Abstract
Standard clinical care in neonatal and pediatric intensive-care units (NICUs and PICUs, respectively) involves continuous monitoring of vital signs with hard-wired devices that adhere to the skin and, in certain instances, can involve catheter-based pressure sensors inserted into the arteries. These systems entail risks of causing iatrogenic skin injuries, complicating clinical care and impeding skin-to-skin contact between parent and child. Here we present a wireless, non-invasive technology that not only offers measurement equivalency to existing clinical standards for heart rate, respiration rate, temperature and blood oxygenation, but also provides a range of important additional features, as supported by data from pilot clinical studies in both the NICU and PICU. These new modalities include tracking movements and body orientation, quantifying the physiological benefits of skin-to-skin care, capturing acoustic signatures of cardiac activity, recording vocal biomarkers associated with tonality and temporal characteristics of crying and monitoring a reliable surrogate for systolic blood pressure. These platforms have the potential to substantially enhance the quality of neonatal and pediatric critical care.
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Data availability
All relevant data are included in the paper. Additional supporting data are available from the corresponding authors on request. All request for raw and analyzed data and materials will be reviewed by the corresponding authors to verify whether the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper were generated as part of clinical trials and may be subject to patient confidentiality. Source data for Figs. 4–6 and Extended Data Figs. 2, 4, 5 and 7–10 are presented within the paper.
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Acknowledgements
D.E.W.-M., A.H., C.M.R., R.G. and J.A.R. acknowledge funding from the Bill & Melinda Gates Foundation (OPP1182909). S.X. and J.A.R. acknowledge additional funding from the Bill & Melinda Gates Foundation (OPP1193311). D.E.W.-M., A.H., C.M.R., R.G. and J.A.R. acknowledge support from the Gerber Foundation. A.S.P., D.E.W.-M., A.H. and J.A.R. recognize the Friends of Prentice Foundation for their support. J.A.R. and S.X. also recognize support from Save the Children (award 999002170). A.Y.R. gratefully acknowledges funding support by the National Institutes of Health’s National Center for Advancing Translational Sciences, grant TL1TR001423. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. E.S. recognizes funding support from the WCAS Undergraduate Research Grant Program, which is administered by Northwestern University’s Weinberg College of Arts and Sciences. The conclusions, opinions and other statements in this publication are the author’s and not necessarily those of the sponsoring institution. This work is also supported by the National Natural Science Foundation of China (11402134 and 11320101001), the National Basic Research program of China (2015CB351900) and the National Science Foundation (1400159, 1534120 and 1635443). The materials and engineering efforts were supported by the Center for Bio-Integrated Electronics of the Simpson Querrey Institute at Northwestern University. This work utilized Northwestern University Micro/Nano Fabrication Facility (NUFAB), which is partially supported by Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF ECCS-1542205), the Materials Research Science and Engineering Center (DMR-1720139), the State of Illinois and Northwestern University.
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A.S.P., S.X., D.E.W.-M., A.H., Y.H. and J.A.R. conducted the overall research program; H.U.C., A.Y.R., A.H.F., K.H.L., C.L., A.C., E.K., J.C., I.C.O., K.B.F., A.B., Jongwon Kim, H.J., M.N., J.W.K., E.S., M.A.P., R.J.K., B.V.P., K.A.H., M.P., H.S.K., S.H.L., J.D.L., Y.Y., S.R., T.S., I.J. and H.A. performed and were involved in the manufacturing of the sensors; H.U.C., A.Y.R., A.H.F., K.H.L., Z.X., C.L., D.R., E.K., J.C., W.R. and J.Y.L. were responsible for the overall engineering design of the sensors; H.U.C., A.Y.R., A.H.F., D.H.K., D.R., C.O., D.G., M.I., A.B. and J.Y.L. were responsible for data analysis; E.C.D., B.H., V.R.S., A.O., A.S., A.B., M.S., C.M.R., L.E.M., Z.L.H., A.H., A.S.P., D.E.W.-M. and S.X. were responsible for data collection; Z.X., K.H.L., R.A., Y.X. and Y.H. performed the mechanical and structural design; H.U.C., D.H.K., D.R., C.O., D.G., J.B.P., J.L., Y.P., Jungwoo Kim, H.H.J., H.H., S.S.K., M.I. and J.Y.L. performed software design, software validation, signal processing and algorithm development; A.S.P., D.E.W.-M., C.M.R., S.X. and J.A.R. were responsible for acquiring funding; H.U.C., A.R., A.H.-F., S.X., J.Y.L. and J.A.R. wrote the original draft of the manuscript; and all authors participated in editing and reviewing of the final manuscript.
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H.U.C., S.X., D.H.K., D.R., E.K., J.C., A.B., J.B.P., J.L., J.K., H.H.J., S.S.K., J.D.L., J.Y.L. and J.A.R. declare equity ownership in a company that is pursuing commercialization of the technology described here.
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Extended data
Extended Data Fig. 1 Fabrication Process of a Chest unit.
a, Leftmost and middle diagrams show the assembly process of the circuit components on a 2-layer flexible printed circuit board. Rightmost diagram shows how the sub-systems of the chest board are folded. b, The encapsulation process involves spin casting Silbione elastomer on top of a glass slide, followed by opening for the electrodes. CB PDMS is used to fill the electrode openings, then combined with the folded board that contains a battery. The top Silbione enclosure is prepared through a 2-part molding process and closes the encapsulation of the chest unit. Removing the glass slide finalizes this process. CB PDMS, carbon black in polydimethylsiloxane.
Extended Data Fig. 2 Information on connectivity, thermal safety, and modular batteries.
a, Measurement of connectivity between sensors and base station (connected with a BLE dongle) involves a received signal strength indicator (RSSI). RSSI was measured at a distance between sensors and the base station from 0 to 14 meters (incremental of 2 meters) three times at three different cases when 1) the chest unit in air, 2) the chest unit was on a healthy adult’s chest, and 3) the limb unit on the finger of the same person. Standard deviation between three measures at every distance is defined as the error bar in the plot. BLE, Bluetooth Low Energy. b, Representative pictures showing temperature changes on a subject’s chest when a chest unit is mounted, operational for 24 hours. Negligible change was observed (n = 3). c, Similarly, negligible change in temperature was observed, when a limb unit was mounted on the foot of the same subject for 24 hours in full operation (n = 3). d, Different form factors of encapsulated modular batteries. The green colored circle with the diameter of choking limit outlines the safety limits to prevent choking hazard for kids under 3 years old.
Extended Data Fig. 3 Mechanical properties of chest and limb units.
a, Schematic illustration of stack and dimension information for serpentine interconnects and sub-systems of a chest unit. PI, polyimide. Cu, copper. b, Computational result showing the stretchability and bendability of a chest unit’s board. L0, nominal length of a serpentine interconnect of the board. L*, pre-compressed length of the serpentine. ε = (L-L*)/ L* defines the elastic stretchability. c, Computational result showing the effect of thickness of the strain isolation layer regarding shear and normal stress when a chest board is assumed to be stretched by 20%. d, Schematic illustration of the stack and dimension information for serpentine interconnects of a limb unit. e, Strain distribution in the encapsulation layer (left) and copper layer (right) of an interconnect during (e) twisting, (f) stretching, (g) and bending at the radius of 3.9 mm. h, Overall bending mechanics of a limb unit board illustrated by strain distribution.
Extended Data Fig. 4 Vital signs processing algorithm.
Block diagram of the signal processing algorithm for (a) heart rate, (b) respiration rate, (c) SpO2. d,e, representative images showing differences between sub-processed time-domain and frequency-domain responses for the signal (d) without motion artifact and (e) with motion artifact. f, Block diagram of signal processing algorithm for pulse timing calculations. BPF, bandpass filter. ECG, electrocardiogram. QRS, QRS complex of ECG. R-R distance, distance between two consecutive R-peaks of ECG. ACCL, acceleration output. PPG, photoplethysmogram output. IR, infra-red.
Extended Data Fig. 5 Vital signs comparison of sensors to FDA-cleared equipment.
a, Closed view around 100 seconds of HR, SpO2, and Temperature plots in Fig. 4b. Global Bland Altman plots for b, HR (n = 515,679 from 20 NICU and PICU subjects) and c, SpO2 (n = 440,077 from 20 NICU and PICU subjects).
Extended Data Fig. 6 Time-synchronization verification experiment for the sensors.
a, Testing set up involves a signal generator sending rectangular pulses to a chest unit, while received data is streamed over to the base station. The signal generator sends a delayed signal to switch the Red LED and the photodetector in the limb unit to capture the output associated with LED switching, followed by streaming the output to the same base station. b, A representative plot showing two signals obtained from the chest and limb unit with a delay between two units defined by the signal generator. c, Mean and standard deviation of calculated timing difference compared to defined delay (testing PAT).
Extended Data Fig. 7 Analysis of estimating blood pressure using pulse timing parameters.
a, A representative plot showing large movement in accelerometry data (bottom) correlates to large and sudden changes in arterial line (A-line) derived SBP signal (top). Bland Altman plots of PAT- and PTT-derived SBP compared to A-line SBP from the data shown in b, Fig. 5d (n = 18,679 data points for both left and right), c, Fig. 5e (n = 17,449 data points for both). d, A plot (left) of A-line SBP (red) and PAT SBP (blue) and the resulting Bland Altman plot (right, n = 7,322 data points) for the subject with H/O splenectomy (39 w GA, 95 w CA). e, A plot (left) of A-line SBP (red) and PTT SBP (blue) and the resulting Bland Altman plot (right) for the same subject (n = 7,322 data points).
Extended Data Fig. 8 Analysis of estimating blood pressure using pulse timing parameters.
a, A plot (left) of A-line SBP (red) and PAT SBP (blue) and the resulting Bland Altman plot (right) for the same subject in ED Fig. 7 (n = 7,024 data points). b, A plot (left) of A-line SBP (red) and PTT SBP (blue) and the resulting Bland Altman plot (right) for the same subject in (a, n = 7,024 data points). c, A plot (left) of A-line SBP (red) and PAT SBP (blue) and the resulting Bland Altman plot (right, n = 17,514 data points) for the subject with hypoxia and hypercapnia (40 w GA, 69 w CA). d, A plot (left) of A-line SBP (red) and PTT SBP (blue) and the resulting Bland Altman plot (right) for the same subject (n = 17,514 data points). e, Accelerometry analysis to verify correlation between large spikes in A-line SBP (top) and motion artifact (bottom).
Extended Data Fig. 9 The effect of calibration window size and re-calibration period in estimating blood pressure using pulse timing parameters.
a, A plot (left) of A-line SBP vs PTT SBP obtained from the same subject in Fig. 5d and its resulting Bland Altman plot (right, n = 18,979 data points) with a single calibration was applied in the first minute of data. b, A plot (left) of A-line SBP vs PTT SBP of the same subject in (a) and its resulting Bland Altman plot (right, n = 18,739 data points) while having calibration every 30 minutes with a calibration window size of 5 minutes.
Extended Data Fig. 10 Accelerometry for analyzing characteristics of crying by a chest unit on neonates.
Representative power spectrum of signal frequency upon fast Fourier transform processing of neonatal mechano-acoustic signal during a, patting, b, resting, and c, crying. d, Bland Altman plot for comparison between cry duration calculated by a chest unit’s data and manual recording (n = 11 data points from 3 neonates).
Supplementary information
Supplementary Information
Supplementary Figs. 1–3 and Supplementary Table 1.
Supplementary Video 1
The video shows the demo of sensors operating along with the real-time streaming data to a base station, with sensors mounted on a healthy adult
Supplementary Video 2
The video shows waterproof operation of the sensors
Supplementary Video 3
The video shows real-time streaming data to a base station, when sensors were mounted on an actual pediatric patient (patient is fully de-identified)
Supplementary Video 4
The video shows the cry pattern created from the acceleration data obtained from a chest unit on a subject
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Chung, H.U., Rwei, A.Y., Hourlier-Fargette, A. et al. Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units. Nat Med 26, 418–429 (2020). https://doi.org/10.1038/s41591-020-0792-9
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DOI: https://doi.org/10.1038/s41591-020-0792-9
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