At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch

Zavanelli, Nathan; Kim, Hojoong; Kim, Jong-Su; Herbert, Robert; Mahmood, Musa; Kim, Yun-Soung; Kwon, Shinjae; Bolus, Nicholas B; Torstrick, F. Brennan; Lee, Christopher S.D.; Yeo, Woon‐Hong

doi:10.1126/sciadv.abl4146

Cited by 37 publications

(26 citation statements)

References 36 publications

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“…13a and b). 285 This work provided health caregivers with all clinically relevant metrics outlined by The American Academy of Sleep Medicine (AASM) via a non-invasive approach. The skin patch consisted of a soft PPG sensor, an ECG sensor, advanced signal processing techniques, and time-series machine learning (Fig.…”

Section: Towards Long-term Continuous Health Monitoringmentioning

confidence: 99%

Skin bioelectronics towards long-term, continuous health monitoring

et al. 2022

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Section: Towards Long-term Continuous Health Monitoringmentioning

confidence: 99%

Skin bioelectronics towards long-term, continuous health monitoring

et al. 2022

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“…As illustrated in Figure 8A, the OSAS is characterized by intermittent cessations of breathing during sleep due to pharyngeal narrowing. [5,49] And the sleep apnea was defined as a complete airflow cessation for at least 10 s. [50] As a proof-ofconcept demonstration, four CH-GT100 hydrogel sensors were simultaneously attached on the corresponding body parts to independently monitor the nostril airflow, movement of chest/ abdomen, and pulse (Figure 8B), to evaluate the potential of this kind of hydrogel sensor for diagnosing OSAS. A volunteer of 26-year-old male adult deliberately hold his breath for 37 s to simulate the cessation of breathing and repeated this behavior four times.…”

Section: Respiration Monitoring Of Ch-gt Hydrogel Sensormentioning

confidence: 99%

“…[ 1 ] Respiration, as the indispensable behavior for human beings, not only plays an essential role of sustaining life but also could be as a crucial feature to assess an individual health. [ 2,3 ] Severe breathing problems could induce a series of diseases, such as obstructive sleep apnea syndrome (OSAS), [ 4,5 ] Alzheimer, [ 6 ] and cardiovascular disorders (CVD). [ 7 ] Hence, effective monitoring of respiratory events is crucial for health surveillance and forecasting human diseases, and even saving human lives.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Hydrogel‐Based Respiratory Monitoring System for Diagnosing Obstructive Sleep Apnea Syndrome

Liu

Wang

Liu

et al. 2022

Adv Funct Materials

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Real‐time monitoring of respiration is vital for human health, especially for forecasting the sleep‐related diseases. A respiratory monitoring system with high accuracy, wearing comfort, portability, and environmental tolerance, is highly desirable, which however remains a big challenge. Here, a multimodal hydrogel sensor with excellent comprehensive performance is fabricated to monitor respiration and diagnose obstructive sleep apnea syndrome (OSAS). The synthetic cellulose‐based hydrogel exhibits good mechanical properties and extreme temperature tolerance, ascribing to the synergistic effects between chemical cross‐linking and multiple hydrogen bonding within the hydrogel network. The fabricated hydrogel sensor can independently monitor the mechanical variation and the thermal change via output signals of capacitance and resistance, respectively. These extraordinary properties of the hydrogel sensor enable the highly reliable and accurate monitoring of the respiratory events and diagnosis of OSAS. This work provides the new and practical way for real‐time respiratory monitoring and preventing the occurrence of sleep‐related diseases.

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“…A customized printed circuit board (PCB) with an nRF52 (Nordic Semiconductor, Trondheim, Norway) and an ADS 1299 (Texas Instruments, Dallas, TX, USA) was used to collect EEG and EOG signals at a sampling rate of 250 Hz and to transmit them to an Android mobile device via Bluetooth for data storage. The systems used to collect the lab datasets, the nanomembrane-based electrodes and the custom PCB with the nRF52 and ADS 1299 have been extensively studied and validated by comparison with wellestablished measurement systems by numerous related previous studies [17][18][19][20][21] Figure 1E shows representative multi-taper spectrograms of data measured by the standard PSG setup and the setup used for this study at each of the five sleep stages.…”

Section: Measured Lab Datasetmentioning

confidence: 99%

Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes

Kwon

Yeo

2022

Biosensors

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Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes.

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At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch

Abstract: A systematic study of soft materials and flexible mechanics yields a soft patch capable of detecting sleep stages and sleep apnea.

Cited by 37 publications

References 36 publications

Skin bioelectronics towards long-term, continuous health monitoring

Skin bioelectronics towards long-term, continuous health monitoring

Multimodal Hydrogel‐Based Respiratory Monitoring System for Diagnosing Obstructive Sleep Apnea Syndrome

Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes

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