Abstract
The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.
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Data availability
The data used in the study are not publicly available because they contain information that could compromise research participant privacy. Anonymized data can be made available on request for academic purposes. Sample data of cardiorespiratory signals from a healthy participant are available at https://github.com/JY9292/BAMS_System.
Code availability
The analysis code that supports the findings of this study is available at https://github.com/JY9292/BAMS_System.
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Acknowledgements
This work was supported by the Querrey-Simpson Institute for Bioelectronics at Northwestern University. W.S. is supported by the Pediatric Research Foundation, and both W.S. and G.M.S. were supported by the Montreal Children’s Hospital Foundation (via the Smart Hospital Project) for this project.
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J.-Y.Y., S.O., W.S., W.-Y.M., E.C., G.M.S., D.E.W.-M., A. Bharat and J.A.R. conceived the idea, designed the research and wrote the manuscript. J.-Y.Y., S.O., W.-Y.M., M.-K.C., M.P., S.S., S.Y., G.K. and H.J. designed the device and carried out fabrication. J.-Y.Y. designed operation protocols and the graphical user interface. J.-Y.Y., W.S., E.C., E.J., S.L., A.T. and A. Bharat analyzed data. J.-Y.Y., S.O., W.S., E.C., E.J., M.C., S.L., Y.W., E.O., A. Banks, G.M.S., D.E.W.-M., A. Bharat and J.A.R. carried out experimental validation and analysis. All authors have read and approved the manuscript.
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Nature Medicine thanks C. Mascolo, J. Ho, J. DiFiore and S. Niu for their contribution to the peer review of this work. Primary Handling Editor: Michael Basson, in collaboration with the Nature Medicine team.
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Extended data
Extended Data Fig. 1 Design for continuous physiological monitoring system with visual feedback.
A photograph of visual feedback and block diagram illustrating the real-time operational scheme.
Extended Data Fig. 2
Flow chart of two-step adaptive acoustic filtering for separate measurements of body and ambient sounds.
Extended Data Fig. 3 Characterization of sound separation using the BAMS system.
(a, b) Experimental setup using a lung sound trainer, sound meter, with (a) a BAMS system and (b) a commercial digital stethoscope (3 M™ Littmann® CORE, Eko) with active noise cancellation (c) Breath sound and heart sound intensity recorded in an ambient of 90 dB white noise across frequency from 20 to 400 Hz (d, e) Signal-to-noise ratio (SNR) of breath sound and heart sound for the BAMS system and the commercial digital stethoscope, measured in different ambient conditions, including (d) levels of white noise (n = 50 datapoints) and (e) types of sounds with a noise level of 75 dB (n = 50 datapoints). Data are presented as the mean ± standard deviation of SNR.
Extended Data Fig. 4 Cardiopulmonary monitoring and cardio-respiratory coupling analysis during daily activities.
(a) Data corresponding to skin temperature, physical activity, and body sounds captured during daily activities. (b, c) Spectrogram images and intensity of breath and heart sounds as a function of time for recordings collected indoors and outdoors. (d) Respiratory rate, heart rate, heart sound intensity, heart rate variability, and cardio-respiratory coupling extracted from data collected indoors and outdoors. (e) Correlation between heart rate and respiratory rate (n = 4291 datapoints). (f) Cardio-respiratory coupling values as a function of physical activity levels (n = 4291 datapoints). Data are presented as the mean ± standard deviation of cardio-respiratory coupling values.
Extended Data Fig. 5 Continuous long-term cardiorespiratory monitoring during sleep and vigorous activity.
Spectrogram image of cardiorespiratory signal and time series results of activity, respiratory rate, breath sound intensity, heart rate, and cardiac sound intensity.
Extended Data Fig. 6 Monitoring of neonatal cardiac activity using the BAMS system.
(a) ECG signal, spectrogram image, and heart sound intensity. (b) Bland-Altman plots comparing heart rate determined using the BAMS system with ECG measurements (5 neonates, 136,013 data points).
Extended Data Fig. 7 Monitoring of neonatal respiratory behavior using the BAMS system, nasal thermistor, and respiratory inductance plethysmograms.
Spectrogram images and time series results comparing respiratory behaviors obtained from breath sounds measured with the microphone in a BAMS system, temperature measured with a nasal thermistor, chest wall movement measured with the IMU in a BAMS system, and the summation of respiratory inductance plethysmograms measured with chest and abdomen RIP bands.
Extended Data Fig. 8 Respiratory rate data collected in the NICU and comparisons to standard measurements.
Bland-Altman plots comparing respiratory rate determined using the BAMS system with nasal temperature flow measurements (5 neonates, 42,738 data points).
Extended Data Fig. 9 Effects of ambient noise on measurements of respiratory sounds.
(a) Microphone data and spectrogram image of respiratory sounds with and without 70 dB white noise. (b) Frequency distribution of respiratory sounds determined by FFT of recorded data. (c) Spectrogram image and respiratory sound intensity with bandpass filtering from 150 Hz to 300 Hz, and (d) spectrogram image and respiratory sound intensity with sound separation.
Supplementary information
Supplementary Information
Supplementary Figs. 1–32 and Tables 1 and 2.
Supplementary Video 1
Audio reconstructions of body sound data with and without sound separation.
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Yoo, JY., Oh, S., Shalish, W. et al. Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring. Nat Med 29, 3137–3148 (2023). https://doi.org/10.1038/s41591-023-02637-5
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DOI: https://doi.org/10.1038/s41591-023-02637-5
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