BENJAMIN SANCHEZ TERRONES portrait
  • Assistant Professor, Elect & Computer Engineering
  • Member of the Experimental Therapeutics Program, Huntsman Cancer Institute
801-585-1494

Publications

  • Pandeya S & Sanchez B (2023). Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice. Muscle & nerve. Vol. 68, 781788781-788. Published, 10/01/2023.
  • Sanchez B & Freedman R (2023). Reply to the Editor- Safety of wearable bioimpedance monitors for CIED patients remains unknown. (pp. 134113421341-1342). Vol. 20. Heart rhythm. Published, 09/01/2023.
  • Ha GB & Steinberg BA (2023). Safety evaluation of smart scales, smart watches, and smart rings with bioimpedance technology shows evidence of potential interference in cardiac implantable electronic devices. Heart rhythm. Vol. 20, 561571561-571. Published, 04/01/2023.
  • Luo X (2023). Modeling and simulation of needle electrical impedance myography in nonhomogeneous isotropic skeletal muscle. IEEE journal of electromagnetics, RF and microwaves in medicine and biology. Vol. 6, 103110103-110. Published, 03/01/2023.
  • Gia-Bao Ha (2023). afety evaluation of smart scales, smart watches, and smart rings with bioimpedance technology show evidence of potential interference in cardiac implantable electronic devices. Heart Rhythm. Published, 02/22/2023.
  • Elaine Wong (2023). Electrical impedance dermography differentiates squamous cell carcinoma in situ from inflamed seborrheic keratoses. Journal of Investigative Dermatology Innovations. Published, 02/22/2023.
  • Luis M Vela (2023). IoMT-enabled stress monitoring in a virtual reality environment and at home. IEEE Internet of Things Journal. Published, 01/25/2023.
  • Crandall H (2022). Characterization of the Analog Device Inc (ADI) MAX30009 Bioimpedance Analog Front End Chip. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. Vol. 2022, 250225052502-2505. Published, 09/01/2022.
  • Pandeya SR, Nagy JA, Riveros D & Semple C (2022). Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography. Muscle & nerve. Vol. 66, 354361354-361. Published, 08/01/2022.
  • Pandeya S, Nagy JA, Riveros D, Semple C, Taylor RS, Sanchez B, Rutkove S. Relationships between in vivo surface and ex vivo electrical impedance myography measurements in three different neuromuscular disorder mouse models. PLoS ONE, 2021, 16(10): e0259071. Published, 11/11/2021.
  • Luo X, Wang S, Rutkove SB and Sanchez B. Nonhomogeneous volume conduction effects affecting needle electromyography: an analytical and simulation study. Physiol. Meas. 2021, in press. Published, 11/09/2021.
  • Luo X, Zhou Y, Smart T, Grossman D and Sanchez B. Electrical characterization of basal cell carcinoma using a novel handheld electrical impedance spectroscopy device. Journal of Investigative Dermatology Innovations, 2021, in press. Published, 11/04/2021.
  • Andreasen N, Crandall H, Brimhall O, Miller B, Perez-Tamayo J, Martinsen O, Kauwe SK, and Sanchez B. Machine learning-based diagnosis of breast cancer and evaluation of therapy effect measuring skin electrical resistance in lymphatic regions. IEEE Access, 2021, in press. Published, 11/03/2021.
  • Luo X, Gutierrez Pulido HV, Rutkove SB and Sanchez B. A bioimpedance- based device to assess the volume conduction properties of the tongue in neurological disorders affecting bulbar function. IEEE Open J Eng. Med. Biol., 2021, 2, 278-28. Published, 10/27/2021.
  • Luo X, Wang S and Sanchez B. Modeling and simulation of needle electrical impedance myography in nonhomogeneous isotropic skeletal muscle. IEEE J Electromagnetics, RF Micro. Med. Biol., 2021, in press. Published, 09/23/2021.
  • Sanchez B. Reply to Putting the patient first: The validity and value of surface-based electrical impedance myography techniques. Clin. Neur., 2021, 132 (7), 1752-1753. Published, 08/25/2021.
  • Luo X, Wang S and Sanchez B. A framework for modeling bioimpedance measurements of nonhomogeneous tissues: a theoretical and simulation study. Physiol. Meas. 2021, 42 (5), 055007. Published, 08/11/2021.
  • Pandeya SR, Nagy JA, Riveros D, Semple C, Taylor RS, Mortreux M, Sanchez B, Kapur K, Rutkove SB. Estimating myofiber cross-sectional area and connective tissue deposition with electrical impedance myography: A study in D2-mdx mice. Muscle Nerve, 2021, 63 (6), 941-950. Published, 07/29/2021.
  • Luo X and Sanchez B. In silico muscle conduction study validates in vivo measurement of tongue volume conduction properties using the UTA depressor. Physiol. Meas. 2021, 42 (4), 045009. Published, 07/27/2021.
  • Pandeya SR, Nagy JA, Riveros D, Semple C, Taylor RS, Mortreux M, Sanchez B, Kapur K, Rutkove SB. Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: a study in db/db mice. Muscle Nerve, 2021, 63 (1), 127-140. Published, 05/12/2021.
  • Martinez de Morentin Cardoner M, Kwon H, Gutierrez Pulido HV, Nagy JA, Rutkove SB and Sanchez B. Modeling and reproducibility of twin concentric electrical impedance myography. IEEE Trans. Biomed. Eng., 2021,Oct;68(10):3068-3077. Published, 04/28/2021.
  • Sanchez B, Martinsen O, Freeborn TJ, Furse CM. Electrical impedance myo- graphy: a critical review and outlook. Clin. Neur., 2021, 132 (2), 338-344. Published, 03/24/2021.
  • Luo X, Gutierrez-Pulido HV, Rutkove SB, Sanchez B. In vivo muscle conduction study of the tongue using a multi-electrode tongue depressor. Clin. Neur., 2021, 132 (2), 683-687. Published, 02/16/2021.
  • Zhang F, Teng Z, Yang Y, Zhong H, Li J, Rutkove S and Sanchez B. A novel method for estimating the fractional Cole impedance model using single-frequency dc-biased sinusoidal excitation. Circuits, Systems, and Signal Processing, 2021, 40, 543-558. Published, 01/29/2021.
  • Zhang F, Sanchez B, et al. Numerical estimation of Fricke-Morse impedance model parameters using single-frequency sinusoidal excitation. Physiol. Meas., 2019, 40, 09NT01. Published, 11/27/2019.

Research Statement

My broad research interests primarily focus on developing novel medical technologies that will enable clinicians to better detect, diagnose, stage, treat, and monitor patients. More narrowly, the technologies I am interested in developing take advantage of the inherent electrical and mechanical properties contrast between different tissue types and pathologies. I am interested in both translatable research (getting these new technologies into the hands of clinicians) and the more basic science and engineering aspects of better understanding the biology influencing a tissue’s electrical behavior, and developing techniques to accurately gauge these properties. I am actively applying these technologies to a variety of clinical conditions. I describe my research efforts as three separate activities that are, by design, coupled in order to enhance development of bench-to-bedside technologies. First, I am actively exploring and quantifying the available electrical property contrast in diseased tissue and developing theoretical approaches and testing hypotheses that explain the underlying biophysical mechanisms responsible for this contrast. Second, I am actively developing bioinstrumentation able to accurately gauge and image these properties. Third, I am leveraging 1) the knowledge gained from understanding contrast levels and mechanisms and 2) the instrumentation designed and constructed to integrate these technologies into the clinical environment. These three primary areas of research all feed into my current overarching goal of developing clinical applications for bioelectromagnetics.

 

Research Keywords

  • Wearable biosensing
  • Physiological monitoring
  • Health diagnostics
  • Electrical Bioimpedance
  • Computational electrostatics
  • Biomedical devices