordings 2.21 ± 0.89 hours each. The PPG recordings are processed by the PPG-BPM algorithm to generate systolic BP (SBP) and diastolic BP (DBP) estimates. For the SBP, the mean difference between the cuff-based and the PPG-BPM values is -0.28 ± 7.54 mmHg. For the DBP, the mean difference between the cuff-based and the PPG-BPM values is -1.30 ± 7.18 mmHg. The results show that the accuracy of our algorithm is within the 5 ± 8 mmHg ISO/ANSI/AAMI protocol requirement.

PPG-Based Respiratory Rate Monitoring Using Hybrid Vote-Aggregate Fusion Technique

In this work, we present a low-complexity photoplethysmography-based respiratory rate monitoring (PPG-RRM) algorithm that achieves high accuracy through a novel fusion method. The proposed technique extracts three respiratory-induced variation signals, namely the maximum slope, the amplitude, and the frequency, from the PPG signal. The variation signals undergo time domain peak detection to identify the inter-breath intervals and produce three different instantaneous respiratory rate (IRR) estimates. The IRR estimates are combined through a hybrid vote-aggregate fusion scheme to generate the final RR estimate. We utilize the publicly available Capnobase data-sets [1] that contain both PPG and capnography signals to evaluate our RR monitoring algorithm. Compared to the reference capnography IRR, the proposed PPG-RRM algorithm achieves a mean absolute error (MAE) of 1.44 breaths per minute (bpm), a mean error (ME) of 0.70±2.54 bpm, a root mean square error (RMSE) of 2.63 bpm, and a Pearson correlation coefficient r = 0.95, p < .001.

Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG

OBJECTIVE

To validate the accuracy of the Oura ring in the quantification of resting heart rate (HR) and heart rate variability (HRV).

BACKGROUND

Wearable devices have become comfortable, lightweight, and technologically advanced for assessing health behavior. As an example, the novel Oura ring integrates daily physical activity and nocturnal cardiovascular measurements. Ring users can follow their autonomic nervous system responses to their daily behavior based on nightly changes in HR and HRV, and adjust their behavior accordingly after self-reflection. As wearable photoplethysmogram (PPG) can be disrupted by several confounding influences, it is crucial to demonstrate the accuracy of ring measurements.

APPROACH

Nocturnal HR and HRV were assessed in 49 adults with simultaneous measurements from the Oura ring and the gold standard ECG measurement. Female and male participants with a wide age range (15-72 years) and physical activity status were included. Regression analysis between ECG and the ring outcomes was performed.

MAIN RESULTS

Very high agreement between the ring and ECG was observed for nightly average HR and HRV (r² = 0.996 and 0.980, respectively) with a mean bias of -0.63 bpm and -1.2 ms. High agreement was also observed across 5 min segments within individual nights in (r² = 0.869 ± 0.098 and 0.765 ± 0.178 in HR and HRV, respectively).

SIGNIFICANCE

Present findings indicate high validity of the Oura ring in the assessment of nocturnal HR and HRV in healthy adults. The results show the utility of this miniaturised device as a lifestyle management tool in long-term settings. High quality PPG signal results prompt future studies utilizing ring PPG towards clinically relevant health outcomes.

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

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.

Sleep staging using nocturnal sound analysis

Sleep staging is essential for evaluating sleep and its disorders. Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results. Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed. Here, we present a pioneering approach for rapid eye movement (REM), non-REM, and wake staging (macro-sleep stages, MSS) estimation based on sleep sounds analysis. Our working hypothesis is that the properties of sleep sounds, such as breathing and movement, within each MSS are different. We recorded audio signals, using non-contact microphones, of 250 patients referred to a polysomnography (PSG) study in a sleep laboratory. We trained an ensemble of one-layer, feedforward neural network classifiers fed by time-series of sleep sounds to produce real-time and offline analyses. The audio-based system was validated and produced an epoch-by-epoch (standard 30-sec segments) agreement with PSG of 87% with Cohen's kappa of 0.7. This study shows the potential of audio signal analysis as a simple, convenient, and reliable MSS estimation without contact sensors.