Research ArticleBiosensors

Epidermal electronics for noninvasive, wireless, quantitative assessment of ventricular shunt function in patients with hydrocephalus

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Science Translational Medicine  31 Oct 2018:
Vol. 10, Issue 465, eaat8437
DOI: 10.1126/scitranslmed.aat8437
  • Fig. 1 Soft, skin-mounted wearable device for noninvasive, continuous, or intermittent measurement of flow through cerebrospinal shunts.

    (A) Exploded view illustration of a platform that incorporates a central thermal actuator surrounded by 100 precision temperature sensors, placed over the skin with an underlying shunt catheter. PI, polyimide. (B) Optical micrograph of the device, showing thermal actuator (surrounded by purple dashed line), with enlarged images showing stretchable, serpentine interconnects (bottom left, blue dashed line) and individual resistive temperature sensors (bottom right, red dashed line). (C) Infrared (IR) thermographs collected during measurement by an individual sensor (left), and thermal actuation with a power of 1.8 mW/mm2, with representative voltage supply line (Vsup) and current measurement line (Imeas) marked in red and green, respectively. (D) Optical images of a device over a shunt during deformation to illustrate the robustness of the soft adhesion. Device was adhered to the skin on a seated subject 2 cm above the clavicle; the edge of the silicone substrate of the shunt is shown (arrows). (E) IR thermographs with color and contrast enhancement to highlight the spatial isotropy of the distribution of temperature in the absence of flow (top) and the anisotropy in the presence of flow (bottom). Flow is to the right (arrow).

  • Fig. 2 Visualization of flow and measurements using an ESA.

    (A) Schematic map of a device, with indication of the tube position (blue shading), and the temperatures at upstream (Tu) and downstream (Td) locations. i, j, and k represent coordinates for sensor identification (j and k for Tu and Td, respectively). (B) Temperature differentials of four sensor pairs after baseline subtraction. Color coding in (A) denotes sensor locations. (C) PCA biplot (PC1 and PC2) of baseline-subtracted differentials between a selected Tu sensor (one off the catheter and one on the catheter as indicated) and each Td sensor. Clustering occurs for the following cases: no flow and no actuation, no flow with actuation at 1.8 mW/mm2, actuation at 1.8 mW/mm2 and flow at 0.02 ml/min, actuation at 1.8 mW/mm2, and flow at 2 ml/min. Vectors correspond to selected Td sensors correlated positively (red) and negatively (blue) with flow. (D) Flow chart of the process for transforming raw ESA sensor data to spatially precise temperature maps. (E) Thermographs from IR imaging (top) and ESA-generated temperature maps (bottom) in the absence (left) and presence (right) of flow (0.02 ml/min; flow from right to left) with actuation at 1.8 mW/mm2. All data were collected on a skin phantom.

  • Fig. 3 Systematic characterization of the effects of geometry, thermal properties, and flow rates.

    (A) Optical image of ELA overlaid with an illustration of a catheter and a blood vessel (top) and schematic illustration of a benchtop system illustrating key features, including thermal properties of the skin phantom, CSF flow (Qflow), and skin thickness (hskin). (B) Temperatures measured after the onset of heating for the actuator (blue curve), the downstream sensor (black curve), and the upstream sensor (red curve) for four values of Qflow: 0 ml/min (unshaded region), 0.05 ml/min (blue-shaded region), 0.1 ml/min (gray-shaded region), and 0.5 ml/min (orange-shaded region). (C) Tsensors/Tactuator for the upstream (red curve) and the downstream (black curve) sensors across a range of flow rates from 0.01 to 0.1 ml/min. (D) ΔTsensors/Tactuator = (TdownstreamTupstream)/Tactuator for a range of Qflow from 0.01 to 0.1 ml/min for three anatomically relevant values of hskin: 1.1 mm (black curve), 1.7 mm (red curve), and 2.1 mm (blue curve). (E) Embedded Imagesensors = (Tdownstream + Tupstream)/2Tactuator for the same Qflow and hskin values as in (D). (F) In vitro experimental measurements of ΔTsensors/Tactuator for hskin (1.1, 1.7, 2.1, and 6.0 mm for four flow rates) and for Qflow [0 ml/min (black curve), 0.05 ml/min (red curve), 0.1 ml/min (blue curve), and 0.5 ml/min (purple curve)]. (G) Ratio between signal (ΔTsensors/Tactuator) and noise (SD, σ) measured for Qflow (0.1 ml/min) over a 60-s sampling window at a sampling frequency of 5 Hz, as a function of normalized actuator power for three different values of hskin [1.1 mm (black curve), 1.7 mm (red curve), and 2.1 mm (blue curve)]. (H) In vitro experimental measurements (solid lines) and analytical fits (dashed lines) for ΔTactuator as a function of time for Qflow = 0 for two different skin phantoms, Sylgard 184 (black curve) and Sylgard 170 (gray curve) to simulate and measure human skin thermal properties (double-headed arrow). All data were collected on a skin phantom.

  • Fig. 4 Wireless data acquisition.

    (A) Optical micrograph of fully assembled, integrated wireless ELA showing soft, conformal sensing/actuating components, flex-PCB (Cu/PI/Cu), and surface-mounted electronic components. PDMS, polydimethylsiloxane. (B) Optical image of device bending, showing flexibility. (C) Optical image of device mounted on the skin using medical-grade, acrylate-based pressure-sensitive adhesive. (D) Schematic illustration of analog front-end, analog-to-digital converter (ADC), Bluetooth (BLE) transmission electronics, and 3.3-V power supply with custom smartphone application for real-time data readout and logging (right). (E) Raw sensor readout in measured bits from an 8-bit ADC during actuation and flow. (F) IR-measured temperature rise due to 3.6-mW actuation on the phantom shunt assembly. (G) Calibration curve to measure raw 8-bit, 3-V ADC values (left) and associated voltages (right) to temperatures via calibration. (H) Difference in Tupstream and Tdownstream acquired wirelessly as a function of time for two different flows, Q = 0.05 ml/min and Q = 0.13 ml/min. All data were collected on a skin phantom.

  • Fig. 5 Patient trials.

    (A) Exploded view illustration of an ELA designed for use in a hospital setting, with elastomeric handling frame and adhesive. (B) Illustration (left) and image (right) of on-shunt (Embedded Image) and off-shunt (Embedded Image) ELA positioning on a patient, with representative Doppler ultrasound image (inset) of the catheter under the skin at the on-shunt location. (C) IR images at on-shunt (top) and off-shunt (bottom) locations indicating the local increase in temperature induced by the actuator, and characteristic teardrop-shaped heat distribution caused by the presence of flow. (D) Computed mean of ΔTsensors/Tactuator measured for each patient at off-shunt and on-shunt location cases, with error bars representing SDs across a 100-sample window. (E) Computed mean of ΔTsensors/Tactuator on n = 5 patients with clinically or surgically confirmed flow on off-shunt and on-shunt locations, with error bars representing SD. Statistical analysis was performed using a paired t test (n = 5) for cases with confirmed flow over on-shunt and off-shunt locations. Individual patient-level data and details of the paired Student’s t tests are shown in table S5.

  • Fig. 6 Case study of a patient with hydrocephalus with shunt malfunction.

    (A) X-ray and radionuclide tracer showing kinking and occlusion of catheter. (B) Optical image of patient’s peritoneal cavity immediately after surgery showing flow in repaired shunt. (C) X-ray and radionuclide tracer confirming proper operation of the repaired shunt. (D) ΔTsensors/Tactuator measured by ELA at locations over (on) and adjacent to (off) the shunt before and after revision, confirming results from x-ray and radionuclide tracer. Blue shading indicates revision period. All data were collected on a patient (n = 1).

  • Fig. 7 Computation of flow rates.

    (A) FEA-computed values of ΔTsensors/Tactuator and Embedded Imagesensors/Tactuator using values of hskin = 1.5 mm (acquired from CT imaging) and kskin = 0.29 W m−1 K−1 and αskin = 0.091 mm2 s−1 acquired in vivo from a patient as shown in Fig. 5, overlaid with experimentally measured points from the same patient, yielding a flow rate of 0.1 ml/min. (B) FEA-computed family of curves of ΔTsensors/Tactuator (top) and Embedded Imagesensors/Tactuator (bottom) for different skin thicknesses with data measured in vivo from each patient, assuming kskin = 0.32 W m−1 K−1 and αskin = 0.1 mm2 s−1. (C) Computed flow rates from iteratively solving for both ΔTsensors/Tactuator and Embedded Imagesensors/Tactuator. The error bars represent average differences in the individual values yielded by the two curves, and the colored background identifies ranges of healthy flow (green) and failure (red). All data were collected on patients (n = 5).

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/10/465/eaat8437/DC1

    Materials and Methods

    Fig. S1. Current pathways through resistive arrays.

    Fig. S2. Schematic illustration of the DAQ and control system for an array of 100 sensors.

    Fig. S3. Calibration map for ESA.

    Fig. S4. PCA for determining the presence of flow with an ESA.

    Fig. S5. PCA for determining the orientation and magnitude of flow with an ESA.

    Fig. S6. Flow diagram detailing the process for conversion of raw ESA sensor recordings to a spatial temperature map.

    Fig. S7. Benchtop flow system.

    Fig. S8. Depth of thermal penetration.

    Fig. S9. Transient thermal analysis of flow.

    Fig. S10. Flow measurements through thick (6 mm) layers of soft tissue.

    Fig. S11. Effect of actuator power.

    Fig. S12. Experimentally measured effects of changing skin thermal properties.

    Fig. S13. Simulated effects of changing skin thermal properties.

    Fig. S14. Effect of ambient free air convection.

    Fig. S15. Effect of uncertainty in placement.

    Fig. S16. Effect of altered intersensor distances.

    Fig. S17. Low-frequency dc noise sources.

    Fig. S18. High-frequency ac and dc noise.

    Fig. S19. In vivo noise.

    Fig. S20. Prevention of delamination during extreme deformation via adhesive design.

    Fig. S21. Effect of near-surface blood vessels.

    Fig. S22. Wired and wireless DAQ and control systems.

    Fig. S23. Wireless control via smartphone.

    Fig. S24. Wired DAQ used in clinical trials.

    Fig. S25. Raw in vivo data.

    Fig. S26. In vivo measurements of skin thermal properties.

    Fig. S27. Measurements made over EVD.

    Fig. S28. In vivo measurements of skin thickness made via radiographic and ultrasound imaging.

    Table S1. Thermal and geometrical quantities required for quantitative measurement of flow rate.

    Table S2. Summary of etiology of and measurements made on each patient.

    Table S3. Raw data measured on each patient.

    Table S4. Raw data and results from paired t tests for on-shunt and off-shunt measurements for patients with patent shunts.

    Table S5. Summary of technical challenges and key advancements over the course of patient study.

    Table S6. Summary of existing shunt diagnostic tools.

    Movie S1. Wireless ELA pairing to smartphone app and on-demand actuation.

    Movie S2. Experimental system in movie S1.

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Current pathways through resistive arrays.
    • Fig. S2. Schematic illustration of the DAQ and control system for an array of 100 sensors.
    • Fig. S3. Calibration map for ESA.
    • Fig. S4. PCA for determining the presence of flow with an ESA.
    • Fig. S5. PCA for determining the orientation and magnitude of flow with an ESA.
    • Fig. S6. Flow diagram detailing the process for conversion of raw ESA sensor recordings to a spatial temperature map.
    • Fig. S7. Benchtop flow system.
    • Fig. S8. Depth of thermal penetration.
    • Fig. S9. Transient thermal analysis of flow.
    • Fig. S10. Flow measurements through thick (6 mm) layers of soft tissue.
    • Fig. S11. Effect of actuator power.
    • Fig. S12. Experimentally measured effects of changing skin thermal properties.
    • Fig. S13. Simulated effects of changing skin thermal properties.
    • Fig. S14. Effect of ambient free air convection.
    • Fig. S15. Effect of uncertainty in placement.
    • Fig. S16. Effect of altered intersensor distances.
    • Fig. S17. Low-frequency dc noise sources.
    • Fig. S18. High-frequency ac and dc noise.
    • Fig. S19. In vivo noise.
    • Fig. S20. Prevention of delamination during extreme deformation via adhesive design.
    • Fig. S21. Effect of near-surface blood vessels.
    • Fig. S22. Wired and wireless DAQ and control systems.
    • Fig. S23. Wireless control via smartphone.
    • Fig. S24. Wired DAQ used in clinical trials.
    • Fig. S25. Raw in vivo data.
    • Fig. S26. In vivo measurements of skin thermal properties.
    • Fig. S27. Measurements made over EVD.
    • Fig. S28. In vivo measurements of skin thickness made via radiographic and ultrasound imaging.
    • Table S1. Thermal and geometrical quantities required for quantitative measurement of flow rate.
    • Table S2. Summary of etiology of and measurements made on each patient.
    • Table S3. Raw data measured on each patient.
    • Table S4. Raw data and results from paired t tests for on-shunt and off-shunt measurements for patients with patent shunts.
    • Table S5. Summary of technical challenges and key advancements over the course of patient study.
    • Table S6. Summary of existing shunt diagnostic tools.

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mp4 format). Wireless ELA pairing to smartphone app and on-demand actuation.
    • Movie S2 (.mp4 format). Experimental system in movie S1.

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