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A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects

Nature Biotechnology volume 42, pages 448–457 (2024)Cite this article

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Abstract

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.

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Fig. 1: Overview of the fully integrated USoP.
Fig. 2: Monitoring and analysis of tissue interface motions using the USoP.
Fig. 3: Autonomous and continuous blood pressure recording in a moving subject.
Fig. 4: Continuous monitoring during exercise.

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Data availability

The data and material resources supporting the findings of this study are available within the article and supplementary materials. The raw data can be found in a publicly accessible repository77 (https://doi.org/10.6084/m9.figshare.22631047.v4).

Code availability

The codes used in M-mode image classification and domain adaptation can be found in a publicly available repository (https://github.com/JackLin95/Autonomous-Ultrasound-CodeData.git).

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Acknowledgements

We thank Z. Wu, W. Qiu and X. Tian for guidance on the ultrasonic sensor and circuit design; A. Abdal for helping with mechanical simulation; and J. Li for sensor fabrication. The material was partially based on research sponsored by the Air Force Research Laboratory (AFRL) under agreement number FA8650-18-2-5402. The US Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the AFRL or the US Government. This research was partially supported by the National Institutes of Health (NIH) (grant no. 1 R01 EB033464-01). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. All biological experiments were conducted in accordance with the ethical guidelines with the approval of the Institutional Review Board of the University of California, San Diego.

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Author notes

  1. These authors contributed equally: Muyang Lin, Ziyang Zhang, Xiaoxiang Gao.

Authors and Affiliations

  1. Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA

    Muyang Lin, Xiaoxiang Gao, Yizhou Bian, Ray S. Wu, Geonho Park, Zhiyuan Lou, Xiangchen Xu, Wentong Yue, Lu Yin, Hongjie Hu, Hao Huang, Amer Yaghi, Yimu Chen, Yusheng Lei, Ruotao Wang, Joseph Wang & Sheng Xu

  2. Department of Computer Science Engineering, University of California San Diego, La Jolla, CA, USA

    Ziyang Zhang

  3. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Zhuorui Zhang & Chonghe Wang

  4. Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA

    Xiangjun Chen, Xinyi Yang, Baiyan Qi, Sai Zhou, Yue Gu, Jing Mu & Sheng Xu

  5. Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA

    Andrea Kang, Mohan Li, Chengchangfeng Lu, Nuno Vasconcelos & Sheng Xu

  6. Department of Neurosurgery, Yale University, New Haven, CT, USA

    Yue Gu

  7. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Albert Yang, Erik B. Kistler & Sheng Xu

  8. Department of Chemical Engineering, Stanford University, Stanford, CA, USA

    Yusheng Lei

  9. Softsonics LLC, San Diego, CA, USA

    Shu Xiang

  10. Department of Anesthesiology and Critical Care, University of California San Diego, La Jolla, CA, USA

    Erik B. Kistler

  11. Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Sheng Xu

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  1. Muyang Lin
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Contributions

M.Y.L., Z.Y.Z., X.X.G. and S. Xu designed the research. M.Y.L., Z.Y.Z., Y.Z.B., R.S.W., G.P. and Z.Y.L. performed the experiments. Z.Y.Z., Y.Z.B. and Z.R.Z. designed the signal processing algorithms. M.Y.L., Z.Y.Z. and S. Xu analyzed the data. M.Y.L., Z.Y.Z. and S. Xu wrote the paper. All authors provided constructive and valuable feedback on the paper.

Corresponding author

Correspondence to Sheng Xu.

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Nature Biotechnology thanks Pranav Rajpurkar, Michel Maharbiz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterizing bandwidth, axial resolution, and penetration of the stretchable ultrasonic probes.

a, Pulse-echo response and bandwidth of the probes with three frequencies. The full width at half maximum (FWHM) is labeled to show the axial resolution of each probe. The 2 MHz, 4 MHz and 6 MHz can achieve 604 μm, 333 μm and 229 μm resolution, respectively. Three probes could achieve a relative bandwidth of ~50% to their center frequencies at -3 dB. b, The pulse-echo response of a commercial ultrasound probe with a center frequency of 3 MHz, which could achieve a relative bandwidth of 42.3%. c, Tissue targets to be sensed by the stretchable ultrasonic probe in this work. The 2 MHz probe is used for deep organ (for example, heart and diaphragm) sensing. The 4 MHz probe is used for deep major artery (for example, carotid, femoral, and abdominal aorta) sensing. The 6 MHz probe is used for shallow peripheral artery (for example, radial and brachial) sensing. d, Transmission beam intensities as a function of penetration depth in tissues of the probes with different frequencies. The intensity decay was measured in water, and then converted into tissue decay with an attenuation factor of -0.3 dB/cm/MHz. Based on the penetration threshold of a -3 dB drop in intensity, the 2 MHz, 4 MHz and 6 MHz can penetrate 164.0 mm, 77.7 mm and 9.2 mm, respectively.

Extended Data Fig. 2 Schematics and control sequence of ultrasonic sensing.

a, Block diagram and signal transmission lines between the functional modules. The control circuit includes two parts: the AFE and the wireless DAQ module. The AFE consists of a multiplexer (Mux), a transmit/receive switch (T/R SW), a receiver, a sequencer, and a pulse generator. The DAQ module consists of a microcontroller (MCU) with an on-chip analog-to-digital convertor (ADC), and a Wi-Fi transmitter. The dashed lines are for digital signal transmission and the solid lines are for analog signal transmission. b, Simulated control sequence for multiplexing and pulse-echo sensing, which shows the time sequence of the receive (Rx) enable, trigger, high-voltage (HV) pulse, clock (CLK), reset (RES), digital input (Din), and latch enable (\(\overline{{\rm{LE}}}\)) signals. c, Signals acquired by an oscilloscope showing the control sequence of the pulse-echo sensing and transducer multiplexing. d, Signals acquired by an oscilloscope showing the input sequence to the shift register for multiplexing and driving the transducer elements. All figure panels share the same color encoding scheme.

Extended Data Fig. 3 Deformation of the packaged USoP.

a, 90° bending, b, 90° twisting, and c, 20% uniaxial stretching of the packaged USoP. d, A zoom-in view of the stretched interconnects.

Extended Data Fig. 4 Skin integration of the conformal USoP device.

The soft patch could conform to multiple curved body parts, including a, forearm, b, brachium, c, neck, d, lower chest, and e, abdomen. f-g, Skin integration of the device before and after exercise. The USoP could maintain robust adhesion to the skin after the subject performs intensive exercise and sweats.

Extended Data Fig. 5 Pulse wave velocity (PWV) measurements.

a, Schematic illustration of the pulse wave propagation paths in this study. Five paths were investigated, including the heart to the abdominal aorta (H-Ao), the heart to the carotid artery (H-CA), the heart to the femoral artery (H-FA), the heart to the brachial artery (H-BA), and the brachial artery to the radial artery (BA-RA). b, Pulse waveforms collected by synchronized USoP pairs. The pulse transit time (PTT) was defined as the delay between the diastolic feet of the ventricular contraction and arterial pulses. c, The average PTT values by the USoP and the tonometer, showing consistency for both H-BA and BA-RA. Ten consecutive pulses were recorded to calculate average PTT values. The error bars represent the measurement standard deviations. d, PWV calculated across five arterial segments using the USoP. e, PWV mapping under normal conditions and cold pressor test. The average PWV along each path was calculated from five independent measurements. The error bars indicate the standard deviations of the measured values. The PWV increases from heart-proximal to heart-distal branches. There is a regional increase of PWV in H-BA and BA-RA segments owing to cold-induced vasoconstriction.

Extended Data Fig. 6 The validation metrics of four models on ideal and compromised image datasets.

a, The images used for validation including ideal carotid artery images and compromised images (for example, noise coupled images, artery shifting images and artery missing images). b, The receiver operating characteristic curves validated on 460 ideal images, suggesting the best model VGG13 has an area under the curve value of 100%. c, The precision, recall and accuracy validated on ideal images. d, The receiver operating characteristic curves validated on 460 images with a mix of ideal and compromised images, suggesting the best model VGG13 has an area under the curve value of 99.4%. e, The precision, recall and accuracy validated on mixed ideal and compromised images.

Extended Data Fig. 7 Continuous monitoring during high-intensity interval training (HIIT).

a, Photographs showing the participant performing HIIT. Six training sessions, including (i) touch shoulder push-ups, (ii) cycling Russian twist, (iii) push-up rotations, (iv) burpees, (v) side kick through and (vi) hand-release push-ups. b, The head motions are recorded by the inertia measurement units, which show the rolling, yawing and pitching rates during the 12 min training and rest. The carotid blood pressure waveforms and heart rate are recorded simultaneously and continuously using the USoP. The systolic pressure increased ~25 mmHg between training sessions and rest sessions, while the diastolic pressure experienced less fluctuation. c, Zoomed-in view of the head motions, continuous blood pressure waveforms and heart rate recorded during the training sessions.

Supplementary information

Supplementary Information

Supplementary Discussions 1–20, Figs. 1–39, Tables 1–5 and Video captions 1–3.

Reporting Summary

Supplementary Video 1

B-mode imaging of the carotid artery and jugular vein. The cross-sectional structure of the carotid artery and a dilated jugular vein could be identified. The subject performed the Valsalva maneuver to dilate the jugular vein during the recording period.

Supplementary Video 2

Autonomous carotid artery tracking under head yawing. The USoP tracked the carotid artery motion while a commercial probe was manually placed adjacent to the USoP to image the carotid artery (left). The participant rotated the head to induce displacement of the carotid artery. The prediction profile was able to follow the moving artery during head rotation (right).

Supplementary Video 3

Continuous blood pressure waveforms recorded during cycling. A point-of-view camera was mounted on the participant’s head to record the motions during cycling (left). The carotid pulse waveforms were continuously recorded by the USoP. A smartphone application could host the data and display tissue motion images, pulse waveforms, heart rate and blood pressure values (right). The tissue motion images illustrated arterial wall positions. The continuous pulse waveforms showed the real-time pulsation of the carotid artery. The corresponding heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were displayed simultaneously.

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Lin, M., Zhang, Z., Gao, X. et al. A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects. Nat Biotechnol 42, 448–457 (2024). https://doi.org/10.1038/s41587-023-01800-0

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