Wearable technology offers the promise of decentralized and personalized healthcare, which can both alleviate current burdens on medical resources, and also help individuals to be more informed about their health. The heterogeneity of disease phenotypes necessitates adaptations to both diagnosing and surveilling disease, but to ensure user adoption and behavioral change, there needs to be a convenient way to amass such health information continuously. This can be in part accomplished by the development of continuously monitoring, compact wearable medical sensors and analytics technology that provide updates on analyte and biosignal measurements at regular intervals in situ. This dissertation investigates methods for collecting and analyzing information from wearable devices with these principles in mind.
In Aim 1, we developed new methods for analysis of cardiovascular biosignals. Current methods of estimating left ventricular mass index (LVMI, a strong risk factor for cardiac outcomes), rely on the analysis of echocardiographic signals. Though still the gold standard, echocardiography can typically only be performed in the clinic, making it inconvenient to obtain frequent measurements of LVMI. Frequent measurements can be useful for monitoring cardiac risk, particularly for high-risk individuals, so we investigated the feasibility of predicting LVMI using a deep learning-based approach through ambulatory blood pressure readings, a one-time laboratory test and demographic information. We find that adding blood pressure waveform information in conjunction with multitask learning improved prediction errors (compared to baseline linear regression and neural network models), pointing to its potential as a clinical tool. Using transfer learning, we developed a model that does not require waveform data, but achieved similar prediction accuracies as methods that do require such data – opening the door to use cases that eliminate the need for wearing a blood pressure cuff continuously during the measurement period. Overall, such a technique has the potential to provide information to individuals who are at high risk of cardiac outcomes both inside and outside the clinic.
In Aims 2 and 3, we developed a minimally invasive hydrogel patch for continuous monitoring of calcium, as proof-of-concept for wearable measurement of a wide variety of analytes typically assayed in the lab – a technology that can facilitate treatment and management of many prevalent diseases. Specifically, in Aim 2, we engineered a DNA polyacrylamide hydrogel microneedle array that sensed physiologically relevant calcium levels, for potential use by individuals who have hypoparathyroidism, a condition in which blood calcium levels are low and calcium supplements are needed. A negative mold was made using a CNC mill, the positive mold was cast in silicone, and the aptamer along with acrylamide and bis-acrylamide was seeded into the silicone mold. The DNA hydrogel was then fabricated using a simple UV curing protocol. The optimized DNA hydrogel was specific to calcium, used simple fabrication methods and had a fast, reversible signal response.
Finally, in Aim 3, we developed the DNA hydrogel sensor into a wearable, integrated system with real-time fluorescence monitoring for testing in vivo. The microneedle array needed to be hydrated for the DNA aptamer to function, but polyacrylamide was too weak in its hydrated state to effectively pierce through skin epidermis. We demonstrated a method for strengthening our hydrogel system with polyethylene glycol diacrylate (PEGDA), while maintaining an optically translucent gel for detection purposes. We conducted piercing studies with a skin phantom on different microneedle array sizes and shapes, and determined that a 3x3 array of beveled microneedles required the least amount of force to pierce through a skin phantom. A custom complementary metal-oxide semiconductor (CMOS) system was developed to capture real-time fluorescence signals from the microneedle array, which correlated to calcium levels in vitro. This setup was then validated in a rat study.
In this dissertation, we demonstrated methods for monitoring human biosignals using signal processing techniques, material innovations and integrated sensing platforms. While a work in progress, this dissertation is a step towards realizing the goal of decentralized, connected health for earlier detection and better management of disease.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/8wgq-qf56 |
| Date | January 2024 |
| Creators | Behnam, Vira |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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