Date Thesis Awarded


Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)




John Delos

Committee Members

Henry Krakauer

Christopher Del Negro


Apnea is common in premature infants, and in severe cases it may impair development. Data recorded during apnea events by hospital monitors at the University of Virginia Neonatal Intensive Care Unit (NICU) include EKG, chest impedance, and pulse oximetry signals. In previous work, an apnea detection algorithm was developed that filtered the cardiac artifact from the chest impedance signal to improve detection of apneas [1]. An unexpected result was the discovery that Very Long Apneas (VLAs) lasting more than 60 seconds are not rare. We use this findings in our research to provide new information about these apneas and to test a model describing the rate of decrease of blood oxygen in apneas of various lengths.

We study 86 very long apneas, along with 285 shorter apneas (10 - 40 s duration), to analyze the properties of VLAs. We begin with a quantitative measure of the oxygen deficit or the heartbeat deficit resulting from the apnea, concluding that both are roughly proportional to the duration of the apnea.

We observe that heart rate and oxygen saturation decrease much more slowly in a VLA than in a short apnea, and the initial oxygen saturation prior to VLAs is unusually high. This raises the question of whether babies are hyperventilating before a VLA. To answer this, we have analyzed respiration rates preceding apneas of various durations, and have shown that VLAs are associated with a significantly increased respiration rate immediately prior to the apnea.

Lastly, we have used the theory provided by [2] to model the rate of decrease in oxygen saturation during individual apnea events. The resulting model confirms our observation that higher initial levels of oxygen saturation result in slower rates of decrease.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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