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Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces

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Science Advances  16 Nov 2016:
Vol. 2, no. 11, e1601185
DOI: 10.1126/sciadv.1601185
  • Fig. 1 Schematic illustration of an epidermal mechano-acoustic–electrophysiological measurement device.

    (A) Exploded view diagram of the overall design structure of the system. (B) Illustration of the assembled device and its interface with soft EP measurement electrodes and flexible cable for power supply and data acquisition. A cross-sectional view appears in the upper inset. (C) Device held by tweezers in a twisted configuration. (D) Device mounted on skin while compressed by pinching. (E) Fluorescence micrographs of cells cultured on the surface of a device to illustrate its biocompatibility. Green and red regions correspond to live and dead cells, respectively. The white arrowheads highlight the latter. Scale bars, 200 μm. (F) Overlay of optical image and finite element simulation results for a device under biaxial stretching to a strain of 25%. (G) Magnified view of modeling results for the part of the interconnect structures that experiences the highest strain. (H) Vibrational response summarized in a plot of spectral power measured while mounted on a layer of chicken breast, to simulate tissue, on a vibration source.

  • Fig. 2 Summary of the experimental and computational studies of the effects of device mass, modulus, tissue thickness, and signal frequency on measured mechano-acoustic responses.

    Experimentally measured spectral power and simulation results associated with the mechano-acoustic response of a device mounted in an acrylic box placed on a tissue sample on a vibrational source at frequencies of 50 Hz (A), 100 Hz (B), and 200 Hz (C). (D) Comparison of measured (experiment) and computed (analytical) dependence of spectral power on mass. (E) Measured maximum signal amplitude recorded with a device mounted on the neck as the subject said the word “left,” as a function of the mass of the device. (F) Amplitude measured using a device in a rigid box and on a thin substrate of Ecoflex, as a function of spatial location of the added mass.

  • Fig. 3 Application of mechano-acoustic–electrophysiological sensing with an epidermal device in diagnosing cardiovascular health status.

    (A) Image of an epidermal device mounted on the chest. (B) Schematic diagram of cardiac cycle: (left) artrial and ventricular diastole, (middle) artrial systole and ventricular diastole, and (right) ventricular systole and atrial diastole. (C) Plot of ECG (top) and heart sound (bottom) signals measured simultaneously. A.U., arbitrary units. (D) Magnified view of ECG (top) and heart sound (bottom) signals measured in (C). MC, mitral valve closure; AO, aortic valve opening; RE, rapid ventricular ejection; AC, aortic valve closure; MO, mitral valve opening; RF, rapid ventricular filling. (E) Comparison of heart sound signals measured using a commercial electronic stethoscope and the reported device. (F) Schematic illustration of the measurement site: A, aortic; P, pulmonary; T, tricuspid; M, mitral. Representative measurement from a 78-year-old female patient with diagnosed mild pulmonary and tricuspid regurgitation at the aortic (G), tricuspid (H), pulmonary (I), and mitral (J) sites.

  • Fig. 4 Application of mechano-acoustic sensing with an epidermal device in diagnosing VAD operation.

    (A) Image of the experimental circulation loop with the device mounted on the VAD (HeartMate II). (B) Fast Fourier transform (FFT) of the vibration response (top) and spectrogram (bottom) associated with the operation of the VAD at 8400 rpm in a water circulation loop. (C) FFT spectral power of the vibration response for operating frequencies between 8400 and 9400 rpm. Distinctive changes with VAD speed occur only on the peak around 150 Hz. (D) Comparison of vibrational responses in a circulation loop with water and with glycerol at 8400 rpm (top) and 9400 rpm (bottom). (E) Demonstration of changes in acoustic signature associated with circulation of a blood clot (500 μl) in the glycerol loop during stages of initial injection of the blood clot, first few circulation passes without decomposition, subsequent complete decomposition, and circulation of tiny blood clots.

  • Fig. 5 Application of mechano-acoustic sensing with an epidermal device for speech recognition.

    (A) Image of an epidermal device mounted on the vocal cords. (B) Plot of EMG (top) and vocal vibrational (bottom) signals measured simultaneously from the neck. (C) Comparison of speech recorded with the reported device (top) and with an external microphone (bottom). The left and right columns represent recordings made under quiet and noisy conditions, respectively. (D) Confusion matrix that describes the performance of the speech classification. (E) Demonstration of speech recognition and classification in a Pac-Man game with left, right, up, and down instruction.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/11/e1601185/DC1

    note S1. Analytical model for mass effect on acceleration.

    note S2. Effect of low-modulus device substrate.

    fig. S1. Device design and circuit layouts.

    fig. S2. Computed x-ray tomography images of the internal structures of the accelerometer chip.

    fig. S3. Schematic illustration of capacitive ECG electrodes and demonstrations of their reusability.

    fig. S4. Adhesion strength of Silbione to the skin and dependence of its thickness on spin speed.

    fig. S5. Measurements of water vapor transmission loss.

    fig. S6. Cell viability assay and cytotoxicity test.

    fig. S7. Mechanical simulation of the circuit interconnects during biaxial stretching.

    fig. S8. Stress-strain response of the device.

    fig. S9. Vibration response of the accelerometer chip without analog filters.

    fig. S10. Comparison of experimental and simulation results on the effect of mass, tissue thickness, and signal frequency on measurement response.

    fig. S11. Schematic illustration and measurement results of the vibration model to capture the effects of device modulus.

    fig. S12. Application of an epidermal mechano-acoustic–electrophysiological device on the neck.

    fig. S13. Echocardiogram characterization results on a patient with tricuspid and pulmonary regurgitation.

    fig. S14. Acoustic signals from aortic, pulmonary, tricuspid, and mitral sites of a patient with irregular heartbeat.

    fig. S15. Experiment on LVAD pump thrombosis.

    fig. S16. Data captured using a reported device and a commercial microphone in quiet and noisy environments.

    fig. S16. Data captured using a reported device and a commercial microphone in quiet and noisy environments.

    fig. S17. Process loop for a speech-based human-machine interface.

    fig. S18. Demonstration of noise reduction in time domain speech data.

    fig. S19. Authentication application.

    fig. S20. Wireless sensing of BioStamp.

    movie S1. Movie of speech recording in a quiet environment.

    movie S2. Movie of speech recording in a noisy environment.

    movie S3. Movie of speech recognition and voice control of a Pac-Man game with real-time machine learning and signal classification.

  • Supplementary Materials

    This PDF file includes:

    • note S1. Analytical model for mass effect on acceleration.
    • note S2. Effect of low-modulus device substrate.
    • fig. S1. Device design and circuit layouts.
    • fig. S2. Computed x-ray tomography images of the internal structures of the accelerometer chip.
    • fig. S3. Schematic illustration of capacitive ECG electrodes and demonstrations of their reusability.
    • fig. S4. Adhesion strength of Silbione to the skin and dependence of its thickness on spin speed.
    • fig. S5. Measurements of water vapor transmission loss.
    • fig. S6. Cell viability assay and cytotoxicity test.
    • fig. S7. Mechanical simulation of the circuit interconnects during biaxial stretching.
    • fig. S8. Stress-strain response of the device.
    • fig. S9. Vibration response of the accelerometer chip without analog filters.
    • fig. S10. Comparison of experimental and simulation results on the effect of mass, tissue thickness, and signal frequency on measurement response.
    • fig. S11. Schematic illustration and measurement results of the vibration model to capture the effects of device modulus.
    • fig. S12. Application of an epidermal mechano-acoustic–electrophysiological device on the neck.
    • fig. S13. Echocardiogram characterization results on a patient with tricuspid and pulmonary regurgitation.
    • fig. S14. Acoustic signals from aortic, pulmonary, tricuspid, and mitral sites of a patient with irregular heartbeat.
    • fig. S15. Experiment on LVAD pump thrombosis.
    • fig. S16. Data captured using a reported device and a commercial microphone in quiet and noisy environments.
    • fig. S17. Process loop for a speech-based human-machine interface.
    • fig. S18. Demonstration of noise reduction in time domain speech data.
    • fig. S19. Authentication application.
    • fig. S20. Wireless sensing of BioStamp.
    • Legends for movies S1 to S3

    Download PDF

    Other Supplementary Material for this manuscript includes the following:

    • movie S1 avi movie S1 .mp4 (.avi and .mp4 format). Movie of speech recording in a quiet environment.
    • movie S2 avi movie S2 mp4 (.avi and .mp4 format). Movie of speech recording in a noisy environment.
    • movie S3 avi movie S3 wmv (.avi and wmv format). Movie of speech recognition and voice control of a Pac-Man game with real-time machine learning and signal classification.

    Files in this Data Supplement:

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