Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2023)

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.

    Cover page: Sensor Validation and Digital Biomarker Exploration for Health Monitoring
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2021)
    The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann-Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60-100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.
      Cover page: Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults.
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2023)
      Associations between cerebrovascular disease and impaired autonomic function and cerebrovascular reactivity have led to increased interest in variability of heart rate (HRV) and blood pressure (BPV) following stroke. In this study, beat-to-beat pulse rate variability (PRV) and BPV were measured in clinically stable stroke patients (6 ischemic, 2 hemorrhagic) at least one year after their last cerebrovascular event. Beat-to-beat blood pressure (BP) measurements were collected from subjects while resting in the sitting position for one hour. Compared with healthy controls, stroke patients exhibited significantly greater time-domain (standard deviation, coefficient of variation, average real variability) and normalized high-frequency BPV (all p < 0.05). Stroke patients also exhibited lower LF:HF ratios than control subjects (p = 0.003). No significant differences were observed in PRV between the two groups, suggesting that BPV may be a more sensitive biomarker of cerebrovascular function in long-term post-stroke patients. Given a paucity of existing literature investigating beat-to-beat BPV in clinically stable post-stroke patients long (> 1 year) after their cerebrovascular events, this pilot study can help inform future studies investigating the mechanisms and effects of BPV in stroke. Elucidating this physiology may facilitate long-term patient monitoring and pharmacological management to mitigate the risk for recurrent stroke.
        Cover page: Changes in beat-to-beat blood pressure and pulse rate variability following stroke.
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2021)
        We test a new wireless soft capacitance sensor (CAP) based on applanation tonometry at the radial and dorsalis pedis arteries against the gold standard, invasive arterial line (A-Line), for continuous beat to beat blood pressure (BP) measurements in the Operating Room during surgical procedures under anesthesia in 17 subjects with the mean age and body mass index (BMI) of 57. 35 ± 18.72 years and 27.36 ± 4.20 kg/m2, respectively. We have identified several parameters to monitor in order to compare how well the CAP sensor tracks the entire hemodynamic waveform as compared to the A-Line. This includes waveform similarity, heart rate (HR), absolute systolic BP (SBP), diastolic BP (DBP), and temporal response to a vasopressor. Overall, the CAP sensor shows good correlations with A-Line with respect to hemodynamic shape (r > 0.89), HR (mean bias = 0.0006; SD = 0.17), absolute SBP, and DBP in a line of best fit (slope = 0.98 in SBP; 1.08 in DBP) and the mean bias derived from Bland-Altman method to be 1.92 (SD = 12.55) in SBP and 2.38 (SD = 12.19) in DBP across body habitus and age in OR patients under general anesthesia. While we do observe drifts in the system, we still obtain decent correlations with respect to the A-Line as evidenced by excellent linear fit and low mean bias across patients. When we post-process using a different calibration method to account for the drift, the mean bias and SD improve dramatically to -1.85 and 7.19 DBP as well as 1.43 and 7.43 SBP, respectively, indicating a promising potential for improvement when we integrate strategies to account for movement identified by our integrated accelerometer data.
          Cover page: Clinical Validation of a Soft Wireless Continuous Blood Pressure Sensor During Surgery
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2022)
          Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach's superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.
            Cover page: Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring
            Top