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Estimation of regularity and synchronism in parallel biomedical time series

Процена регуларности и синхронизма у паралелним биомедицинским временским низовима

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Author
Mohamoud, Omer
Mentor
Bajić, Dragana
Committee members
Trpovski, Željen
Japundžić-Žigon, Nina
Šenk, Vojin
Bojović, Živko
Bajić, Dragana
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Abstract
Objectives: Self-monitoring in health applications has already been recognized as a part of the mobile crowdsensing concept, where subjects, equipped with adequate sensors, share and extract information for personal or common benefit. Limited data transmission resources force a local analysis at wearable devices, but it is incompatible with analytical tools that require stationary and artifact-free data. The key objective of this thesis is to explain a computationally efficient binarized cross-approximate entropy, (X)BinEn, for blind cardiovascular signal processing in environments where energy and processor resources are limited. Methods: The proposed method is a descendant of cross-approximate entropy ((X)ApEn). It operates over binary differentially encoded data series, split into m-sized binary vectors. Hamming distance is used as a distance measure, while a search for similarities is performed over the vector sets, instead of over the individual vectors. The procedure is tested in... laboratory rats exposed to shaker and restraint stress and compared to the existing (X)ApEn results. Results: The number of processor operations is reduced. (X)BinEn captures entropy changes similarly to (X)ApEn. The coding coarseness has an adverse effect of reduced sensitivity, but it attenuates parameter inconsistency and binary bias. A special case of (X)BinEn is equivalent to Shannon entropy. A binary conditional m=1 entropy is embedded into the procedure and can serve as a complementary dynamic measure. Conclusion: (X)BinEn can be applied to a single time series as auto-entropy or, more generally, to a pair of time series, as cross-entropy. It is intended for mobile, battery operated self-attached sensing devices with limited power and processor resources.

Cilj: Snimanje sopstvenih zdravstveih prametara je postalo deo koncepta mobilnog ‘crowdsensing-a’ prema kojem učesnici sa nakačenim senzorima skupljaju i dele informacije, na ličnu ili opštu dobrobit. Međutim, ograničenja u prenosu podataka dovela su do koncepta lokalne obrade (na licu mesta). To je pak nespojivo sa uobičajenim metodama za koje je potrebno da podaci koji se obrađuju budu stacionarni i bez artefakata. Ključni deo ove teze je opis procesorski nezahtevne binarizovane unakrsne aproksimativne entropije (X)BinEn koja omogućava analizu kardiovaskularnih podataka bez prethodne predobrade, u uslovima ograničenog napajanja i procesorskih resursa. Metoda: (X)BinEn je nastao razradom postojećeg postupka unakrsne entropije ((X)ApEn). Definisan je nad binarnim diferencijalno kodovanim vremenskim nizovima, razdeljenim u binarne vektore dužine m. Za procenu razmaka između vektora koristi se Hemingovo rastojanje, a sličnost vektora se ne procenjuje između svakog vektora pojedinačno, ve...ć između skupova vektora. Procedura je testirana nad laboratorijskim pacovima izloženim različitim vrstova stresova i upoređena sa postojećim rezultatima. Rezultati: Broj potrebnih procesorskih operacija je značajno smanjen. (X)BinEn registruje promene entropije slično (X)ApEn. Beskonačno klipovanje je gruba kvantizacija i za posledicu ima smanjenu osetljivost na promene, ali, sa druge strane, prigušuje binarnu asimetriju i nekonzistentnan uticaj parametara. Za određeni skup parametara (X)BinEn je ekvivalentna Šenonovoj entropiji. Uslovna binarna m=1 entropija automatski se dobija kao uzgredni product binarizovane entropije, i može da se iskoristi kao komplementarna dinamička mera. Zaključak: (X)BinEn može da se koristi za jedan vremenski niz, kao auto-entropija, ili, u opštem slučaju, za dva vremenska niza kao unakrsna entropija. Namenjena je mobilnim uređajima sa baterijskim napajanjem za individualne korisnike, to jest za korisnike sa ograničenim napajanjem i procesorskim resursima.

Faculty:
University of Novi Sad, Faculty of Technical Science
Date:
20-12-2017
Projects:
  • Development of multivariable methods for analytical support to biomedical diagnostics (RS-32040)
Keywords:
Entropy / Entropija / kardiovaskularni vremenski nizovi / Cardiovascular time series
[ Google Scholar ]
Handle
https://hdl.handle.net/21.15107/rcub_nardus_9125
URI
https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija147522235668753.pdf?controlNumber=(BISIS)101879&fileName=147522235668753.pdf&id=6939&source=NaRDuS&language=sr
https://nardus.mpn.gov.rs/handle/123456789/9125
https://www.cris.uns.ac.rs/record.jsf?recordId=101879&source=NaRDuS&language=sr
https://www.cris.uns.ac.rs/DownloadFileServlet/IzvestajKomisije147522236110131.pdf?controlNumber=(BISIS)101879&fileName=147522236110131.pdf&id=6940&source=NaRDuS&language=sr

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