Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in-home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re-trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% (kappa = 0.729) for five sleep stages (W, N1, N2, N3, and R), 84.1% (kappa = 0.773) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (kappa = 0.801) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in-home PSGs.Peer reviewe

Generalizable Deep Learning-Based Sleep Staging Approach for Ambulatory Textile Electrode Headband Recordings

Publisher Copyright: © 2013 IEEE.Reliable, automated, and user-friendly solutions for the identification of sleep stages in home environment are needed in various clinical and scientific research settings. Previously we have shown that signals recorded with an easily applicable textile electrode headband (FocusBand, T 2 Green Pty Ltd) contain characteristics similar to the standard electrooculography (EOG, E1-M2). We hypothesize that the electroencephalographic (EEG) signals recorded using the textile electrode headband are similar enough with standard EOG in order to develop an automatic neural network-based sleep staging method that generalizes from diagnostic polysomnographic (PSG) data to ambulatory sleep recordings of textile electrode-based forehead EEG. Standard EOG signals together with manually annotated sleep stages from clinical PSG dataset (n = 876) were used to train, validate, and test a fully convolutional neural network (CNN). Furthermore, ambulatory sleep recordings including a standard set of gel-based electrodes and the textile electrode headband were conducted for 10 healthy volunteers at their homes to test the generalizability of the model. In the test set (n = 88) of the clinical dataset, the model's accuracy for 5-stage sleep stage classification was 80% (κ = 0.73) using only the single-channel EOG. The model generalized well for the headband-data, reaching 82% (κ = 0.75) overall sleep staging accuracy. In comparison, accuracy of the model was 87% (κ = 0.82) in home recordings using the standard EOG. In conclusion, the CNN model shows potential on automatic sleep staging of healthy individuals using a reusable electrode headband in a home environment.Peer reviewe

Seleenityöryhmän raportti 2016

Suomessa 1970–luvulla tehdyissä tutkimuksissa havaittiin elintarvikkeiden seleenipitoisuuksien olevan erittäin pieniä ja väestön seleeninsaanti jäi selvästi alle saantisuositusten. Taustalla oli seleenin ja erityisesti liukoisen, kasveille käyttökelpoisen seleenin pieni määrä viljelymaissa. Tilanteen korjaamiseksi natriumselenaattia on lisätty moniravinteisiin lannoitteisiin vuodesta 1984 lähtien. Seleenilannoituksen avulla epäorgaaninen lannoiteseleeni muuttuu kasveissa orgaanisiksi seleeniyhdisteiksi, joita ihmiset ja eläimet pystyvät hyödyntämään tehokkaammin kuin epäorgaanista seleeniä. Seleenilannoituksen myötä kotimaisten viljelykasvien ja rehujen ja sitä kautta elintarvikkeiden seleenipitoisuudet ovat kasvaneet. Liukoisen seleenin määrä viljelymaissa ei ole kuitenkaan kasvanut 30 vuoden aikana, sillä Suomen olosuhteissa seleeni muuttuu nopeasti niukkaliukoiseen muotoon. Lannoitteiden kautta maahan vuosittain tuleva seleenilisä tarvitaan kasvien seleenitason ylläpitämiseksi. Viljelykasvien seleenipitoisuus riippuu täysin lannoitteiden seleenitasosta ja seleenipitoisten lannoitteiden käyttömääristä. Väestön keskimääräinen seleeninsaanti on nykyisin sekä koti- että ulkomaisten saantisuositusten mukaista. Tärkeimmät saantilähteet ovat maitotuotteet ja liha, mutta myös kasvisruokavaliosta voidaan saada riittävästi seleeniä. Ihmisen veren seerumin seleenipitoisuus on 2000-luvulla ollut keskimäärin 1,4 µmol l-1, mikä on 60 % suurempi kuin ennen lannoitteiden seleenilisäystä vuonna 1984. Vuonna 2007 tehty lannoittei-den seleenipitoisuuden nosto (10→15 mg kg-1) näkyy seerumista mitatun seleenitason vakiintumi-sena >1,4 µmol l-1 pitoisuuksiin. Seleenilannoitustasoa on muutettu kolme kertaa vuosina 1990, 1998 ja 2007. Muutokset ovat pohjautuneet seleeninsaannissa tapahtuneisiin muutoksiin. Seleenilannoitus on tehokas, turvallinen, edullinen ja toimiva tapa vaikuttaa tuotantoeläinten ja väestön seleeninsaantiin ja sitä kautta kansanterveyteen. Se parantaa eläinten hyvinvointia vähentämällä tarvetta lisätä seleeniä rehuihin sekä vähentämällä tarvetta eläinten seleenilääkintään ja se ehkäisee seleeninpuutossairauksia kuten esim. lihasrappeumaa. Suomen olosuhteissa toimenpide on osoittautunut hyväksi ja turvalliseksi keinoksi vaikuttaa kotieläinten ja väestön seleeninsaantiin. Suunnitelmallisen ja tarkkaan kohdennetun seurannan myötä systeemi on kontrolloitavissa ja seleeninsaannissa tapahtuviin muutoksiin pystytään reagoimaan nopeasti.201

Kuiva-ainepitoisuuden ja säilöntäaineen vaikutus pyöröpaalatun säilörehun laatuun ja lypsylehmien maidontuotantoon

Tutkimuksen tekopaikka: MTT Kotieläintuotannon tutkimus Jokioinen; Työn ohjaus ja tutkijat: Yliopistonlehtori Seija Jaakkola, tutkija Terttu Heikkilä ja tutkija Eeva Saarisalo

Effect of Sweating on Electrode-Skin Contact Impedances and Artifacts in EEG Recordings With Various Screen-Printed Ag/Agcl Electrodes

In response to the growing clinico-economic need for comprehensive home-based sleep testing, we recently developed a self-applicable facial electrode set with screen-printed Ag/AgCl electrodes. Our previous studies revealed that nocturnal sweating is a common problem, causing low-frequency artifacts in the measured electroencephalography (EEG) signals. As the electrode set is designed to be used without skin abrasion, not surprisingly this leads to relatively high electrode-skin impedances, significant impedance changes due to sweating and an increased risk of sweat artifacts. However, our recent electrochemical in vitro investigations revealed that the sweat artifact tolerance of an EEG electrode can be improved by utilizing an appropriate Ag/AgCl ink. Here we have investigated in vivo electrode-skin impedances and the quality of EEG signals and interference due to sweating in the population of 11 healthy volunteers. Commercial Ag and Ag/AgCl inks (Engineered Conductive Materials ECM LLC and PPG Industries Inc.) were used to test electrode sets with differently constructed ink layers. Electrode-skin impedances and EEG signals were recorded before and after exercise-induced sweating. There was extensive variation in the electrode-skin impedances between the volunteers and the electrode positions: 14.6 & x2013;200 (PPG electrodes) and 7.7 & x2013;200 (ECM electrodes). Sweating significantly decreased the impedances in most cases. The EEG signal quality was assessed by comparing average band powers from 0.5 to 2 Hz before and after sweating. Only slight differences existed between the ECM and PPG electrodes; however, the lowest band power ratio (i.e. the smallest increase in the band power due to sweating) was achieved with ECM electrodes.Peer reviewe

Thirty years of selenium fertilization: a Finnish solution to ensure an adequate selenium intake

The 11th International Symposium on Selenium in Biology and Medicine and The 5th International Conference on Selenium in the Environment and Human Health, Stockholm, 13-17 August 2017201

Technical Performance of Textile-Based Dry Forehead Electrodes Compared with Medical-Grade Overnight Home Sleep Recordings

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadThe current clinically used electroencephalography (EEG) sensors are not self-applicable. This complicates the recording of the brain's electrical activity in unattended home polysomnography (PSG). When EEG is not recorded, the sleep architecture cannot be accurately determined, which decreases the accuracy of home-based diagnosis of sleep disorders. The aim of this study was to compare the technical performance of FocusBand, an easily applicable textile electrode headband, to that of clinical EEG and electrooculography (EOG) electrodes. Overnight unattended recordings were conducted at participants' (n = 10) homes. Signals were recorded using a portable Nox A1 PSG device. The FocusBand's forehead EEG (Fp1-Fp2) signals contained features that are visible at both, the standard EEG (F4-M1) and EOG (E1-M2) signals. The FocusBand's EEG signal amplitudes were significantly lower compared to standard EEG (F4-M1; average difference 98%) and EOG (E1-M2; average difference 29%) signals during all sleep stages. Despite the amplitude difference, forehead EEG signals displayed typical EEG characteristics related to certain sleep stages. However, the frequency content of the FocusBand-based signals was more similar to that of the standard EOG signals than that of standard EEG signals. The majority of the artifacts seen in the FocusBand signals were related to a loosened headband. High differences in the frequency content of the compared signals were also found during wakefulness, suggesting susceptibility of the textile electrodes to electrode movement artifacts. This study demonstrates that the forehead biopotential signals recorded using an easily attachable textile electrode headband could be useful in home-based sleep recordings.European Union's Horizon 2020 Research and Innovation Programme NordForsk through the Business Finland NordForsk through Icelandic Center for Research Academy of Finland European Commission Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding Finnish Cultural Foundation Finnish Cultural Foundation-Kainuu Regional Fund Respiratory Foundation of Kuopio Region Research Foundation of the Pulmonary Diseases Finnish Anti-Tuberculosis Association Tampere Tuberculosis Foundatio

Effects of nationwide addition of selenium to fertilizers on foods, and animal and human health in Finland: From deficiency to optimal selenium status of the population

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