Doctor of Philosophy (Ph.D.)
Degree Granting Department
Biochemistry and Molcecular Biology
Robert Frisina, Ph.D.
Mark Jaroszeski, Ph.D.
William Lee, Ph.D.
Stephanie Carey, Ph.D.
Farhan Siddiqi, M.D.
ECG, Poincaré plot, QRS Complex, R-R interval, Stress detection
The motivation for this research is the high prevalence experienced by divers of carbon dioxide (CO2) issues and concerns throughout their lives. Additionally, CO2 is naturally occurring, the product of oxidative metabolism, and is undetectable to human senses. Current CO2 sensor or detection technology is limited to high-cost machines such as arterial blood gas monitors and end tidal CO2 monitors, or more modern technology relying upon bright lights which measure blood flow. The light technology is notoriously unreliable and does not produce a very effective heart beat allowing a user to detect minimal changes. Heart Rate Variability (HRV) is the change (or variability) in the timing between each heartbeat. There are few previous studies on this subject, but new research is being done on HRV now, and professional athletes are training using HRV as a performance and health indicator. Initial indications show that HRV may prove quite useful in detecting stress on the autonomic nervous system which is affected by CO2.
The experimental methods we used were the following. The study population included 15 males between the ages of 18 and 50 years old and excluded those with pulmonary concerns, as identified by a physician, because of HRV confounding factors. This University of South Florida IRB approved study had minimal risk and noted numerous off ramps or stopping points to ensure subject safety. Each subject was normalized with respect to the known drivers for HRV and then tested breathing four different breathing mixtures. Subjects breathed air as well as air containing 4% CO2, 5% CO2 and 6% CO2 with air breaks in between each CO2 exposure to ensure no buildup of CO2. After being informed by the initial results of this clinical research study, a stand- alone device with an HRV sensing mechanism using a 3 lead ECG and proprietary software designed by the authors to manipulate the data collected and present it in a novel methodology was developed at the system level. The ultimate goal was to detect CO2 prior to the onset of hypercapnic symptoms. This novel system, including the analysis algorithm, relies upon comparing successive heart rates and plotting them in an ever-shifting time domain axis which creates a return plot map. Formulating an ellipse encompassing the majority of the data and detecting a change in eccentricity of that ellipse when a subject is breathing a different gas is the novel nature of the system.
Experimental results can be summarized as follows. The null hypothesis was that the mean of the ratio of the standard deviation of the long axis of the ellipse as compared to the standard deviation of the short axis of the ellipse would be the same for both the air breathing data set and the worst case of the CO2 breathing data set. A standard t score with a one tail t test was calculated. The t-value is 13.91. On a student’s T distribution table with a 99.95% confidence interval and 28 degrees of freedom, the t-value would have to be greater than 3.6 to be significant. The p-value is < 0.00001. The null hypothesis was rejected. The results of the study were statistically very significant. The study clearly demonstrates a dramatic change in the plot given an identical time period for air breathing and CO2 rich air being breathed. The minimum change (or difference) in the ratio when breathing CO2 to breathing air was a difference of 1.32 which represents a 34% change in ratio. The mean change was 1.93 (48.4%) with a 0.30 (5.9%) standard deviation.
In conclusion, while there are some known concerns and issues to overcome, the results are very promising for this pioneering study. This initial cohort study demonstrates that HRV monitoring is reproducible in our study group, did show a statistical change in response to elevated inspired CO2, and correlates with CO2 monitoring by other means. This type of HRV monitoring can be easily added via software upgrades to current ECG monitoring machines or manufactured as a stand- alone ECG/HRV monitoring device. This algorithm reduces the need for additional, more expensive CO2 monitoring devices and arterial blood gas analysis. We further propose that HRV analysis can potentially provide an early warning system to detect hypercapnia related symptomatology prior to escalation of symptoms to a detrimental level and perhaps damaging or irreversible state.
Scholar Commons Citation
Dituri, Joseph, "Heart Rate Variability Analysis as a Means of Real-time Hypercapnia Detection" (2018). Graduate Theses and Dissertations.