Spontaanien aktiviteettipurskeiden automaattinen tunnistus keskosten aivosähkökäyrästä

Very preterm infants may require neonatal intensive care for several months, and the developmental outcome of the care depends on how well brain function can be managed. Direct monitoring of brain function with electroencephalography (EEG) is currently not a part of routine care, since it is perceived challenging due to difficulties in its interpretation. Therefore, automated methods for EEG interpretation are needed in order to make brain monitoring part of the routine in neonatal intensive care. This thesis investigates the detection of spontaneous activity transients (SATs), which form the majority of brain activity in preterm infants. Using manual markings by three doctors in 18 short recordings of preterm EEG, I show that SATs can be recognized by doctors in a consistent manner. A commercially available algorithm is then tested for its ability to detect SATs automatically. The performance of the algorithm is clearly insufficient and therefore it is developed further. The parameters of the new, streamlined algorithm are optimized using unanimous markings by the three doctors as a gold standard. Estimates for the performance of the algorithm on unseen data are obtained by running the optimization 18 times, each time leaving out one of the recordings. The algorithm is then run on the EEG left out from the optimization using the optimized parameters. The estimated performance of the algorithm is found to be excellent, with sensitivity of 96.6 +- 2.8 % and specificity of 95.1 +- 5.6 %. Segmentation of the EEG into SATs and periods between SATs is a starting point for further analysis. One promising direction for future studies is to use SAT%, the proportion of time covered by SATs, to detect cycles of different vigilance stages in preterm infants. Such cyclicity could become a marker of the brain's wellbeing. The algorithm presented in this thesis may contribute to better care of preterm infants.Erittäin ennenaikaisesti syntyneet keskoset saattavat tarvita teho-osastohoitoa jopa kuukausien ajan. Hoidon vaikutus lapsen kehitykseen riippuu paljon siitä, kuinka hyvin aivojen hoito onnistuu. Aivojen toiminnan jatkuva valvonta elektroenkefalografian (EEG) avulla ei vielä kuulu tavanomaiseen hoitokäytäntöön, koska EEG:n tulkintaa pidetään vaikeana. EEG:n tulkintaan tarvitaankin automaattisia menetelmiä, jotta aivojen tarkkailusta tulisi osa vastasyntyneiden tehohoidon rutiinia. Tässä työssä tutkitaan spontaanien aktiviteettipurskeiden tunnistamista (engl. spontaneous activity transient, SAT). Keskosten aivotoiminta muodostuu suurelta osin aktiviteettipurskeista. Käyttämällä kolmen lääkärin käsin tehtyjä merkintöjä aktiviteettipurskeista 18 lyhyessä keskosilta mitatussa EEG:ssä todistan, että lääkärit tunnistavat aktiviteettipurskeet johdonmukaisesti. Tämän jälkeen testaan, sopiiko eräs myynnissä oleva algoritmi aktiviteettipurskeiden automaattiseen tunnistukseen. Algoritmin suorituskyky ei ole riittävä, joten kehitän siitä paremman version. Uuden, parannellun algoritmin parametrit optimoidaan käyttämällä opetusaineistona niitä EEGjaksoja, joiden luokittelusta kaikki kolme lääkäriä olivat yhtä mieltä. Algoritmin suorituskykyä arvioidaan suorittamalla optimointi 18 kertaa siten, että kullakin kerralla yksi mittauksista jätetään pois opetusaineistosta. Optimoitua menetelmää käytetään sitten aktiviteettipurskeiden tunnistamiseen poisjätetyssä mittauksessa. Algoritmin arvioitu suorituskyky on erinomainen; sen sensitiivisyys on 96,6 +- 2,8 % ja spesifisyys 95,1 +- 5,6 %. EEG:n segmentointi aktiviteettipurskeisiin ja niiden välisiin jaksoihin tarjoaa pohjan jatkoanalyysille. Aktiviteettipurskeiden osuutta EEG:stä (SAT%) voidaan mahdollisesti käyttää keskosen vireystilan vaihtelujen seuraamiseen. Vireystilojen säännöllinen vaihtelu saattaa olla merkki aivojen hyvinvoinnista. Tässä työssä esitelty algoritmi voi osaltaan edesauttaa keskosten hoidon kehittymistä entistä paremmaksi

Altered N100-potential associates with working memory impairment in Parkinson's disease

The diagnosis of cognitive impairment and dementia often occurring with Parkinson's disease (PD) is still based on the clinical picture and neuropsychological examination. Ancillary methods to detect cognitive decline in these patients are, therefore, needed. Alterations in the latencies and amplitudes of evoked response potential (ERP) components N100 and P200 have been described in PD. Due to limited number of studies their relation to cognitive deficits in PD remains obscure. The present study was designed to examine if alterations in the N100- and P200-potentials associate with neuropsychological impairment in PD. EEG-ERP was conducted to 18 PD patients and 24 healthy controls. The patients underwent a thorough neuropsychological evaluation. The controls were screened for cognitive impairment with Consortium to Establish Alzheimer's disease (CERAD)-testing and a normal result were required to be included in the study. The N100-latency was prolonged in the patients compared to the controls (p = 0.05). In the patients, the N100 latency correlated significantly with a visual working memory task (p = 0.01). Also N100 latency was prolonged and N100 amplitude habituation diminished in the patients achieving poorly in this task. We conclude that prolonged N100-latency and diminished amplitude habituation associate with visual working memory impairment in PD.Peer reviewe

Impulsiveness and burn patients

Objective: Impulsiveness is a tendency to act quickly based on a whim without reflection or consideration of consequences. We studied its correlations with burn variables and mental disorders among burn patients. Methods: Consecutive acute burn patients (N=107) admitted to the Helsinki Burn Center were assessed with the Structured Clinical Interview for DSM-IV mental disorders (SCID) at baseline and at 6 months. All patients filled out the 30-item Barratt Impulsiveness Scale (BIS-11), the most commonly administered self-report measure and a standard point of reference in research on impulsiveness. Results: The mean total score of BIS-11 was 64.5 (range 41.0-87.8, SD +/- 6.9). There was not a significant correlation between impulsiveness and a range of characteristics of burn injury (all p-values >0.05). We found a significant correlation between some pre-burn mental disorders and impulsiveness, alcohol dependence and attentional impulsiveness (OR=1.22, p=0.022), any personality disorder and non-planning impulsiveness (OR=1.21, p=0.005), and antisocial personality disorder and motor impulsiveness (OR=1.35, p=0.043). Patients with high impulsiveness (total score >65) more often than those with low impulsiveness ( Conclusion: Impulsiveness had a significant correlation with mental disorders but not with burn-related variables. Therefore the role of impulsiveness in burn injuries should not be investigated independently without first accounting for the role of mental disorders. (C) 2018 Elsevier Ltd and ISBI. All rights reserved.Peer reviewe

Alcohol use and smoking in burn patients at the Helsinki Burn Center

Objective: We investigated alcohol use and smoking at time of burn and their relationships with severity of burn and presence of mental disorders. Methods: Consecutive acute burn patients (N = 107) admitted to the Helsinki Burn Center were assessed with the structured clinical interview for mental disorders (SCID) at baseline and after 6 months. Information regarding being under the influence of alcohol and having smoking-related activity at burn as well as about hazardous drinking (Alcohol Use Disorders Identification Test) and heavy smoking before the burn was recorded. Results: Around half (52%) of the acute burn patients were under the influence of alcohol and 19% had been both drinking and smoking at the time of the burn. Patients under the influence at the time of burn had significantly higher prevalence of lifetime mental disorders compared to those patients who were not under the influence of alcohol (73.2% vs. 45.1%, p = 0.003), especially alcohol dependence (55.4% vs. 13.7%, p <0.001) and anxiety disorders (28.6% vs. 9.8%, p = 0.015). Patients who had both alcohol use and smoking at burn had even more often at least one mental disorder (95.0% vs. 51.7%, p <0.001), in specific alcohol dependence (90.0% vs. 23.0%, p <0.001), or psychotic disorder (25.0% vs. 6.9%, p = 0.016). The main characteristics of the burns themselves did not differ significantly between these groups. Conclusion: Half of the burn patients were under the influence of alcohol at the time of the burn in this study. In almost all patients where alcohol and smoking contributed to the burn a diagnosable alcohol use disorder was present. Interventions for those with alcohol use disorders and the associated risk behaviors are important for the prevention of burns. (C) 2017 Published by Elsevier Ltd.Peer reviewe

Early Brain Activity Relates to Subsequent Brain Growth in Premature Infants

Recent experimental studies have shown that early brain activity is crucial for neuronal survival and the development of brain networks; however, it has been challenging to assess its role in the developing human brain. We employed serial quantitative magnetic resonance imaging to measure the rate of growth in circumscribed brain tissues from preterm to term age, and compared it with measures of electroencephalographic (EEG) activity during the first postnatal days by 2 different methods. EEG metrics of functional activity were computed: EEG signal peak-to-peak amplitude and the occurrence of developmentally important spontaneous activity transients (SATs). We found that an increased brain activity in the first postnatal days correlates with a faster growth of brain structures during subsequent months until term age. Total brain volume, and in particular subcortical gray matter volume, grew faster in babies with less cortical electrical quiescence and with more SAT events. The present findings are compatible with the idea that (1) early cortical network activity is important for brain growth, and that (2) objective measures may be devised to follow early human brain activity in a biologically reasoned way in future research as well as during intensive care treatmen

Tapahtumien tunnistus keskosten aivosähkökäyrästä

Preterm infants may spend months in neonatal intensive care units (NICU). Progress in neurological care of these infants depends on the ability to adequately monitor brain activity during NICU treatment. Brain monitoring is most commonly performed using electroencephalography (EEG). The preterm EEG signals are qualitatively different from EEG signals of older individuals, their distinguishing characteristics are the intermittently occurring spontaneous activity transients (SAT), which are believed to be crucial to early brain development. Automated detection of SATs might offer new tools for a neuroscientifically reasoned monitoring of infant brain in the NICU.  In this Thesis, a commercially available algorithm was tested for its applicability in detecting SATs. Because the algorithm was found to be suboptimal, an improved algorithm was developed and its parameters were optimized. Optimization and validation were done systematically, using a gold standard composed of unanimous detections by three human raters. The optimized algorithm was then used to calculate event-based measures in two clinical studies, one studying SAT occurrence in sleep stages, and the other comparing brain activity to structural brain growth.  In leave-one-out crossvalidation, the optimized algorithm showed excellent performance (sensitivity 96.6±2.8 %, specificity 95.1±5.6 %). In the clinical studies conducted, the proportion of EEG covered by SATs (SAT%) was shown to differ between sleep states, providing a possibility for developing an EEG-based measure of brain activity cycling in preterm infants. Finally, brain activity indices derived from EEG recordings shortly after birth were shown to correlate with subsequent structural growth of the brain during preterm life. The findings together show that an SAT event detector can be constructed for the brain monitoring in NICU, and that indices based on event detection may offer important insight to brain function in the clinical research.Keskosten hoito vastasyntyneiden teho-osastolla saattaa kestää kuukausia. Keskosten neurologisen hoidon kehittämisen kannalta on ensiarvoisen tärkeää, että aivojen toimintaa voidaan monitoroida asianmukaisesti hoidon aikana. Yleisin aivojen monitorointiin käytetty menetelmä on elektroenkefalografia (EEG). Keskosten EEG-signaalit eroavat laadullisesti vanhempien yksilöiden signaaleista. Niille tunnusomaisia ovat epäsäännöllisin välein ilmenevät spontaanit aktiviteettipurskeet (spontaneous activity transient, SAT), joiden uskotaan olevan korvaamattomia varhaiselle aivojen kehitykselle. Aktiviteettipurskeiden automaattinen tunnistus saattaisi tarjota uusia neurotieteellisesti perusteltuja välineitä vauvan aivojen monitorointiin vastasyntyneiden teho-osastolla. Tässä väitöskirjatyössä testattiin erään kaupallisesti saatavilla olevan algoritmin soveltuvuutta aktiviteettipurskeiden tunnistukseen. Testeissä havaittiin, että algoritmi ei ollut tarkoitukseen optimaalinen, ja tästä syystä tunnistukseen kehitettiin uusi algoritmi, jonka parametrit optimoitiin. Optimointi ja validointi tehtiin järjestelmällisesti, hyödyntäen vertailuaineistona vain sellaisia EEG-jaksoja, jotka kolme asiantuntijaa olivat luokitelleet samalla tavoin. Optimoitua algoritmia käytettiin sitten tapahtumapohjaisten muuttujien laskemiseen kahdessa kliinisessä tutkimuksessa, joista toisessa tutkittiin aktiviteettipurskeiden esiintymistä eri univaiheissa ja toisessa vertailtiin aivotoiminnan aktiivisuutta aivojen rakenteelliseen kasvuun. Optimoidun algoritmin suorituskyky todettiin ristiinvalidoinnissa erinomaiseksi (sensitiivisyys 96,6±2,8%, spesifisyys 95,1±5,6%). Sillä tehdyissä kliinisissä tutkimuksissa osoitettiin, että aktiviteettipurskeiden osuus EEG:stä (SAT%) on erilainen eri univaiheissa. Tulos tarjoaa mahdollisuuden kehittää EEG:hen pohjautuva muuttuja keskosten aivojen aktiivisuuden jaksottaisen vaihtelun tutkimiseen. Lopuksi osoitettiin, että pian syntymän jälkeen tehdyistä EEG-mittauksista lasketut aivojen aktiivisuutta kuvaavat muuttujat korreloivat aivojen rakenteellisen kasvun kanssa keskosaikana. Tulokset osoittavat, että aktiviteettipurskeiden automaattinen tunnistus on mahdollista ja sitä voidaan käyttää aivojen toiminnan monitorointiin vastasyntyneiden teho-osastolla. Tunnistukseen pohjautuvat muuttujat voivat kliinisessä tutkimuksessa antaa tärkeää uutta ymmärrystä aivojen toiminnasta

An openly available wearable, a diaper cover, monitors infant's respiration and position during rest and sleep

cited By 0Aim To describe and test the accuracy of respiratory rate assessment in long-term surveillance using an open-source infant wearable, NAPping PAnts (NAPPA). Methods We recorded 24 infants aged 1-9 months using our newly developed infant wearable that is a diaper cover with an integrated programmable electronics with accelerometer and gyroscope sensors. The sensor collects child's respiration rate (RR), activity and body posture in 30-s epochs, to be downloaded afterwards into a mobile phone application. An automated RR quality measure was also implemented using autocorrelation function, and the accuracy of RR estimate was compared with a reference obtained from the simultaneously recorded capnography signal that was part of polysomnography recordings. Results Altogether 88 h 27 min of data were recorded, and 4147 epochs (39% of all data) were accepted after quality detection. The median of patient wise mean absolute errors in RR estimates was 1.5 breaths per minute (interquartile range 1.1-2.6 bpm), and the Blandt-Altman analysis indicated an RR bias of 0.0 bpm with the 95% limits of agreement of -5.7-5.7 bpm. Conclusion Long-term monitoring of RR and posture can be done with reasonable accuracy in out-of-hospital settings using NAPPA, an openly available infant wearable.Peer reviewe