Dataset Information

Wearable skin-like optoelectronic systems with suppression of motion artifacts for cuff-less continuous blood pressure monitor.

ABSTRACT: According to the statistics of the World Health Organization, an estimated 17.9 million people die from cardiovascular diseases each year, representing 31% of all global deaths. Continuous non-invasive arterial pressure (CNAP) is essential for the management of cardiovascular diseases. However, it is difficult to achieve long-term CNAP monitoring with the daily use of current devices due to irritation of the skin as well as the lack of motion artifacts suppression. Here, we report a high-performance skin-like optoelectronic system integrated with ultra-thin flexible circuits to monitor CNAP. We introduce a theoretical model via the virtual work principle for predicting the precise blood pressure and suppressing motion artifacts, and propose optical difference in the frequency domain for stable optical measurements in terms of skin-like devices. We compare the results with the blood pressure acquired by invasive (intra-arterial) blood pressure monitoring for >1500 min in total on 44 subjects in an intensive care unit. The maximum absolute errors of diastolic and systolic blood pressure were ±7/±10 mm Hg, respectively, in immobilized, and ±10/±14 mm Hg, respectively, in walking scenarios. These strategies provide advanced blood pressure monitoring techniques, which would directly address an unmet clinical need or daily use for a highly vulnerable population.

SUBMITTER: Li H

PROVIDER: S-EPMC8288864 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Wearable skin-like optoelectronic systems with suppression of motion artifacts for cuff-less continuous blood pressure monitor.

Li Haicheng H Ma Yinji Y Liang Ziwei Z Wang Zhouheng Z Cao Yu Y Xu Yuan Y Zhou Hua H Lu Bingwei B Chen Ying Y Han Zhiyuan Z Cai Shisheng S Feng Xue X

National science review 20200214 5

According to the statistics of the World Health Organization, an estimated 17.9 million people die from cardiovascular diseases each year, representing 31% of all global deaths. Continuous non-invasive arterial pressure (CNAP) is essential for the management of cardiovascular diseases. However, it is difficult to achieve long-term CNAP monitoring with the daily use of current devices due to irritation of the skin as well as the lack of motion artifacts suppression. Here, we report a high-perform ...[more]

PMID: 34692108

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Project description:Background: Currently-used tools for early recognition of clinical deterioration have high sensitivity, but with low specificity and are based on infrequent measurements. We aimed to develop a pre-symptomatic and real-time detection and warning tool for potential patients' deterioration based on multi-parameter real-time warning score (MPRT-WS). Methods: A total of more than 2 million measurements were collected, pooled, and analyzed from 521 participants, of which 361 were patients in general wards defined at high-risk for deterioration and 160 were healthy participants allocation as controls. The risk score stratification was based on cutoffs of multiple physiological parameters predefined by a panel of specialists, and included heart rate, blood oxygen saturation (SpO2), respiratory rate, cuffless systolic and diastolic blood pressure (SBP and DBP), body temperature, stroke volume (SV), cardiac output, and systemic vascular resistance (SVR), recorded every 5 min for a period of up to 72 h. The data was used to define the various risk levels of a real-time detection and warning tool, comparing it with the clinically-used National Early Warning Score (NEWS). Results: When comparing risk levels among patients using both tools, 92.6%, 6.1%, and 1.3% of the readings were defined as "Low", "Medium", and "High" risk with NEWS, and 92.9%, 6.4%, and 0.7%, respectively, with MPRT-WS (p = 0.863 between tools). Among the 39 patients that deteriorated, 30 patients received 'High' or 'Urgent' using the MPRT-WS (42.7 ± 49.1 h before they deteriorated), and only 6 received 'High' score using the NEWS. The main abnormal vitals for the MPRT-WS were SpO2, SBP, and SV for the "Urgent" risk level, DBP, SVR, and SBP for the "High" risk level, and DBP, SpO2, and SVR for the "Medium" risk level. Conclusion: As the new detection and warning tool is based on highly-frequent monitoring capabilities, it provides medical teams with timely alerts of pre-symptomatic and real-time deterioration.

Project description:Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels simultaneously: one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to identify and remove the artifacts. However, existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel-independent noise in the measured fluorescence. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the two channel fluorescence that incorporates motion artifact, neural activity, and noise. We use Bayesian inference to infer latent neural activity under this model, thus reducing the motion artifact present in the measured fluorescence traces. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore and an activity-independent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five models for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 20x more accurately on average than from control when using TMAC inferred activity and outperforms all other methods of motion correction tested, the best of which were ~8x more accurate than control.

Dataset Information

Wearable skin-like optoelectronic systems with suppression of motion artifacts for cuff-less continuous blood pressure monitor.

Publications

Wearable skin-like optoelectronic systems with suppression of motion artifacts for cuff-less continuous blood pressure monitor.

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