Browsing by Author "Fan, Yingying"

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    Integrated Microheater Array for Localized Heating of Magnetic Nanoparticles
    (2022-12-01) Fan, Yingying; Chi, Taiyun
    There is a growing interest in leveraging microwave technology for biomedical applications including non-invasive imaging, respiratory and heartbeat radar detection, and spectroscopy. While most of these applications are sensing-oriented, my research focuses on microwave actuation, particularly heat generation. It enables a novel non-invasive approach for applications such as tumor ablation and neural stimulations. A variety of techniques have been employed to raise the local temperature for these emerging biomedical applications including dielectric heating and ohmic heating. However, they suffer from poor material specificity and undesired damage to the surrounding tissues. Our key idea is to explore magnetic heating induced by magnetic nanoparticles (MNP) which offers superior heating specificity. We proposed and demonstrated a miniaturized microheater array utilizing ferromagnetic resonance of the MNP at GHz to enable localized yet accurate temperature manipulation in living cells and tissues.
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    Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries
    (IOP Publishing, 2021) Schmid, William; Fan, Yingying; Chi, Taiyun; Golanov, Eugene; Regnier-Golanov, Angelique S.; Austerman, Ryan J.; Podell, Kenneth; Cherukuri, Paul; Bentley, Timothy; Steele, Christopher T.; Schodrof, Sarah; Aazhang, Behnaam; Britz, Gavin W.; Neuroengineering Initiative (NEI)
    Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.