UC Irvine

We live in an era where advanced technology is enhancing the quality of life. With the assistance of new medical devices and activity trackers, our knowledge and awareness of our own health and disease state have grown rapidly. However, existing products still suffer from severe limitations. For instance, hemodynamic monitoring is essential for specific populations with conditions, as hypotension and hypertension may impair vital organ function. Continuous monitoring provides more information about how blood pressure (BP) and heart rate (HR) fluctuate, but current sphygmomanometers provide only static measurements. While current medical-grade continuous BP monitors exist, they are limited to critically ill patients due to their bulkiness and price. On the other hand, activity-tracking smartwatches are either inaccurate or intermittent. We have been developing soft conformal pressure sensors that are lightweight, inexpensive, and comfortable to wear. The aims of this work include validating the performance of the sensor in tracking beat-to-beat BP and exploring how the data can be understood in various clinical settings. The pressure sensors were compared against both invasive and FDA-cleared noninvasive BP devices. In addition to the beat-to-beat absolute systolic and diastolic BP, the waveform shape analysis, HR, HR variability (HRV), and temporal response to vasopressors show promising potential. Yet, how the data is interpreted is challenging even with accurate recordings from devices. For this reason, we explore continuous physiological data to determine its predictive capabilities in well-controlled clinical settings.

One promising digital biomarker that has been widely studied for the past several decades is HRV. More and more methods of evaluating HRV have been proposed, yet its prognostic potential remains unclear. Hence, our first study covers the fundamentals of HRV analysis, including investigating the proper minimum data length of each common HRV measure in young healthy subjects. Next, we explore clinical applications via an ongoing sleep study and an electroacupuncture (EA) study with our clinical collaborators. The apnea-hypopnea index (AHI) is commonly used to diagnose sleep apnea in clinical practice. A counterargument is that AHI cannot holistically represent the disorder without considering the full picture of physiological characteristics such as the durations and depths of oxygen desaturation episodes. To discover new indicators for the severity level of sleep apnea, we demonstrate that the nasal airflow signal alone could predict arousal using a deep learning method with an accuracy of 85%. For the EA clinical study, we seek to understand how this therapy regulates BP among hypertensive subjects. Throughout 8 weeks of EA at cardiovascular-specific acupoints, we assessed changes in both HRV and BPV.