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A wearable, flexible sensor for real-time, home monitoring of sleep apnea.

ABSTRACT: A flexible sensor that can be attached to the body to collect vital data wirelessly enables real-time human healthcare management. One potential application for home-use healthcare devices is monitoring of sleep conditions to diagnose sleep apnea syndrome. Such data are not readily gathered using conventional tools, owing to the bulk and cost of instrumentation. In order to monitor respiration at home, it is necessary to improve sensing performance and long-term stability of the sensors without sacrificing wearability and comfortability. To build a platform for wireless home-use respiration monitoring, this study develops a mask-borne flexible humidity sensor using ZnIn₂S₄ nanosheets as a humidity-sensitive material with high sensitivity and stability for more than 150 h. As proof-of-concept, long-term wireless respiration monitoring is demonstrated during sleep to identify symptoms of sleep apnea in wearers.

SUBMITTER: Honda S

PROVIDER: S-EPMC9010767 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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A wearable, flexible sensor for real-time, home monitoring of sleep apnea.

Honda Satoko S Hara Hyuga H Arie Takayuki T Akita Seiji S Takei Kuniharu K

iScience 20220325 4

A flexible sensor that can be attached to the body to collect vital data wirelessly enables real-time human healthcare management. One potential application for home-use healthcare devices is monitoring of sleep conditions to diagnose sleep apnea syndrome. Such data are not readily gathered using conventional tools, owing to the bulk and cost of instrumentation. In order to monitor respiration at home, it is necessary to improve sensing performance and long-term stability of the sensors without ...[more]

PMID: 35434564

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Project description:Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis. Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture. Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference. Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.

Project description:In the present global context, continuous blood pressure (BP) monitoring is paramount in addressing the global mortality rates attributed to hypertension. Achieving precise insights into the human cardiovascular system necessitates accurate measurement of BP, and the accuracy depends on the faithful recording of oscillations or pulsations. This task ultimately depends on the caliber of the pressure sensor embedded in the BP device. In this context, we have fabricated a flexible resistive pressure sensor based on reduced graphene oxide (rGO) and a polydimethylsiloxane (PDMS) sponge that is highly flexible and sensitive. The designed device operates effectively with a minimal bias voltage of 500 mV, at which point it showed its maximum relative change in current, reaching approximately 25%. Additionally, the sensing device showed a notable change in resistance values, exhibiting almost 100% change in resistance when subjected to a pressure of 400 mmHg and high sensitivity of 0.27 mmHg-1. After promising outcomes were obtained during static pressure measurement, the sensor was used for BP monitoring in humans. The sensor accurately traced the oscillometric waveform (OMW) for distinct systolic blood pressure (SBP) and diastolic blood pressure (DBP) combinations to cover a range of medical situations, including hypotension, standard or normal, and hypertension. The values of SBP, DBP, and MAP were derived from the sensor's output using the MAA technique, and the errors in these values concerning the simulator and the traditional BP monitor follow the universal AAMI/ESH/ISO protocols. Bland-Altman (B&A) correlation and scatter plots were used to compare the sensor's results and further validate the proposed sensor. The sensor showed the mean and standard deviation error in the SBP, DBP, and MBP of -0.2 ± 5.9, -0.5 ± 7, and -0.9 ± 4.7 mmHg when compared with the noninvasive blood pressure (NIBP) simulator. The pulse rate (PR) was also calculated from the same OMW for the specified value of 80 beats per minute (bpm) given by the simulator and reported a mean PR value of ∼81 bpm, close to the reference value. The findings show that the flexible resistive sensing device can accurately measure BP and replace the existing sensors of BP devices.

Dataset Information

A wearable, flexible sensor for real-time, home monitoring of sleep apnea.

Publications

A wearable, flexible sensor for real-time, home monitoring of sleep apnea.

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