Huomaamattomat mittausmenetelmät unen laadun tarkkailussa

Sleep is an important part of health and well-being. While sleep quantity is directly measurable, sleep quality has traditionally been assessed with subjective methods such as questionnaires. The study of sleep disorders has for a long time been confined to clinical environments, and patients have had to endure cumbersome procedures involving multiple electrodes placed on the body. Recent developments in sensor technology as well as data analysis methods have enabled continuous, unobtrusive sleep data recording in the home environment. This has opened new possibilities for studying various sleep parameters and their effect on the quality of sleep. This thesis consists of two parts. The first part is a literature review examining the field of sleep quality research with focus on the application of intelligent methods and signal processing. The second part is a descriptive data analysis look at sleep data obtained with non-invasive sensors.Uni on terveyden ja hyvinvoinnin keskeinen tekijä. Unen määrä on helposti mitattavissa, mutta unen laatua on perinteisesti seurattu kyselylomakkeiden kaltaisin subjektiivisin menetelmin. Unihäiriöiden tutkiminen on pitkään rajoittunut kliinisiin ympäristöihin, ja potilaiden on täytynyt sietää hankalia tutkimusmenetelmiä useine kehoon kiinnitettävine elektrodeineen. Anturiteknologian ja data-analyysimenetelmien kehittyminen on mahdollistanut unidatan jatkuvan ja huomaamattoman tallentamisen kotiympäristössä. Tämä on avannut uusia mahdollisuuksia sekä unen ominaisuuksien että niiden unen laatuun vaikuttavien tekijöiden tutkimiselle. Tämä tutkimus jakautuu kahteen osaan. Ensimmäinen osa on kirjallisuuskatsaus unen laadun tutkimukseen, painopisteenä älykkäiden menetelmien ja signaalinkäsittelyn soveltaminen. Toisessa osassa esitellään huomaamattomilla sensoreilla kerättävän unidatan tutkimista ja sen deskriptiivistä data-analyysiä, esimerkkinä ballistokardiografia

Principal Component Analysis Visualizations in State Discovery by Animating Exploration Results

Visualization is a key point in data exploration. In this paper we have emphasis in adding dynamic features by constructing exploration animations. We use Principal Component Analysis (PCA) in dimensionality reduction and Kmeans clustering algorithm in defining states. In predicting state transitions, we use Hidden Markov Model (HMM). Analyzed physical data is got from self-healing autonomous data centers. Our research methodology is to animate state transitions for data exploration in modern computerized environment. We use Jupyter tool and Python 3 programming language in our experimental realization. As results we get PCA animations for exploration purposes. Our approach is based on state discovery, where it is possible to find some physical interpretations for the defined states and state transitions. State structure and behaviour depend strongly on analyzed data.Peer reviewe

Gaussian process classification for prediction of in-hospital mortality among preterm infants

We present a method for predicting preterm infant in-hospital mortality using Bayesian Gaussian process classification. We combined features extracted from sensor measurements, made during the first 72 h of care for 598 Very Low Birth Weight infants of birth weight <1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores. (C) 2018 Elsevier B.V. All rights reserved.Peer reviewe

A triple-chamber parenteral nutrition solution was associated with improved protein intake in very low birthweight infants

Aim We evaluated the nutrient intakes of very low birthweight (VLBW) infants weighing less than 1500 g and tested the hypothesis that using a triple-chamber parenteral nutrition (PN) solution, containing lipids, glucose and amino acids, would improve protein intake. Methods This retrospective cohort study comprised 953 VLBW infants born in 2005-2013 at a gestational age of less than 32 + 0/7 weeks and admitted to the neonatal care unit at Helsinki Children's Hospital, Finland. The infants were divided into four groups according their birth year and PN regime. Nutrient intakes were obtained from computerised medication administration records. Results In 2012-2013, when a triple-chamber PN solution was used, infants were more likely to reach the target parenteral protein intake of 3.5 g/kg/d, and reach it 3-7 days earlier, compared with infants who received individual PN or standard two-in-one PN solutions in 2005-2011. In addition, infants in the triple-chamber group had the highest median energy intake (90 kcal/kg/d) during the first week. They also had higher median protein intakes in weeks one, two and three (3.1, 3.4 and 3.7 g/kg/d) than infants born in 2005-2011 (P <.05). Conclusion Using a triple-chamber PN solution was associated with improved protein intake, and the protein target was more likely to be achieved.Peer reviewe

Early oxygen levels contribute to brain injury in extremely preterm infants

Background Extremely low gestational age newborns (ELGANs) are at risk of neurodevelopmental impairments that may originate in early NICU care. We hypothesized that early oxygen saturations (SpO(2)), arterial pO(2) levels, and supplemental oxygen (FiO(2)) would associate with later neuroanatomic changes. Methods SpO(2), arterial blood gases, and FiO(2) from 73 ELGANs (GA 26.4 +/- 1.2; BW 867 +/- 179 g) during the first 3 postnatal days were correlated with later white matter injury (WM, MRI, n = 69), secondary cortical somatosensory processing in magnetoencephalography (MEG-SII, n = 39), Hempel neurological examination (n = 66), and developmental quotients of Griffiths Mental Developmental Scales (GMDS, n = 58). Results The ELGANs with later WM abnormalities exhibited lower SpO(2) and pO(2) levels, and higher FiO(2) need during the first 3 days than those with normal WM. They also had higher pCO(2) values. The infants with abnormal MEG-SII showed opposite findings, i.e., displayed higher SpO(2) and pO(2) levels and lower FiO(2) need, than those with better outcomes. Severe WM changes and abnormal MEG-SII were correlated with adverse neurodevelopment. Conclusions Low oxygen levels and high FiO(2) need during the NICU care associate with WM abnormalities, whereas higher oxygen levels correlate with abnormal MEG-SII. The results may indicate certain brain structures being more vulnerable to hypoxia and others to hyperoxia, thus emphasizing the role of strict saturation targets. Impact This study indicates that both abnormally low and high oxygen levels during early NICU care are harmful for later neurodevelopmental outcomes in preterm neonates. Specific brain structures seem to be vulnerable to low and others to high oxygen levels. The findings may have clinical implications as oxygen is one of the most common therapies given in NICUs. The results emphasize the role of strict saturation targets during the early postnatal period in preterm infants.Peer reviewe

Koneoppiminen vastasyntyneiden tehohoidossa

This thesis deals with the applications of machine learning in the context of neonatal intensive care. The main focus is on prediction using multichannel time series data. The purpose of developing prediction methods is to create data-driven tools for helping care personnel to make more informed decisions.  Preterm infants, born before 37 weeks of gestation, are subject to many developmental issues and health problems. Very Low Birth Weight infants, with a birth weight under 1500 g, are the most afflicted in this group. These infants require treatment in the neonatal intensive care unit before they are mature enough for hospital discharge.  The neonatal intensive care unit is a data-intensive environment, where multi-channel physiological data is gathered from patients using a number of sensors to construct a comprehensive picture of the patients' vital signs. Neonatal intensive care requires combining information from multiple sources, often with severe and far-reaching consequences for the patient. Real-time data is available on multiple monitor screens in the ward, and is used by care personnel to help them make informed decisions. Analysis of trends and vast data sets, possibly spanning thousands of patients, requires the use of machine learning methods.  In the research done for this thesis we have applied machine learning methods, in particular Gaussian process classification, to predict neonatal in-hospital mortality and morbidities. We have used time series data collected from Very Low Birth Weight infants treated in the neonatal intensive care unit of Helsinki University Hospital between 1999 and 2013. Our results show that machine learning models based on time series data alone have predictive power comparable with standard medical scores, and combining the two results in improved predictive ability. We have also studied the effect of observer bias on recording vital sign measurements in the neonatal intensive care unit, as well as conducted a retrospective cohort study on trends in the growth of Extremely Low Birth Weight (birth weight under 1000 g) infants during intensive care.Tämä väitöskirja käsittelee koneoppimismenetelmien soveltamista vastasyntyneiden tehohoidossa. Pääasiallinen tavoite on potilaan tilan ennustaminen monikanavaisen aikasarjadatan perusteella. Ennustusmenetelmien tutkimisen päämäärä on datalähtöisten työkalujen kehittäminen hoitohenkilökunnan päätöksenteon avuksi.  Ennen 37. raskausviikkoa syntyneillä keskoslapsilla on monia kehitykseen ja terveyteen liittyviä ongelmia. Näistä lapsista kaikkein ongelmallisimpia ovat syntymäpainoltaan alle 1500 g painoiset pikkukeskoset, jotka tarvitsevat tehohoitoa vastasyntyneiden teho-osastolla ennen kotiutumistaan.  Vastasyntyneiden teho-osasto on paljon dataa tuottava ympäristö, jossa potilaista kerätään monikanavaista fysiologista dataa erilaisten sensorien avulla. Sensoridataa käyttämällä voidaan muodostaa kokonaisvaltainen kuva potilaan tilasta ja elintoiminnoista. Vastasyntyneiden tehohoidossa joudutaan tekemään monista lähteistä kerätyn tiedon perusteella reaaliaikaisia päätöksiä, joilla voi olla potilaan kannalta vakavia ja pitkäkestoisia vaikutuksia. Hoitohenkilökunta voi päivittäisessä työssään käyttää hyväkseen teho-osaston näytöiltä saatavaa reaaliaikaista dataa. Pitkäaikaisten trendien ja suurien, jopa tuhansia potilaita sisältävien datamäärien analyysi edellyttää koneoppimismenetelmien käyttöä.  Tässä väitöskirjassa sovelletaan koneoppimismenetelmiä, erityisesti luokittelua gaussisten prosessien avulla, sairaalakuolleisuuden ja tiettyjen sairauksien ennustamiseen. Tutkimusaineisto oli fysiologisia signaaleja sisältävää aikasarjadataa, jota on kerätty HUS:in vastasyntyneiden teho-osastolla vuosina 1999-2013 hoidetuista pikkukeskosista. Tuloksemme osoittavat, että aikasarjadatan pohjalta luodun koneoppimismallin ennustusvoima on verrannollinen lääketieteellisten tunnuslukujen kanssa, ja että näiden yhdistelmällä saadaan parempia tuloksia kuin kummallakaan yksin. Tutkimme myös elintoimintomittausten muuttumista havainnoitsijan painotuksen mukaan. Lisäksi väitöskirjaan sisältyy retrospektiivinen kohorttitutkimus muutoksista alle 1000 g syntymäpainoisten pikkukeskosten kasvussa tehohoidon aikana

Hidden sources like saline flushes make a significant contribution to the fluid intake of very low birthweight infants during the first postnatal week

Aim We examined actual fluid intake, and routes of administration, in very low birthweight (VLBW) infants during the first week of life in a neonatal intensive care unit. Methods This retrospective cohort study comprised 953 infants born atPeer reviewe