Transcranial eddy current damping sensors for detection and imaging of hemorrhagic stroke: feasibility in benchtop experimentation

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  • 1 Departments of Neurological Surgery and
  • | 2 Neurology, and
  • | 3 Laboratory of NeuroImaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles; and
  • | 4 Department of Medical Engineering, California Institute of Technology, Pasadena, California
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OBJECTIVE

Spontaneous intracerebral hemorrhage occurs in an estimated 10% of stroke patients, with high rates of associated mortality. Portable diagnostic technologies that can quickly and noninvasively detect hemorrhagic stroke may prevent unnecessary delay in patient care and help rapidly triage patients with ischemic versus hemorrhagic stroke. As such, the authors aimed to develop a rapid and portable eddy current damping (ECD) hemorrhagic stroke sensor for proposed in-field diagnosis of hemorrhagic stroke.

METHODS

A tricoil ECD sensor with microtesla-level magnetic field strengths was constructed. Sixteen gelatin brain models with identical electrical properties to live brain tissue were developed and placed within phantom skull replicas, and saline was diluted to the conductivity of blood and placed within the brain to simulate a hemorrhage. The ECD sensor was used to detect modeled hemorrhages on benchtop models. Data were saved and plotted as a filtered heatmap to represent the lesion location. The individuals performing the scanning were blinded to the bleed location, and sensors were tangentially rotated around the skull models to localize blood. Data were also used to create heatmap images using MATLAB software.

RESULTS

The sensor was portable (11.4-cm maximum diameter), compact, and cost roughly $100 to manufacture. Scanning time was 2.43 minutes, and heatmap images of the lesion were produced in near real time. The ECD sensor accurately predicted the location of a modeled hemorrhage in all (n = 16) benchtop experiments with excellent spatial resolution.

CONCLUSIONS

Benchtop experiments demonstrated the proof of concept of the ECD sensor for rapid transcranial hemorrhagic stroke diagnosis. Future studies with live human participants are warranted to fully establish the feasibility findings derived from this study.

ABBREVIATIONS

ECD = eddy current damping; ICH = intracerebral hemorrhage; MSU = mobile stroke unit; NIRS = near-infrared spectroscopy; Rp = parallel resistance; STICH = Surgical Trial in Intracerebral Hemorrhage.

OBJECTIVE

Spontaneous intracerebral hemorrhage occurs in an estimated 10% of stroke patients, with high rates of associated mortality. Portable diagnostic technologies that can quickly and noninvasively detect hemorrhagic stroke may prevent unnecessary delay in patient care and help rapidly triage patients with ischemic versus hemorrhagic stroke. As such, the authors aimed to develop a rapid and portable eddy current damping (ECD) hemorrhagic stroke sensor for proposed in-field diagnosis of hemorrhagic stroke.

METHODS

A tricoil ECD sensor with microtesla-level magnetic field strengths was constructed. Sixteen gelatin brain models with identical electrical properties to live brain tissue were developed and placed within phantom skull replicas, and saline was diluted to the conductivity of blood and placed within the brain to simulate a hemorrhage. The ECD sensor was used to detect modeled hemorrhages on benchtop models. Data were saved and plotted as a filtered heatmap to represent the lesion location. The individuals performing the scanning were blinded to the bleed location, and sensors were tangentially rotated around the skull models to localize blood. Data were also used to create heatmap images using MATLAB software.

RESULTS

The sensor was portable (11.4-cm maximum diameter), compact, and cost roughly $100 to manufacture. Scanning time was 2.43 minutes, and heatmap images of the lesion were produced in near real time. The ECD sensor accurately predicted the location of a modeled hemorrhage in all (n = 16) benchtop experiments with excellent spatial resolution.

CONCLUSIONS

Benchtop experiments demonstrated the proof of concept of the ECD sensor for rapid transcranial hemorrhagic stroke diagnosis. Future studies with live human participants are warranted to fully establish the feasibility findings derived from this study.

ABBREVIATIONS

ECD = eddy current damping; ICH = intracerebral hemorrhage; MSU = mobile stroke unit; NIRS = near-infrared spectroscopy; Rp = parallel resistance; STICH = Surgical Trial in Intracerebral Hemorrhage.

Evidence suggests that the lifetime risk of stroke is increasing in the past decades, affecting 24.9% of all adults aged 25 years or older.1,2 Spontaneous intracerebral hemorrhage (ICH) represents an estimated 10% of stroke, with high rates of associated permanent disability and mortality.3 There have been no specific therapies that have clearly demonstrated improvement in clinical outcomes. Supportive management aimed at tight systolic blood pressure control and pharmacological agents such as recombinant factor VII and tranexamic acid have all failed to show clinical benefit.4,5 The efficacy of surgical evacuation remains disputed. The Surgical Trial in Intracerebral Hemorrhage (STICH), as well as the follow-up STICH II trial, failed to show improvement after prompt surgical evacuation versus best medical management.6,7 The STICH and STICH II trials are often criticized for patient crossover from the medical to the surgical cohort, and they do not include patients at risk for herniation in which surgical evacuation is a lifesaving intervention. Although ongoing trials such as ENRICH and MIND may affect indications for ICH evacuation in the near future, improvements on the diagnostic front may allow for rapid field diagnosis and prehospital triage, which has the potential to improve patient outcomes.8

The current paradigm for stroke diagnosis requires CT or MRI prior to intervention to confirm the presence and location of the hemorrhage. Aside from the time required to transport potential stroke patients to the hospital, current national guidelines call for a door-to-imaging time of 25 minutes and imaging interpretation by a physician within 45 minutes.9,10 Delays in patient transportation and imaging may contribute toward increased rates of patient morbidity and mortality. Portable diagnostic technologies that can quickly and noninvasively detect hemorrhagic stroke may streamline patient management and reduce wasted time in the clinic.

As such, we developed a rapid and portable eddy current damping (ECD) hemorrhagic stroke sensor for bedside diagnosis of hemorrhagic stroke. This device operates as an electrical conductivity sensor, since normal brain parenchyma is relatively nonconductive because of its high composition of fatty myelin (0.2 S/m).11,12 However, ICH and hematomata are more conductive (0.65 S/m) than brain parenchyma as a result of higher blood concentrations of dissolved ions and charged proteins.11,12 These differences in conductivity can be leveraged to diagnose ICH and develop crude images of a hematoma location and/or size. We hypothesized that the ECD sensor could accurately and rapidly detect ICH on benchtop testing while achieving acceptable spatial resolution and facilitating organized management and triage of stroke patients.

Methods

ECD Sensor Construction

Our equivalent circuit model consists of three sensor coils paired with capacitors to form electrical resonant circuits (Fig. 1), which are similar to the architecture of previous ECD sensors used in industry for metal detection and crack inspection, consisting of a bridge circuit that measures the sensor coil impedance.13,14 Thus, the sensor operates as a coil carrying an alternating current, which produces a time-varying magnetic field. This magnetic field induces an electromotive force that creates a looping “eddy” current in the conductive material described through Ohm’s law. The coil within the sensor is shaped like a solenoid, with the number of turns and the wire length designed to operate around 1 MHz and produce a magnetic field on the order of several microteslas, several orders of magnitude lower than that of transcranial magnetic stimulation and MRI.15 Each coil is connected to an LDC 1101 (Texas Instruments Inc.) chip for inductance-to-digital conversion of the signal.16 As a result, when a conductive target, such as a hemorrhage (0.65 S/m), is placed within range of the sensor, eddy currents generated within the target produce a counteracting magnetic field, resulting in a decrease in coil inductance and an increase in coil resonant frequency.11

FIG. 1.
FIG. 1.

ECD sensor components and construction. Nonconductive cylindrical plastic scaffolds (1), copper wire wound as a solenoid around each plastic scaffold creating the sensor coil (2), LDC 1101 inductance-to-digital converting chips connected to each coil (3), USB connection from the chip to a local computer (4), and a laptop for signal visualization, storage, and analysis (5). Made in ©BioRender - biorender.com.

ECD Sensor Benchtop Model and Image Production

To demonstrate the feasibility of our sensor, we developed a benchtop intracranial hemorrhage model. We developed this model using a plastic life-size skull replica, gelatin brain parenchyma, and diluted saline to mimic blood. The gelatin brain was confirmed to have the same uniform conductivity at room temperature as its in vivo counterpart, which is 0.2 S/m.11,17 Saline was diluted to a conductivity of 0.65 S/m to mimic blood and injected into several latex balloons, with the volume of each balloon ranging from roughly 30 mL to 50 mL. Eight equidistant horizontal scanning rows were marked on the skull replica with flexible tubing, with the first row just above the eyebrows and the last row at the occiput, along which the sensor moved to measure and record parallel resistance (Rp) values (Fig. 2). Store-bought gelatin was added to 1000 mL of deoinized water according to the formula developed by Kandadai et al. and brought to a boil.17 A plastic bag was placed within the plastic skull model such that the opening of the bag exited the foramen magnum on the skull. Once all the gelatin had dissolved, it was allowed to cool, and the conductivity was measured. Table salt was added to the solution to bring the conductivity to 0.2 S/m. Following placement and scanning of the model, data were filtered in MATLAB (MathWorks) software to remove high-frequency noise, the collected data from each row were downsampled to 8 averaged points, and control values were subtracted to produce a differential signal. Thresholding was used to improve image resolution such that all data points less than 95% of the maximum conductivity in the heatmap were removed. We used the 8 data points from the 8 scanning rows to create an 8 × 8 interpolated conductivity heatmap, with brighter areas indicating higher probability of hemorrhage locations.

FIG. 2.
FIG. 2.

Skull model and sensor placement for scanning. Upper: Eight equidistant rows were created on the phantom head model. The sensor was tangentially rotated across each row, collecting Rp data that reflected the brain’s conductivity at that point. Lower: Regions of hemorrhage have higher conductivity, and the ECD sensor can identify this conductivity change through the skull.

Results

Sensor Analysis

Using the Biot-Savart law, the maximum calculated magnetic fields of the three sensors (at a distance of 0 cm) were 0.1786 μT for the largest coil, 1.131 μT for the medium-sized coil, and 7.917 μT for the smallest coil. Using the specific heat and power equations with the known current, voltage, specific heat of the copper wire, and mass values, it was calculated that the minimum time necessary to heat the coils by 1°C was 2742.17 seconds.

ECD Sensor Benchtop Experiments

Images produced by this method can all be interpreted as axial scans, with true left and true right represented by the left of the image and the right of the image, respectively. The raw data obtained after scanning were plotted as a heatmap for the 30-mL, 40-mL, and 50-mL hemorrhagic models (Fig. 3A–C). The same heatmaps are also shown after filtering and thresholding (Fig. 3D–F). Confirmed placement of each hemorrhage is shown in Fig. 3G–I, with each lesion represented as a red circle. In all benchtop skull experiments (n = 16), it was possible to approximate the location of the hemorrhage with centimeter resolution. The average time spent scanning across the entire head with one sensor was 2.43 minutes, at which point enough data were collected to produce a predictive conductivity heatmap. These results may also be projected onto a 3D head model for better interpretation of the hemorrhage location (Fig. 4).

FIG. 3.
FIG. 3.

Benchtop experimental results using the ECD sensor. Each column represents an individual experimental trial. A–C: Raw data collected from the ECD sensor after moving across each of the 8 scanning paths for a 30-mL (A), 40-mL (B), and 50-mL (C) hematoma. Brighter regions represent higher probability of hematoma. D–F: After filtering and thresholding to remove noise, the regions of highest hematoma probability can be seen clearly. G–I: The actual locations of hematoma placement within the model. Notably, the ECD sensor is able to accurately locate each hematoma (red circle) and create a preliminary image. Made in ©BioRender - biorender.com.

FIG. 4.
FIG. 4.

The filtered prediction heatmaps (Fig. 3D–F) can be shown on a brain for easier interpretation and to identify better lesion relationships to anatomical landmarks. Excellent agreement is seen between predictions of lesion location and actual lesion location (Fig. 3G–I).

Discussion

Preliminary results of our portable ECD sensor for detection of hemorrhagic stroke demonstrate that it is feasible and accurate based on phantom head model simulations. While the technology has been implemented in other industries, this has yet to be applied to stroke detection; however, the technology is safe with a minimal risk profile.14 By varying the diameter, turn number, and length of the coil, it was possible to design several sensors with varying detection depths. While further testing and technological refinements are certainly necessary prior to implementation of this device in the field for direct patient care, this represents the first step toward more expedient hemorrhagic stroke detection that can streamline delivering therapeutic efforts.

ECD sensors represent a novel nonionizing stroke diagnostic technology. Our preliminary study results suggest that ECD sensors may accurately detect ICH rapidly, noninvasively, and with accurate spatial resolution. In addition, the device was portable and compact (11.4-cm maximum diameter). For reference, CT head imaging takes roughly 30 seconds, and MRI for stroke protocol ranges on the order of several minutes or longer. Thus, the time utilized by the ECD scanner for ICH detection (2.43 minutes) is reasonable and can be performed in a field setting long before admission to the emergency room. Theoretically, the ECD sensor may be able to differentiate between ischemic and hemorrhagic stroke because the device operates using conductivity measurements, which are drastically different between normal brain parenchyma (0.2 S/m), hemorrhage/hematoma (0.65 S/m), and ischemia (0.1 S/m).11 However, future randomized controlled trials are necessary to confirm this hypothesis. Nonetheless, ECD sensors demonstrate promise in rapid, portable, and low-cost stroke diagnosis and imaging, and future research is necessary to fully demonstrate the utility of this sensor.

Previous research has described several novel medical devices for noninvasive stroke detection. Near-infrared spectroscopy (NIRS) has emerged as a potential method for detecting hemorrhages within the most superficial 2.5 cm of the head.18 Although NIRS has a moderate cross-study sensitivity of 78%, specificity of 90%, positive predictive value of 77%, and a negative predictive value of 90%, it has gained FDA approval for ICH detection.19 Aside from NIRS, volumetric impedance phase-shift spectroscopy and microwave-based detection methods of ICH have been explored with limited results.20,21 Namely, the spatial resolution of these devices is lower than that of ECD-based stroke detection devices. In addition, the sensors described in this study had an extremely low cost ($100) and represent the only next-generation stroke device, other than portable MRI technology,22,23 that is able to generate near real-time images of hemorrhagic lesions.

The results of our benchtop experimentation bolster the claim that ECD sensors may allow for rapid triage and stroke imaging and provide diagnostic information accurately in acute neurovascular lesions. With continuing investigation regarding the optimal treatment of ICH, diagnostic strategies that reduce the time-to-intervention after cerebrovascular accidents continue to be critical. Although the STICH trials demonstrated no appreciable improvement in outcomes after prompt surgical intervention or best medical management, there have not yet been studies evaluating patient outcomes after rapid diagnosis with next-generation medical devices.6,7 There is no doubt that this lack of data can be partially contributed to the recent development of novel stroke diagnostic technologies and to many device limitations that have yet to be overcome. However, as technology and computational resources continue to advance, rapid diagnostic technologies such as ECD sensors should be evaluated to understand the interplay between diagnostic time and patient outcomes.

With respect to the neurosurgical management and organization of acute stroke care, ECD sensors have the potential to facilitate in-field triage and determine whether a patient should be transported to a hospital with a stroke center and neurosurgical team. Current prehospital triage techniques utilize stroke scales such as the Los Angeles Motor Scale, which is a simple 3-item rapid, reproducible approach, to diagnose severe strokes in the prehospital setting.24–26 However, recent evidence suggests that mobile stroke unit (MSU) intervention, which consists of neurological examination, point-of-care testing, and noncontrast CT, as well as CT angiography for patients with no ICH, may more accurately triage patients with acute stroke.27 Despite the clear benefits of MSUs, several limitations continue to prevent their spread. Namely, the initial setup cost for MSUs ranges from approximately $600,000 to $1,000,000, and annual operational costs range between $950,000 and $1,200,000 for a unit that operates 12 hours per day.28,29 Another limitation is the utility of MSUs in rural settings; studies have shown that individuals residing in rural areas are less likely than those in urban areas to receive acute stroke care and imaging within 24 hours due to limited resources.29–31 The development of next-generation stroke diagnostic tools, such as the ECD sensor, has the potential to provide cheap, rapid, and portable diagnosis and imaging of hemorrhagic stroke in rural and urban prehospital settings and to dramatically affect the workflow of organized stroke care and patient triage. While future advancement of ECD sensors is required, both for hemorrhagic and ischemic stroke subtypes, the present study has demonstrated the feasibility of these sensors for acute hemorrhagic stroke diagnosis. Should ECD sensors be proven efficacious for stroke subtyping, they may facilitate the workflow for stroke management by allowing for prehospital administration of thrombolytics.

Limitations

Several limitations still need to be addressed to establish the efficacy of this sensor. First, a large trial in humans is necessary to demonstrate appropriate hemorrhagic stroke detection and imaging, with associated sensitivity, specificity, and receiver operating characteristic curve metrics. Second, a thorough investigation of sensitivity to hemorrhagic volume is necessary to assess the ability to detect small (1–10 mL) hematomas. Last, further sensor optimization, with regard to signal noise and scanning depth, may allow for additional resolution and increase the accuracy of the device.

Conclusions

Overall, the proof-of-concept benchtop experiments shown in this study establish the feasibility of ECD sensors for hemorrhagic stroke detection. These sensors are rapid, portable, and affordable, making them potentially useful for prehospital triage. Additional multiinstitutional studies are required to fully establish the efficacy and accuracy of these sensors for hemorrhagic stroke diagnosis and to implement this sensor type within the workflow of current organized stroke management.

Acknowledgments

S.S., Y.C.T., A.W.T., N.S., and G.Z. are funded by National Institute of Neurological Disorders and Stroke (NINDS) grant no. R01 NS119596-01.

Disclosures

Dr. Mack reports being a consultant to Integra and having direct stock ownership in Rebound Therapeutics and Cerebrotech.

Author Contributions

Conception and design: Shahrestani. Acquisition of data: Shahrestani. Analysis and interpretation of data: Shahrestani. Drafting the article: Shahrestani. Critically revising the article: Shahrestani, Strickland, Bakhsheshian, Mack, Toga, Sanossian, Tai. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Zada. Statistical analysis: Shahrestani. Administrative/technical/material support: Zada, Tai. Study supervision: Zada, Tai.

References

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    Gorelick PB. The global burden of stroke: persistent and disabling. Lancet Neurol. 2019;18(5):417418.

  • 2

    GBD 2016 Lifetime Risk of Stroke Collaborators; Feigin VL, Nguyen G, Cercy K, et al. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. N Engl J Med. 2018;379(25):24292437.

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    de Oliveira Manoel AL. Surgery for spontaneous intracerebral hemorrhage. Crit Care. 2020;24(1):45.

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    Qureshi AI, Palesch YY, Barsan WG, et al. Intensive blood-pressure lowering in patients with acute cerebral hemorrhage. N Engl J Med. 2016;375(11):10331043.

    • Search Google Scholar
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    Mayer SA, Brun NC, Begtrup K, et al. Efficacy and safety of recombinant activated factor VII for acute intracerebral hemorrhage. N Engl J Med. 2008;358(20):21272137.

    • Search Google Scholar
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    Mendelow AD, Gregson BA, Fernandes HM, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial intracerebral haematomas in the International Surgical Trial in Intracerebral Haemorrhage (STICH): a randomised trial. Lancet. 2005;365(9457):387397.

    • Search Google Scholar
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    Gabriel C, Peyman A, Grant EH. Electrical conductivity of tissue at frequencies below 1 MHz. Phys Med Biol. 2009;54(16):48634878.

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Schematic representing the concept of “Time is Brain," highlighting that timely intervention of large-vessel occlusion can lead to a complete neurological recovery. Copyright Sandeep Kandregula (artist). Published with permission. See the article by Kandregula et al. (E4).

Contributor Notes

Correspondence Gabriel Zada: Keck School of Medicine, University of Southern California, Los Angeles, CA. gabriel.zada@med.usc.edu.

INCLUDE WHEN CITING DOI: 10.3171/2021.4.FOCUS21121.

Disclosures Dr. Mack reports being a consultant to Integra and having direct stock ownership in Rebound Therapeutics and Cerebrotech.

  • View in gallery

    ECD sensor components and construction. Nonconductive cylindrical plastic scaffolds (1), copper wire wound as a solenoid around each plastic scaffold creating the sensor coil (2), LDC 1101 inductance-to-digital converting chips connected to each coil (3), USB connection from the chip to a local computer (4), and a laptop for signal visualization, storage, and analysis (5). Made in ©BioRender - biorender.com.

  • View in gallery

    Skull model and sensor placement for scanning. Upper: Eight equidistant rows were created on the phantom head model. The sensor was tangentially rotated across each row, collecting Rp data that reflected the brain’s conductivity at that point. Lower: Regions of hemorrhage have higher conductivity, and the ECD sensor can identify this conductivity change through the skull.

  • View in gallery

    Benchtop experimental results using the ECD sensor. Each column represents an individual experimental trial. A–C: Raw data collected from the ECD sensor after moving across each of the 8 scanning paths for a 30-mL (A), 40-mL (B), and 50-mL (C) hematoma. Brighter regions represent higher probability of hematoma. D–F: After filtering and thresholding to remove noise, the regions of highest hematoma probability can be seen clearly. G–I: The actual locations of hematoma placement within the model. Notably, the ECD sensor is able to accurately locate each hematoma (red circle) and create a preliminary image. Made in ©BioRender - biorender.com.

  • View in gallery

    The filtered prediction heatmaps (Fig. 3D–F) can be shown on a brain for easier interpretation and to identify better lesion relationships to anatomical landmarks. Excellent agreement is seen between predictions of lesion location and actual lesion location (Fig. 3G–I).

  • 1

    Gorelick PB. The global burden of stroke: persistent and disabling. Lancet Neurol. 2019;18(5):417418.

  • 2

    GBD 2016 Lifetime Risk of Stroke Collaborators; Feigin VL, Nguyen G, Cercy K, et al. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. N Engl J Med. 2018;379(25):24292437.

    • Search Google Scholar
    • Export Citation
  • 3

    de Oliveira Manoel AL. Surgery for spontaneous intracerebral hemorrhage. Crit Care. 2020;24(1):45.

  • 4

    Qureshi AI, Palesch YY, Barsan WG, et al. Intensive blood-pressure lowering in patients with acute cerebral hemorrhage. N Engl J Med. 2016;375(11):10331043.

    • Search Google Scholar
    • Export Citation
  • 5

    Mayer SA, Brun NC, Begtrup K, et al. Efficacy and safety of recombinant activated factor VII for acute intracerebral hemorrhage. N Engl J Med. 2008;358(20):21272137.

    • Search Google Scholar
    • Export Citation
  • 6

    Mendelow AD, Gregson BA, Fernandes HM, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial intracerebral haematomas in the International Surgical Trial in Intracerebral Haemorrhage (STICH): a randomised trial. Lancet. 2005;365(9457):387397.

    • Search Google Scholar
    • Export Citation
  • 7

    Mendelow AD, Gregson BA, Rowan EN, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial lobar intracerebral haematomas (STICH II): a randomised trial. Lancet. 2013;382(9890):397408.

    • Search Google Scholar
    • Export Citation
  • 8

    Audebert HJ, Saver JL, Starkman S, et al. Prehospital stroke care: new prospects for treatment and clinical research. Neurology. 2013;81(5):501508.

    • Search Google Scholar
    • Export Citation
  • 9

    Kelly AG, Hellkamp AS, Olson D, et al. Predictors of rapid brain imaging in acute stroke: analysis of the Get With the Guidelines-Stroke program. Stroke. 2012;43(5):12791284.

    • Search Google Scholar
    • Export Citation
  • 10

    Jauch EC, Saver JL, Adams HP Jr, et al. Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2013;44(3):870947.

    • Search Google Scholar
    • Export Citation
  • 11

    Gabriel C, Peyman A, Grant EH. Electrical conductivity of tissue at frequencies below 1 MHz. Phys Med Biol. 2009;54(16):48634878.

  • 12

    Frank RT. A note on the electric conductivity of blood during coagulation. Am J Physiol. 1905;14(5):466468.

  • 13

    García-Martín J, Gómez-Gil J, Vázquez-Sánchez E. Non-destructive techniques based on eddy current testing. Sensors (Basel). 2011;11(3):25252565.

    • Search Google Scholar
    • Export Citation
  • 14

    Ditchburn RJ, Burke SK, Posada M. Eddy-current nondestructive inspection with thin spiral coils: long cracks in steel. J Nondestr Eval. 2003;22(2):6377.

    • Search Google Scholar
    • Export Citation
  • 15

    Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55(2):187199.

  • 16

    Texas Instruments. Optimizing L Measurement Resolution for the LDC161x and LDC1101. Published February 2016. Accessed May 10, 2021. http://www.ti.com/lit/an/snoa944/snoa944.pdf

    • Search Google Scholar
    • Export Citation
  • 17

    Kandadai MA, Raymond JL, Shaw GJ. Comparison of electrical conductivities of various brain phantom gels: developing a ‘Brain Gel Model’. Mater Sci Eng C. 2012;32(8):26642667.

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