People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiological signals in combination with machine learning to detect physiological changes related to stress. Four experiments were conducted to assess if the system could detect stress in people with and without ID. Three experiments were conducted with people without ID (n = 14, n = 18, and n = 48), and one observational study was done with people with ID (n = 12). To analyze if the system could detect stress, the performance of random, general, and personalized models was evaluated. The mixed ANOVA found a significant effect for model type, F(2, 134) = 116.50, p < .001. Additionally, the post-hoc t-tests found that the personalized model for the group with ID performed better than the random model, t(11) = 9.05, p < .001. The findings suggest that the personalized model can detect stress in people with and without ID. A larger-scale study is required to validate the system for people with ID.

This paper presents the results of the development of a new sock garment with integrated electrodes for monitoring physiological signals for stress detection in people with intellectual disabilities or dementia. Misunderstood stress-induced behaviours reduce the quality of life of these individuals and complicate caregiver support and treatment, as the correct interpretation of these behaviours. One of the physiological parameters most related to stress is electrodermal activity (EDA). It shows a direct response to the sympathetic nervous system activation ('fight or flight' response) in the form of a change in skin electrical properties such as skin conductance (SC) or skin impedance (SI). The phasic component of EDA is associated with short-term events and occurs in the presence of stimuli that control sweat gland activity. Therefore, analysis of this signal can be used as an indicator of emotional arousal or stress.To continuously measure EDA on an individual, a comfortable, durable, and easy-to-use carrier is essential. Current medical electrode patches (carriers) have limited user-friendliness because of their large shape and risk of skin irritation during extended use. Besides, the daily disposal of electrode patches would pose a major supply chain challenge and generate large amounts of medical waste. Furthermore, depending on the target group, classic wrist sensors may not be accepted by patients due to their discomfort and removed during recording. Considering the above limitations, a garment sock with integrated electrodes was proven to be the most efficient location in terms of signal quality, comfort, and an optimal alternative to standard medical electrodes. This allows the electrodes to be applied in one handling while maintaining permanent spacing and positioning of the electrodes on the skin. This garment can also be reused several times after regular washing cycles. Screen printing was chosen as a method for incorporating conductive electrodes onto garments. Conductive inks can be printed onto the garment directly or onto a thermoplastic polyurethane (TPU) film, which has been proven to be a suitable material for this type of integration. Screen printing onto these films offers both high flexibility and stretchability. The printing process allows the use of complex designs, such as stacking layers and printing dielectric insulating layers on top of the conductive layers. Different types of connectors were studied and designed to convert this stretchable film into a fixed connector tail with strain relief. Finally, test prints were made in a lab to validate each material and ink combination of silver, carbon, and dielectric inks. This aim was to achieve the desired robustness, and flexibility and to optimise the position of the sensors to achieve a good balance between patient comfort and good EDA signal output.The work showed that the use of advanced screen-printing technologies in the smart sock was the best solution to ensure high wear comfort while maintaining good signal quality even after repeated use and washing while maintaining low costs and high flexibility during production. In addition, the sheet-to-sheet production method proved to be cost-effective and enabled rapid changes in the material stack and sock design.

Abstract Objective To assess the performance of a multimodal seizure detection device, first tested in adults (sensitivity 86%, PPV 49%), in a pediatric cohort living at home or residential care. Methods In this multicenter, prospective, video‐controlled cohort‐study, nocturnal seizures were detected by heartrate and movement changes in children with epilepsy and intellectual disability. Participants with a history of >1 monthly major motor seizure wore Nightwatch bracelet at night for 3 months. Major seizures were defined as tonic–clonic, generalized tonic >30 s, hyperkinetic, or clusters (>30 min) of short myoclonic or tonic seizures. The video of all events (alarms and nurse diaries) and about 10% of whole nights were reviewed to classify major seizures, and minor or no seizures. Results Twenty‐three participants with focal or generalized epilepsy and nightly motor seizures were evaluated during 1511 nights, with 1710 major seizures. First 1014 nights, 4189 alarms occurred with average of 1.44/h, showing average sensitivity of 79.9% (median 75.4%) with mean PPV of 26.7% (median 11.1%) and false alarm rate of 0.2/hour. Over 90% of false alarms in children was due to heart rate (HR) part of the detection algorithm. To improve this rate, an adaptation was made such that the alarm was only triggered when the wearer was in horizontal position. For the remaining 497 nights, this was tested prospectively, 384 major seizures occurred. This resulted in mean PPV of 55.5% (median 58.1%) and a false alarm rate 0.08/h while maintaining a comparable mean sensitivity of 79.4% (median 93.2%). Significance Seizure detection devices that are used in bed which depend on heartrate and movement show similar sensitivity in children and adults. However, children do show general higher false alarm rate, mostly triggered while awake. By correcting for body position, the false alarms can be limited to a level that comes close to that in adults.

OBJECTIVE:To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement.METHODS:In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers' user experience.RESULTS:Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65-0.89). Median sensitivity per participant amounted to 86% (95% CI 77%-93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01-0.05); and the positive predictive value was 49% (95% CI 33%-64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%-80%, p < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively.CONCLUSION:Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.

BACKGROUND:Heartrate variability biofeedback (HRVB) is a non-invasive treatment in which patients are assumed to self-regulate a physiological dysregulated vagal nerve. Although the therapeutic approach of HRVB is promising in various stress-related disorders, it has only been offered on a regular basis in a few mental health treatment settings.AIM:To analyse the efficacy of HRV biofeedback as an additional psychophysiological treatment for depression and PTSD.METHOD:Systematic review with search terms HRV, biofeedback, PTSD, depression, panic disorder and anxiety disorder.RESULTS:Our search of the literature yielded 789 studies. After critical appraisal using the GRADE method, we selected 6 randomised controlled trials (RCTs) and 4 relevant studies. The RCTs with control groups 'treatment as usual' and muscle relaxation training revealed significant clinical efficacy and better results than control conditions after 4 to 8 weeks training.CONCLUSION:Although this systematic review shows the popularity of HRV in literature, it does not indicate that HRVB really has been reviewed systematically. Significant outcomes of this limited number of randomised studies indicate there may be a clinical improvement when HRVB training is integrated into treatment of PTSD and depression, particularly when this integration procedure is combined with psychotherapy. More research needs to be done with larger groups and further efforts are needed to integrate HRVB into treatment of stress-related disorders in psychiatry. Future research also needs to focus on the psychophysiological mechanisms involved.

Electroencephalography (EEG) is paramount for both retrospective analysis and real-time monitoring of epileptic seizures. Studies have shown that EEG-based seizure detection is very difficult for a specific epileptic population with intellectual disability due to the cerebral development disorders. In this work, a seizure detection method based on dynamic warping (DW) is proposed for patients with intellectual disability. It uses an EEG template of an individual subject's dominant seizure type, to extract the morphological features from EEG signals. A linear discriminant analysis (LDA) classifier is used to perform the seizure detection. Results show that the DW-based feature in the frequency domain is superior than that in the time domain, and the features extracted using wavelet transform method.

Objective: Psychogenic nonepileptic seizures (PNESs) resemble epileptic seizures but originate from psychogenic rather than organic causes. Patients with PNESs are often unable or unwilling to reflect on underlying emotions. To gain more insight into the internal states of patients during PNES episodes, this study explored the time course of heart rate variability (HRV) measures, which provide information about autonomic nervous system functioning and arousal.Methods: Heart rate variability measures were extracted from double-lead electrocardiography data collected during 1-7 days of video-electroencephalography monitoring of 20 patients with PNESs, in whom a total number of 118 PNESs was recorded. Heart rate (HR) and HRV measures in time and frequency domains (standard deviation of average beat-to-beat intervals (SDANN), root mean square of successive differences (RMSSD), high-frequency (HF) power, low-frequency (LF) power, and very low-frequency (VLF) power) were averaged over consecutive five-minute intervals. Additionally, quantitative analyses of Poincare plot parameters (SD1, SD2, and SD1/SD2 ratio) were performed.Results: In the five-minute interval before PNES, HR significantly (p=0.05) increased (d=2.5), whereas SDANN (d=-0.03) and VLF power (d=-0.05) significantly decreased. During PNES, significant increases inHF power (d=0.0006), SD1 (d=0.031), and SD2 (d=0.016) were observed. In the five-minute interval immediately following PNES, SDANN (d=0.046) and VLF power (d=0.073) significantly increased, and HR (d=-5.1) and SD1/SD2 ratio (d=-0.14) decreased, compared to the interval preceding PNES.Conclusion: The results suggest that PNES episodes are preceded by increased sympathetic functioning, which is followed by an increase in parasympathetic functioning during and after PNES. Future research needs to identify the exact nature of the increased arousal that precedes PNES. (C) 2015 Elsevier Inc. All rights reserved.

In August 2015, one of our partners in the regional government pointed out to us that the MarathonMarathon of Eindhoven that year was subsidized based on an innovation clause.

We investigated the effect of exposure to bright white light as compared to a commonly experienced illuminance (1000lx vs. 200lx at eye level, 4000K) on electroencephalography spectral power density during daytime. Spectral power density was measured during one hour of exposure in the morning and in the afternoon. Results showed a lower relative power density in the theta range under bright light. In the morning, relative alpha power was also lower under exposure to 1000lx. The current findings extend earlier results on the effect of illuminance on alertness and arousal in the late evening and at night. Moreover, they largely corroborate results on subjective experience and sustained attention during daytime, and together suggest higher alertness under brighter light even for daytime exposure in everyday situations.

Mental retardation (MR) is one of the most common secondary disabilities in people with Epilepsy. However, to our knowledge there are no reliable seizure detection methods specified for MR-patients. In this paper we performed a pilot study on a group of six patients with mental retardation to assess what EEG features potentially work well on this group. A group of EEG features on the time, frequency and spatio-temporal domain were extracted, the modified wrapper approach was then employed as an improved feature subset selection method. Results show high variance on obtained features subset across this group, meanwhile there exist some common features which characterize the high-frequency components of epileptic EEG signals.

Objective: Psychogenic non-epileptic seizures (PNES) are epilepsy-like episodes which have an emotional rather than organic origin. Although PNES have often been related to the process of dissociation, the psychopathology is still poorly understood. To elucidate underlying mechanisms, the current study applied independent component analysis (ICA) on resting-state fMRI to investigate alterations within four relevant networks, associated with executive, fronto-parietal, sensorimotor, and default mode activation, and within a visual network to examine specificity of between-group differences.Methods: Twenty-one patients with PNES without psychiatric or neurologic comorbidities and twenty-seven healthy controls underwent resting-state functional MR imaging at 3.0T (Philips Achieva). Additional neuropsychological testing included Raven's Matrices test and dissociation questionnaires. ICA with dual regression was used to identify resting-state networks in all participants, and spatial maps of the networks of interest were compared between patients and healthy controls.Results: Patients displayed higher dissociation scores, lower cognitive performance and increased contribution of the orbitofrontal, insular and subcallosal cortex in the fronto-parietal network; the cingulate and insular cortex in the executive control network; the cingulate gyrus, superior parietal lobe, pre- and postcentral gyri and supplemental motor cortex in the sensorimotor network; and the precuneus and (para-) cingulate gyri in the default-mode network. The connectivity strengths within these regions of interest significantly correlated with dissociation scores. No between-group differences were found within the visual network, which was examined to determine specificity of between-group differences.Conclusions: PNES patients displayed abnormalities in several resting-state networks that provide neuronal correlates for an underlying dissociation mechanism. (C) 2014 Elsevier Ltd. All rights reserved.

OBJECTIVE:To quantify the neuronal connectivity in preterm infants between homologous channels of both hemispheres. METHODS:EEG coherence analysis was performed on serial EEG recordings collected from preterm infants with normal neurological follow-up. The coherence spectrum was divided in frequency bands: δnewborn(0-2 Hz), θnewborn(2-6 Hz), αnewborn(6-13 Hz), βnewborn(13-30 Hz). Coherence values were evaluated as a function of gestational age (GA) and postnatal maturation. RESULTS:All spectra show two clear peaks in the δnewborn and θnewborn-band, corresponding to the delta and theta EEG waves observed in preterm infants. In the δnewborn-band the peak magnitude coherence decreases with GA and postnatal maturation for all channels. In the θnewborn-band, the peak magnitude coherence decreases with GA for all channels, but increases with postnatal maturation for the frontal polar channels. In the βnewborn-band a modest magnitude coherence peak was observed in the occipital channels, which decreases with GA. CONCLUSIONS:Interhemispherical connectivity develops analogously with electrocortical maturation: signal intensities at low frequencies decrease with GA and postnatal maturation, but increase at high frequencies with postnatal maturation. In addition, peak magnitude coherence is a clear trend indicator for brain maturation. SIGNIFICANCE:Coherence analysis can aid in the clinical assessment of the functional connectivity of the infant brain with maturation.

The strong capability of the combined EEG-fMRI for investigating and revealing new insights on mapping of the brain activity as well as on several other neuroscientific studies has attracted the interest of researchers and clinicians over the past years. However, its consolidation as a powerful and independent technique still depends on enhancing the quality of the EEG signal, mainly due to the occurrence of artefacts. This paper presents a simple and effective approach for removal of the gradient artefact, which is induced in the EEG by the rapidly varying gradient magnetic fields of the fMRI scanner. According to our method, a moving-average filter is used to perform the removal of the gradient artefact. Nevertheless, rather than estimation of an artefact waveform template to be subtracted and achieve the EEG restoration, we have proposed to optimize the moving-average filtering process along the entire EEG excerpt. Thereby, the restored EEG can be estimated either from a sum of partial waveform components resulting from the recursive application of the optimized moving-average filter; or from an estimative of the artefact along the entire excerpt. Our methodology shows to achieve a quite satisfactory restoration of the EEG signal, even for low signal amplitudes. Moreover, in addition to predict the variability of the artefact waveform over the time, synchronization between EEG and fMRI clocks and extensive data segmentation are not required as well.

Objective Dissociation is a mental process with psychological and somatoform manifestations, which is closely related to hypnotic suggestibility and essentially shows the ability to obtain distance from reality. An increased tendency to dissociate is a frequently reported characteristic of patients with functional neurological symptoms and syndromes (FNSS), which account for a substantial part of all neurological admissions. This review aims to investigate what heart rate variability (HRV), EEG and neuroimaging data (MRI) reveal about the nature of dissociation and related conditions. Methods Studies reporting HRV, EEG and neuroimaging data related to hypnosis, dissociation and FNSS were identified by searching the electronic databases Pubmed and ScienceDirect. Results The majority of the identified studies concerned the physiological characteristics of hypnosis; relatively few investigations on dissociation related FNSS were identified. General findings were increased parasympathetic functioning during hypnosis (as measured by HRV), and lower HRV in patients with FNSS. The large variety of EEG and functional MRI investigations with diverse results challenges definite conclusions, but evidence suggests that subcortical as well as (pre)frontal regions serve emotion regulation in dissociative conditions. Functional connectivity analyses suggest the presence of altered brain networks in patients with FNSS, in which limbic areas have an increased influence on motor preparatory regions. Conclusions HRV, EEG and (functional) MRI are sensitive methods to detect physiological changes related to dissociation and dissociative disorders such as FNSS, and can possibly provide more information about their aetiology. The use of such measures could eventually provide biomarkers for earlier identification of patients at risk and appropriate treatment of dissociative conditions.

This paper presents a novel approach for removing gradient artefacts from the EEG signal recorded during continuous EEG-fMRI, which are influenced by transient head movements of the subject within the magnetic scanner. Transient head movements provoke abrupt changes in the gradient artefact waveform, in such a way that they compromise the estimation of an artefact waveform to be subtracted and achieve the EEG correction. According to our proposed methodology, a cubic spline waveform is used to model and represent the signal transitions components. This model is then used to change and approximate the shape of the EEG signal as homogeneous data, in order to improve the performance of the gradient artefact correction technique. The proposed approach also makes use of the signal slope adaption (SSD) method, combined with sum-of-sinusoids modelling for correction of the gradient artefact. Our methodology reveals to perform a robust and satisfactory removal of gradient artefacts under the occurrence of abrupt transient head movements.

The aim of this study was to evaluate EEG source localization by standardized weighted low-resolution brain electromagnetic tomography (swLORETA) for monitoring of full-term newborns with hypoxic-ischemic encephalopathy, using a standard anatomic head model. Three representative examples of neonatal hypoxic-ischemia were included. The method was validated with MRI data. Hypoxic-ischemic areas, visible on MRI, correlated well with swLORETA current density distributions. In addition, neonatal seizure activity may be localized. The calculated current density distributions provide easy-to-interpret localized information about neonatal brain function, which may enable detailed longitudinal monitoring and potential assessment of treatment efficacy.