V četrtek, 17. 9. 2020, je Albert Zorko uspešno zagovarjal doktorsko disertacijo z naslovom Modelling human cardiorespiratory system through heart-rate variability / Modeliranje človeškega kardio-respiratornega sistema s pomočjo spremenljivosti srčnega utripa (HRV).

Iskreno čestitamo!

Komisijo za zagovor so sestavljali:

  • Izr. prof. dr. Borut Lužar (predsednik komisije),
  • Izr. prof. dr. Zoran Levnajić (mentor),
  • Prof. dr. Maximilian Moser (somentor) in
  • Izr. prof. dr. Biljana Mileva Boshkoska (članica komisije).

Zagovor je potekal na fakulteti, dva člana komisije pa sta bila prisotna preko Skypa. 

Povzetek doktorske disertacije:

The modern computer resources and the data analysis methods allow for a biomedical data to be examined in a more detail than ever. The heart rate variability (HRV) is an easily accessible vital signal that offers a range of useful information about the person under a study. One such application regards an automatical determining whether a person is awake or asleep from the HRV data only. This is of an importance not just for  medical but also for  practical applications, such as a traffic safety or  smart homes. 

In this doctoral work we study the HRV data of  75 healthy individuals of a varying age and  sex, recorded with a microsecond precision. We employ the empirical fact that  heart and respiration cycles couples differently during a sleep and awake period. Namely, a respiratory modulation of a heart rhythm or a respiratory sinus arrhythmia (RSA) is more pronounced while asleep, as both sleep and RSA are connected to a strong vagal activity. Therefore, the onset of sleep can be recognized or perhaps even predicted by a carefully examining the cardio-respiratory coupling. We show that the above can indeed be used, at least in principle, to design an algorithmic method to automatically determine the state of a person’s consciousness from the HRV data only. 

On the methodological front we rely on quantifying the (self)similarity among the shapelets, the short chunks of the HRV time series, that allow for a consistent comparison among and within the time series. To establish a better benchmark, we also carry out a comprehensive analysis of the overall HRV dynamics depending on age and sex.

Results include: (i) that a distinctive patterns of the HRV dynamics are consistent across  age and sex, (ii) that they are not only an indicative of sleep and awake, but allow to pinpoint the change from awake to sleep and vice versa almost immediately, (iii) that the shapelet analysis is a viable tool to examine these data with  a great precision. We conclude that a more systematic analysis involving more subjects could lead to a practical method for the prediction of the onset of sleep.

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