Estimation of respiration rate and sleeping position using a wearable accelerometer

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Comparison of sleep position monitoring between NaTu sensor and video-validated polysomnography in patients with obstructive sleep apnea

PURPOSE

This study aimed to evaluate the accuracy of a Bluetooth position monitor called NaTu sensor and its mobile phone application for detecting sleep position among patients with obstructive sleep apnea (OSA) during polysomnography (PSG).

METHODS

A cross-sectional study was conducted on adults with suspected of having OSA who underwent PSG. Sleep positions were recorded simultaneously using a video-validated PSG position sensor and the NaTu sensor. The area under the Receiver Operator Characteristic curve (ROC AUC), sensitivity, and specificity values were calculated to evaluate the validity of the NaTu sensor.

RESULTS

Ninety participants (56.7% male) were included, with median age of 40.0 years and body mass index of 29.4 kg/m2. The mean AHI was 58.4 ± 31.2 events/hour, categorizing the severity of OSA as mild (5.6%), moderate (18.9%), and severe (75.5%). Sleep positions (supine, lateral right, lateral left) identified by the NaTu sensor closely agreed with the video-validated PSG. The kappa statistic demonstrated almost perfect agreement (k = 0.95, P < 0.001) for overall position recording. The ROC AUC for identifying supine, lateral right, and lateral left positions ranged from 0.974 to 0.981, with sensitivity ranging from 95.1% to 99.1% and specificity from 96.5% to 99.6%.

CONCLUSION

Our wearable sensor monitoring significantly agrees with video-validated PSG in identifying sleep positions. This device is reliable and accurate for position monitoring and could be an alternative tool for monitoring positions in in-lab PSG, home sleep apnea testing, or tracking positional treatment at home.

REGISTRATION

Thaiclinicaltrials.org with number TCTR20210701008.

Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring

Wrist-based respiratory rate (RR) measurement during sleep faces accuracy limitations. This study aimed to assess the accuracy of the RR estimation function during sleep based on the severity of obstructive sleep apnea (OSA) using the Samsung Galaxy Watch (GW) series. These watches are equipped with accelerometers and photoplethysmography sensors for RR estimation. A total of 195 participants visiting our sleep clinic underwent overnight polysomnography while wearing the GW, and the RR estimated by the GW was compared with the reference RR obtained from the nasal thermocouple. For all participants, the root mean squared error (RMSE) of the average overnight RR and continuous RR measurements were 1.13 bpm and 1.62 bpm, respectively, showing a small bias of 0.39 bpm and 0.37 bpm, respectively. The Bland-Altman plots indicated good agreement in the RR measurements for the normal, mild, and moderate OSA groups. In participants with normal-to-moderate OSA, both average overnight RR and continuous RR measurements achieved accuracy rates exceeding 90%. However, for patients with severe OSA, these accuracy rates decreased to 79.45% and 75.8%, respectively. The study demonstrates the GW's ability to accurately estimate RR during sleep, even though accuracy may be compromised in patients with severe OSA.

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.

A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization

Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR-respiration rate; TI/TE-inhalation/exhalation time; IER-inhalation-to-exhalation time; V-flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland-Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = -0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/-1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.

Sleep Posture Detection Using an Accelerometer Placed on the Neck

Sleep position monitoring is key when attempting to address posture triggered sleep disorders. Many studies have explored sleep posture detection from a dedicated physical sensing channel exploiting optimum body locations, such as the torso; or alternatively non-contact approaches. But, little work has been done to try to detect sleep position from a body location which, whilst being suboptimal for that purpose, does however allow for better extraction of more critical biomarkers from other sensing modalities, making possible multi-modal monitoring in certain clinical applications. This work presents two different approaches, at varying levels of complexity, for detecting 4 main sleep positions (supine, prone, lateral right and lateral left) from accelerometry data obtained by a single wearable device placed on the neck. An ultra light-weight threshold-based model is presented in this work, in addition to an Extra-Trees classifier. The threshold-based model was able to achieve 95% average accuracy and 0.89 F1-score on out-of-sample data, showing that it is possible to obtain a moderately high classification performance using a simple rule-based model. The ExtraTrees classifier, on the other hand, was able to achieve 99 % average accuracy and 0.99 average F1-score using only 25 base estimators with maximum depth of 20. Both models show promise in detecting sleep posture with high accuracy when collecting the signals from a neck-worn accelerometer sensor.

An openly available wearable, a diaper cover, monitors infant's respiration and position during rest and sleep

AIM

To describe and test the accuracy of respiratory rate assessment in long-term surveillance using an open-source infant wearable, NAPping PAnts (NAPPA).

METHODS

We recorded 24 infants aged 1-9 months using our newly developed infant wearable that is a diaper cover with an integrated programmable electronics with accelerometer and gyroscope sensors. The sensor collects child's respiration rate (RR), activity and body posture in 30-s epochs, to be downloaded afterwards into a mobile phone application. An automated RR quality measure was also implemented using autocorrelation function, and the accuracy of RR estimate was compared with a reference obtained from the simultaneously recorded capnography signal that was part of polysomnography recordings.

RESULTS

Altogether 88 h 27 min of data were recorded, and 4147 epochs (39% of all data) were accepted after quality detection. The median of patient wise mean absolute errors in RR estimates was 1.5 breaths per minute (interquartile range 1.1-2.6 bpm), and the Blandt-Altman analysis indicated an RR bias of 0.0 bpm with the 95% limits of agreement of -5.7-5.7 bpm.

CONCLUSION

Long-term monitoring of RR and posture can be done with reasonable accuracy in out-of-hospital settings using NAPPA, an openly available infant wearable.