Modelling human cardiorespiratory system through heart-rate variability

doctoral dissertation

Albert Zorko (Author), Zoran Levnajić (Mentor), Maximilian Moser (Co-mentor)

Abstract

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. Our 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.

Keywords

shapelet;algorithm;ECG;Holter;sample entropy;signal to noise ratio;classification;

Data

Language:	English
Year of publishing:	2020
Typology:	2.08 - Doctoral Dissertation
Organization:	FIŠ - Faculty of Information Studies
Publisher:	[A. Zorko]
UDC:	616.12-008.3:616.12-073.7(043.3)
COBISS:	29947651
Views:	2935
Downloads:	226
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary abstract:	Sodobni računalniški viri in metode analize podatkov omogočajo, da se biomedicinski podatki podrobneje preučijo kot kadarkoli prej. Spremenljivost srčnega utripa (HRV) je lahko dostopen vitalni signal, ki ponuja vrsto koristnih informacij o preiskovani osebi. Ena takih vlog se nanaša na samodejno določitev, ali je oseba budna ali spi samo z upoštevanjem podatkov o HRV. To je pomembno ne le za medicinsko, temveč tudi za praktično uporabo, na primer prometno varnost ali pametne domove. V tej doktorski disertaciji preučujemo HRV podatke 75 zdravih posameznikov različne starosti in spola, posnete z mikrosekundno natančnostjo. Uporabljamo empirično dejstvo, da sta med spanjem in budnim ciklom srčni utrip in dihanje različno sklopljena. Respiratorna modulacija srčnega ritma ali respiratorna sinusna aritmija (RSA) je namreč med spanjem bolj izrazita, saj sta tako spanec kot RSA povezana z močno vagalno aktivnostjo. Zato je mogoče začetek spanja prepoznati ali morda celo napovedati s skrbnim pregledom kardio-respiratorne sklopljenosti. Pokazali smo, da lahko zgoraj navedeno vsaj načelno uporabimo za načrtovanje algoritemske metode, za samodejno določitev stanja človekove zavesti samo iz podatkov o HRV. Na metodološkem področju se zanašamo na kvantifikacijo (samo)podobnosti med shapeleti, kratkimi delčki časovnih vrst HRV, ki omogočajo dosledno primerjavo med časovnimi vrstami in znotraj nje. Za zagotovitev bolj kvalitetnih rezultatov, smo izvedli obsežno analizo celotne dinamike HRV glede na starost in spol. Naši rezultati vključujejo ugotovitve: (i) da so značilni vzorci dinamike HRV konsistentni s starostjo in spolom, (ii) da ne zaznavamo samo spanja in budnosti, ampak je možno skoraj trenutno zaznavanje spremembe od budnosti do spanja in obratno, iii) da je analiza metode shapeletov uspešno orodje za natančno preučevanje podatkov. Zaključujemo, da bi lahko bolj sistematična analiza, ki vključuje več preiskovancev, pripeljala do praktične metode za napovedovanje začetka spanja.
Secondary keywords:	shapelet;algoritem;EKG;Holter;vzorčna entropija;razmerje signal-šum;klasifikacija;spremenljivost srčnega utripa;Kardiologija;Disertacije;
Type (COBISS):	Doctoral dissertation
Thesis comment:	Fakulteta za informacijske študije v Novem mestu
Pages:	XXI, 177 str.
ID:	12029381