Exploring behaviour patterns with self-organizing map for personalised mental stress detection

Abstract. Stress is an important health problem and the cause for many illnesses and working days lost. It is often measured with different questionnaires that capture only the current stress levels and may come in too late for early prevention. They are also prone to subjective inaccuracies since the feeling of stress, and the physiological response to it, have been found to be individual. Real-time stress detectors, trained on biosignals like heart rate variability, exist but majority of them employ supervised learning which requires collecting a large amount of labelled data from each system user. Commonly, they are tested in situations where the stress response is deliberately induced (e.g. laboratory). Thus they may not generalise to real-life conditions where more general behavioural data could be used. In this study the issues with labelling and individuality are addressed by fitting unsupervised stress detection models at several personalisation levels. The method explored, the Self-Organizing Map, is combined with different clustering algorithms to find personal, semi-personal and general behaviour patterns that are converted to stress predictions. Laboratory biosignal-data are used for method validation. To provide an always-on type stress detection, real-life behavioural data consisting of biosignals and smartphone data are experimented on. The results show that personalisation does improve the predictions. The best classification performance for the laboratory data was found with the fully personalised model (F1-score 0.89 vs. 0.45 with the general model) but for the real-life data there was no big difference between fully personal (F1-score 0.57) and general model as long as the behaviour patterns were mapped to stress individually (F1-score 0.60). While the scores also validate the feasibility of SOM for mental stress detection, further research is needed to determine the most suitable and practical level of personalisation and an unambiguous mapping between behaviour patterns and stress.Tiivistelmä. Stressi on merkittävä terveysongelma ja syynä useisiin sairauksiin sekä työpoissaoloihin. Sitä mitataan usein erilaisilla kyselyillä, jotka kuvaavat vain hetkellistä stressitasoa ja joihin voidaan vastata liian myöhään ennaltaehkäisyn kannalta. Kyselyt ovat myös alttiita subjektiivisille epätarkkuuksille, koska stressintunteen, ja stressinaikaisten fysiologisten reaktioiden, on havaittu olevan yksilöllisiä. Reaaliaikaisia, biosignaalien kuten sykevälivaihtelun analyysiin perustuvia, stressintunnistimia on olemassa, mutta pääosin ne käyttävät ohjatun oppimisen menetelmiä, mikä vaatii jokaiselta järjestelmän käyttäjältä suuren stressintunteella merkityn aineiston. Stressintunnistimia myös usein testataan tilanteissa, joissa stressi on tahallisesti aiheutettua (esimerkiksi laboratoriossa). Siten ne eivät yleisty tosielämän tarpeisiin, jolloin voidaan käyttää yleisempää käyttäytymistä kuvaavaa aineistoa. Tässä tutkimuksessa vastataan datan merkintäongelmaan sekä yksilöllisyyden huomioimiseen käyttäen ohjaamattoman oppimisen stressintunnistusmalleja eri yksilöimisen tasoilla. Käytetty menetelmä, itseorganisoituva kartta, yhdistetään eri ryhmittelyalgoritmeihin tavoitteena löytää henkilökohtaiset, osin henkilökohtaiset sekä yleiset käyttäytymismallit, jotka muunnetaan stressiennusteiksi. Menetelmän sopivuuden vahvistamiseksi käytetään laboratoriossa kerättyä biosignaalidataa. Menetelmää sovelletaan myös tosielämän stressintunnistukseen biosignaaleista ja älypuhelimen käyttödatasta koostuvalla käyttäytymisaineistolla. Tulokset osoittavat, että yksilöiminen parantaa ennustetarkkuutta. Laboratorio-aineistolla paras luokittelutarkkuus löydettiin täysin yksilöllisellä mallilla (F1-pistemäärä 0.89, kun yleisellä 0.45). Tosielämän aineistolla täysin yksilöllisen (F1-pistemäärä 0.57) ja yleisen mallin, jossa käyttäytymismallien ja stressin välinen kuvaus määrättiin yksilöidysti (F1-pistemäärä 0.60), välinen ero ei ollut suuri. Vaikka tulokset vahvistavatkin itseorganisoituvan kartan sopivuuden psyykkisen stressin tunnistamisessa, lisätutkimusta tarvitaan määräämään soveltuvin ja käytännöllisin yksilöimisen taso sekä yksikäsitteinen kuvaus käyttäytymismallien ja stressin välille

A characteristic time sequence of epileptic activity in EEG during dynamic penicillin-induced focal epilepsy—A preliminary study

AbstractPenicillin-induced focal epilepsy is a well-known model in experimental epilepsy. However, the dynamic evolution of waveforms, DC-level changes, spectral content and coherence are rarely reported. Stimulated by earlier fMRI findings, we also seek for the early signs preceding spiking activity from frequency domain of EEG signal. In this study, EEG data is taken from previous EEG/fMRI series (six pigs, 20–24kg) of an experimental focal epilepsy model, which includes dynamic induction of epileptic activity with penicillin (6000IU) injection into the somatosensory cortex during deep isoflurane anaesthesia. No ictal discharges were recorded with this dose. Spike waveforms, DC-level, time–frequency content and coherence of EEG were analysed. Development of penicillin induced focal epileptic activity was not preceded with specific spectral changes. The beginning of interictal spiking was related to power increase in the frequencies below 6Hz or 20Hz, and continued to a widespread spectral increase. DC-level and coherence changes were clear in one animal. Morphological evolution of epileptic activity was a collection of the low-amplitude monophasic, bipolar, triple or double spike-wave forms, with an increase in amplitude, up to large monophasic spiking. In conclusion, in the time sequence of induced epileptic activity, immediate shifts in DC-level EEG are plausible, followed by the spike activity-related widespread increase in spectral content. Morphological evolution does not appear to follow a clear continuum; rather, intermingled and variable spike or multispike waveforms generally lead to stabilised activity of high-amplitude monophasic spikes

Genetic susceptibility of intervertebral disc degeneration among young Finnish adults

<p>Abstract</p> <p>Background</p> <p>Disc degeneration (DD) is a common condition that progresses with aging. Although the events leading to DD are not well understood, a significant genetic influence has been found. This study was undertaken to assess the association between relevant candidate gene polymorphisms and moderate DD in a well-defined and characterized cohort of young adults. Focusing on young age can be valuable in determining genetic predisposition to DD.</p> <p>Methods</p> <p>We investigated the associations of existing candidate genes for DD among 538 young adults with a mean age of 19 belonging to the 1986 Northern Finland Birth Cohort. Nineteen single nucleotide polymorphisms (SNP) in 16 genes were genotyped. We evaluated lumbar DD using the modified Pfirrmann classification and a 1.5-T magnetic resonance scanner for imaging.</p> <p>Results</p> <p>Of the 538 individuals studied, 46% had no degeneration, while 54% had DD and 51% of these had moderate DD. The risk of DD was significantly higher in subjects with an allele G of <it>IL6 </it>SNPs rs1800795 (OR 1.45, 95% CI 1.07-1.96) and rs1800797 (OR 1.37, 95% CI 1.02-1.85) in the additive inheritance model. The role of <it>IL6 </it>was further supported by the haplotype analysis, which resulted in an association between the GGG haplotype (SNPs rs1800797, rs1800796 and rs1800795) and DD with an OR of 1.51 (95% CI 1.11-2.04). In addition, we observed an association between DD and two other polymorphisms, <it>SKT </it>rs16924573 (OR 0.27 95% CI 0.07-0.96) and <it>CILP </it>rs2073711 in women (OR 2.04, 95% CI 1.07-3.89).</p> <p>Conclusion</p> <p>Our results indicate that <it>IL6</it>, <it>SKT </it>and <it>CILP </it>are involved in the etiology of DD among young adults.</p

Magnetic resonance imaging-guided biopsies in children

Abstract Background:Magnetic resonance imaging (MRI) is used far less as an imaging-guided method for percutaneous biopsies than computed tomography (CT) and ultrasound (US), despite its imaging benefits, particularly in children. Purpose:To evaluate the feasibility, accuracy and safety of MRI-guided biopsies in paediatric patient population. Material and Methods: The retrospective study included 57 consecutive paediatric patients (&lt;18 years old). A percutaneous core needle biopsy (PCNB) or trephine biopsy was performed in 53 cases, and an additional fine-needle aspiration biopsy (FNAB) in 26 cases. In 4 cases, a stand-alone FNAB was taken. Biopsies were performed with 0.23 T open and 1.5 T closed MRI scanners. Statistical methods used for confidence intervals and p-values were Wilson score method and chi-square test. Results:The overall diagnostic accuracy of histologic biopsy was 0.94, with sensitivity 0.82, specificity 1.00, positive predictive value (PPV) 1.00 and negative predictive value (NPV) 0.92. In histological bone biopsies, diagnostic accuracy was 0.96, with sensitivity 0.86, specificity 1.00, PPV 1.00 and NPV 0.94. The FNAB sample diagnosis was associated with the histological diagnosis in 79% of cases. There were no major primary complications and only a few late complications. After biopsy, 83% of the children were ambulatory in 6 h. Anti-inflammatory drugs and paracetamol provided satisfactory pain relief in 96% of the patients after biopsy. Most outpatients (71%) were discharged from hospital either on the same day or 1 day later. Conclusions:MRI is a technically feasible, accurate and safe guidance tool for performing percutaneous biopsies in children

Does bone scintigraphy show Modic changes associated with increased bone turnover?

Abstract Purpose: Our purpose was to evaluate whether Modic changes (MC) revealed in lumbar MRI are associated with increased tracer uptake shown in bone scintigraphy. To our knowledge, this has not previously been studied. Methods: We included patients with MC shown in lumbar MRI and bone scintigraphy performed within six months before or after MRI. Exclusion criteria included metastasis and other specific lesions in the area of interest such as discitis, tumors or fractures. We compared the level and type of MC to the degree of tracer uptake shown in bone scintigraphy. Tracer uptake was assessed both visually and quantitatively. We calculated the lesion-to-normal-bone ratios between the MC area with increased tracer uptake and the vertebra with normal tracer uptake. We used linear mixed models in statistical analyses. Results: Our study sample consisted of 93 patients (aged 37–86) with 299 MC (28 Type 1 (M1), 50 mixed Type 1/2 (M1/2), 3 mixed Type 1/3 (M1/3), 211 Type 2 (M2), 6 mixed Type 2/3 (M2/3), and 1 Type 3 (M3)). Of all the MC, 26 (93 %) M1, 34 (64 %) in the combined M1/2 and M1/3 group, and 11 (5 %) in the combined M2, M2/3 and M3 group showed increased tracer uptake. The mean lesion-to-normal-bone ratio was higher for lesions with a Type 1 component (M1, M1/2 and M1/3) than for other types, at 1.55 (SD 0.16) for M1; 1.44 (SD 0.21) for combined M1/2 and M1/3; and 1.28 (SD 0.11) for combined M2, M2/3 and M3; p = 0.001). Conclusion: In most cases, MC with a Type 1 component showed increased tracer uptake in bone scintigraphy. This indicates that bone turnover is accelerated in the M1 area

Video2IMU:realistic IMU features and signals from videos

Abstract Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled data is not straightforward, since wearable sensor signals are not easy to label upon simple human inspection. In our work, we propose the use of neural networks for the generation of realistic signals and features using human activity monocular videos. We show how these generated features and signals can be utilized, instead of their real counterparts, to train HAR models that can recognize activities using signals obtained with wearable sensors. To prove the validity of our methods, we perform experiments on an activity recognition dataset created for the improvement of industrial work safety. We show that our model is able to realistically generate virtual sensor signals and features usable to train a HAR classifier with comparable performance as the one trained using real sensor data. Our results enable the use of available, labeled video data for training HAR models to classify signals from wearable sensors

Association between modic changes and low back pain in middle age:a Northern Finland Birth Cohort Study

Abstract Study Design: A cross-sectional study of the Northern Finland Birth Cohort 1966 (NFBC1966). Objective: The aim of this study was to evaluate the association between the type, size, and location of lumbar Modic changes (MC), and prolonged disabling low back pain (LBP). Summary of Background Data: LBP is the leading cause of disability worldwide and it affects all age- and socioeconomical groups. Only a small proportion of LBP patients are diagnosed with a specific cause: In most cases no single nociceptive cause for the pain can be identified. MC are visualized in magnetic resonance imaging (MRI) as a signal intensity change in vertebral bone marrow and have been proposed to represent a specific degenerative imaging phenotype associated with LBP. MC can be classified into several subtypes, of which inflammatory Type 1 (MC1) is suggested as being more likely to be associated with LBP. Methods: We assessed lumbar MRI (n = 1512) for the presence, type, and size of MC. The associations of MC characteristics with prolonged (≥30 days during the past year) and disabling (bothersomeness of LBP at least 6 on a 0–10 Numeric Rating Scale) LBP, evaluated at the time of imaging at 47 years, were analyzed using binary logistic regression, adjusted for sex, BMI, smoking, educational status, lumbar disc degeneration, and disc herniations. Results: Any MC and MC1 were associated with prolonged disabling LBP (odds ratio [OR] after full adjustments 1.50 [95% confidence interval, CI 1.05–2.15] and 1.50 [95% CI 1.10–2.05], respectively). Furthermore, MC covering the whole anterior-posterior direction or the whole endplate, as well as the height of MC, were significantly associated with prolonged disabling LBP (OR after full adjustments 1.59 [95% CI 1.14–2.20], 1.67 [95% CI 1.13–2.46] and 1.26 [95% CI 1.13–1.42], respectively). Conclusions: Our study showed a significant and independent association between MC and clinically relevant LBP

The effect of zoledronic acid on type and volume of Modic changes among patients with low back pain

Abstract Background: Modic changes (MC) are associated with low back pain (LBP). In this study, we compared changes in size and type of MC, after a single intravenous infusion of 5 mg zoledronic acid (ZA) or placebo, among chronic LBP patients with MC on magnetic resonance imaging (MRI), and evaluated whether the MRI changes correlate with symptoms. Methods: All patients (N = 19 in ZA, 20 in placebo) had MRI at baseline (0.23–1.5 T) and at one year (1.5–3 T). We evaluated the level, type and volume of all the MC. The MC were classified into M1 (M1 (100%)), predominating M1 (M1/2 (65:35%)) or predominating M2 (M1/2 (35:65%)), and M2 (M2 (100%)). The first two were considered M1-dominant, and the latter two M2-dominant. Volumes of M1 and M2 were calculated separately for the primary MC, which was assumed to cause the symptoms, and the other MC. We analysed the one-year treatment differences in M1 and M2 volumes using analysis of covariance with adjustments for age, sex, body mass index, and smoking. The correlations between the MRI changes and the changes in LBP symptoms were analysed using Pearson correlations. Results: In the ZA group, 84.2% of patients had M1-dominant primary MC at baseline, compared to 50% in the placebo group (p = 0.041). The primary MC in the ZA group converted more likely to M2-dominant (42.1% ZA, 15% placebo; p = 0.0119). The other MC (15 ZA, 8 placebo) were on average 42% smaller and remained largely M2-dominant. The M1 volume of the primary MC decreased in the ZA group, but increased in the placebo group (−0.83 cm³ vs 0.91 cm³; p = 0.21). The adjusted treatment difference for M1 volume was −1.9 cm³ (95% CI −5.0 to 1.2; p = 0.22) and for M2 volume 0.23 cm³ (p = 0.86). In the MC that remained M1-dominant, volume change correlated positively with increased symptoms in the placebo group, whereas the correlations were negative and weak in the ZA group. Conclusions: Zoledronic acid tended to speed up the conversion of M1-dominant into M2-dominant MC and decrease the volume of M1-dominant MC, although statistical significance was not demonstrated. Trial registration: The registration number in ClinicalTrials.gov is NCT01330238 and the date of registration February 11, 2011

T₂-weighted magnetic resonance imaging texture as predictor of low back pain:a texture analysis-based classification pipeline to symptomatic and asymptomatic cases

Abstract Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population-based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T₂-weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin-echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow-up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4-L5 and L5-S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area-under-curve of 0.91. To conclude, textural features from T₂-weighted magnetic resonance images can be applied in low back pain classification