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In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

There was an immediate ad-hoc response of the international scientific community to help scholars from universities affected by Russia's war in Ukraine. Official government-backed funding programmes later allowed the ad-hoc help offers to be sustainable and stable. The #ScienceForUkraine initiative is a grass-root initiative that emerged out of the desire to help; initially created as a central database for the worldwide offers for help. Its 133 active volunteers engaged with policy-makers and funding bodies to improve support to the Ukrainian academic community. The website scienceforukraine.eu became a central and well-known hub with a general database for help listings but also extensive curated link lists for further help. There were more than 120k visits in the past three months, 12k of which are reportedly from Ukraine. #ScienceForUkraine has many active country groups that flexibly respond to needs in their geographic area. Collecting information, creating posters for border stations, mentoring, consulting on how to organise transfers, and engaging with policy makers are some of the tasks performed by members. #ScienceForUkraine is active on all major social networks to be in touch with both the Western academic community (primarily Twitter and LinkedIn) and the Ukrainian academic community (mainly Facebook and Telegram); whereas students are reached mostly via Instagram. The initiative fulfils an important function by transmitting information from one sphere to the other. #ScienceForUkraine recorded well over 2,600 support listings (one listing may be directed at several scholars). 15% of these were offered by German institutions (410), followed by French (227) and Polish (183) institutions. no ISSN ispartof: SSRN vol:22-13 pages:1-44 status: published

The presented computational pipeline is designed to analyze drug-induced changes in EEG data from the Temple University EEG Corpus. The data is cleaned from artifacts, pre-processed, the averaged absolute and relative frequency powers are calculated and compared to a control group. Thus, different research hypotheses can be tested with the intention to reuse accessible data collections.

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setup of several shallow artificial neural networks aggregated via voting results in accuracy of 81%. Stepwise simplification of the model shows the expected decrease in accuracy, but a naive model with thresholding of a single extracted feature (relative wavelet energy) is still able to achieve 75%, which remains strongly above the random guess baseline of 54%. These results suggest the feasibility of building a simple classification model ensuring accuracy scores close to the state-of-the-art research but remaining fully interpretable.

openMNGlab is an open-source software framework for data analysis, tailored for the specific needs of microneurography - a type of electrophysiological technique particularly important for research on peripheral neural fibers coding. Currently, openMNGlab loads data from Spike2 and Dapsys, which are two major data acquisition solutions. By building on top of the Neo software, openMNGlab can be easily extended to handle the most common electrophysiological data formats. Furthermore, it provides methods for data visualization, fiber tracking, and a modular feature database to extract features for data analysis and machine learning.

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setup of several shallow artificial neural networks aggregated via voting results in accuracy of 81%. Stepwise simplification of the model shows the expected decrease in accuracy, but a naive model with thresholding of a single extracted feature (relative wavelet energy) is still able to achieve 75%, which remains strongly above the random guess baseline of 54%. These results suggest the feasibility of building a simple classification model ensuring accuracy scores close to the state-of-the-art research but remaining fully interpretable.

openMNGlab is an open-source software framework for data analysis, tailored for the specific needs of microneurography – a type of electrophysiological technique particularly important for research on peripheral neural fibers coding. Currently, openMNGlab loads data from Spike2 and Dapsys, which are two major data acquisition solutions. By building on top of the Neo software, openMNGlab can be easily extended to handle the most common electrophysiological data formats. Furthermore, it provides methods for data visualization, fiber tracking, and a modular feature database to extract features for data analysis and machine learning.

Background Sleep disorders have a prevalence of up to 50% and are commonly diagnosed using polysomnography. However, polysomnography requires trained staff and specific equipment in a laboratory setting, which are expensive and limited resources are available. Mobile and wearable devices such as fitness wristbands can perform limited sleep monitoring but are not evaluated well. Here, the development and evaluation of a mobile application to record and synchronize data from consumer-grade sensors suitable for sleep monitoring is presented and evaluated for data collection capability in a clinical trial. Methods Wearable and ambient consumer-grade sensors were selected to mimic the functionalities of clinical sleep laboratories. Then, a modular application was developed for recording, processing and visualizing the sensor data. A validation was performed in three phases: (1) sensor functionalities were evaluated, (2) self-experiments were performed in full-night experiments, and (3) the application was tested for usability in a clinical trial on primary snoring. Results The evaluation of the sensors indicated their suitability for assessing basic sleep characteristics. Additionally, the application successfully recorded full-night sleep. The collected data was of sufficient quality to detect and measure body movements, cardiac activity, snoring and brightness. The ongoing clinical trial phase showed the successful deployment of the application by medical professionals. Conclusion The proposed software demonstrated a strong potential for medical usage. With low costs, it can be proposed for screening, long-term monitoring or in resource-austere environments. However, further validations are needed, in particular the comparison to a clinical sleep laboratory.

One of the important questions in the research on neural coding is how the preceding axonal activity affects the signal propagation speed of the following one. We present an approach to solving this problem by introducing a multi-level spike count for activity quantification and fitting a family of linear regression models to the data. The best-achieved score is R2=0.89 and the comparison of different models indicates the importance of long and very short nerve fiber memory. Further studies are required to understand the complex axonal mechanisms responsible for the discovered phenomena.

The authors wish to make the following erratum to this paper [...]

In this manuscript we propose a novel method to compare simultaneously recorded electroencephalography (EEG) signals from different devices. Although standard methods like correlation and spectral analysis give quantitative answers to this question, these methods often penalize certain artifacts such as eye blinking too strongly. In our analysis we instead utilize an unsupervised labeling technique to evaluate the matching of two signals by comparing their label sequences. The proposed method was successfully tested on artificial data, where it showed a reduced deviation from the ground truth compared to the correlation coefficient. Furthermore, the method was applied on a real use-case to assess the quality of a low-cost EEG device compared to a clinical one. Here it showed more consistent results than the correlation coefficient, while it also did not rely on outlier removal prior to the analysis. However, the proposed method still suffers from accidental matches of labels, so that unrelated data sets may be assigned an unexpectedly high matching score. This paper suggests extensions to the proposed method, which could improve this issue.

Background Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel’s holiday planning. Methods We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May–September). Results An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. Conclusions The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.

Intensive development of e-learning methods still struggles with domains where feedback on manual and physical skills is necessary, for example, crafts or physiotherapy movements. Most of such training is currently done exclusively through direct teacher-student interaction. The traditional approach minimizes the possibilities for remote learning, requires large time investments, and contributes to high costs of education. While human feedback remains very important, modern wearable sensors allow to transfer part of the workload to e-learning. In this chapter, we present an overview of available solutions with particular focus on wearable sensors. We argue that wearable devices have the ability to enable a new step in e-learning, not only allowing the acquisition of theoretical knowledge but also training of manual and physical skills.

We consider the hyperbolic generalization of Burgers equation with polynomial source term. The transformation of auto-B\"{a}cklund type was found. Application of the results is shown in the examples, where the pair of two stationary solutions produces kink and bi-kink solutions., Comment: 10 pages, 3 figures

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.

Long term EEG examinations, for example during epilepsy diagnosis, can be performed more efficiently with support of automated abnormality detection. Currently, these methods are usually developed based on one specific database, which limits the possibilities of generalizations. Here, we present a machine learning solution for detection of interictal abnormal EEG segments optimized on the publically available TUH Abnormal EEG Corpus. The classifier is further re-trained and tested on several combinations of publicly available data sets. The results achieved internally on the datasets are comparable to the known state of the art, while training and testing on different sources produced accuracy in the range of 67.51% to 99.50%. Lower accuracy is achieved when the training data set is highly preprocessed and relatively small.

In recent years the need for informal home care in European countries is growing quickly due to increased life expectancy and demographic change. Informal caregivers have to overcome many obstacles ranging from a lack of adequate training to misjudging their physical and psychological abilities. The aim of this project is to create a low cost wearable device, which unobtrusively measures the physical stress load on caregivers. Two parameters with the most impact on performance and well-being of the caregiver have been identified: (i) fatigue and (ii) back-stress. Based on the measurements, an alert is issued if caregivers are not performing a task correctly, or if they are overexerting themselves. This paper discusses the design of such device and description of an initial prototype, its advantages and possible further development and applications.

We consider a three-dimensional three-time-scale system that was first proposed by Krupa et?al. (2008) under the additional assumption that two singular perturbation parameters are present in the equations. While the presence of three scales was shown to give rise to canard-induced periodic mixed-mode oscillations (MMOs) (Desroches et?al., 2012) in the parameter regime studied by Krupa et?al. (2008), we additionally observe mixed-mode patterns that display delayed-Hopf-type behaviour (Ne?shtadt, 1987). We present analytical and numerical evidence for the occurrence of stable periodic dynamics that realises both mechanisms, and we discuss the transition between them. To the best of our knowledge, the resulting mixed sector-delayed-Hopf-type MMO trajectories represent a novel class of mixed-mode dynamics in singularly perturbed systems of ordinary differential equations.

Lack of proper hand hygiene is a common source of hospital acquired infections. Training and evaluating efficiency in hand washing is therefore an important part of medical education. Here, we propose to use the Myo wearable armband to measure correctness of hand washing for mobile learning. Myo's sensors are designed in order to recognize the activity of the forearm, palm and fingers. Using signal processing and machine learning, the quality of the hand washing process can be estimated and used as evaluation in medical teaching. The project is in its initial phase, thus we present preliminary results and a vision of future development.

Following the paper of Shimizu et al. (Phys Lett A 375:1566, 2011), we consider the Bonhoeffer-van der Pol oscillator with non-autonomous periodic perturbation. We show that the presence of mixed mode oscillations reported in that paper can be explained using the geometric singular perturbation theory. The considered model can be re-written as a four-dimensional (locally three-dimensional) autonomous system, which under certain conditions has a folded saddle-node singularity and additionally can be treated as a three time scale one. Author is very grateful to prof. Peter De Maesschalck for many stimulating discussions. The presented research was supported by the Research Foundation Flanders (FWO) under grant number G.0939.10N and by Polish Ministry of Science and Higher Education.

We consider an extended three-dimensional Bonhoeffer-van der Pol oscillator which generalises the planar FitzHugh-Nagumo model from mathematical neuroscience, and which was recently studied by Sekikawa et al. (Phys Lett A 374(36):3745-3751, 2010) and by Freire and Gallas (Phys Lett A 375:1097-1103, 2011). Focussing on a parameter regime which has hitherto been neglected, and in which the governing equations evolve on three distinct time-scales, we propose a reduction to a model problem that was formulated by Krupa et al. (J Appl Dyn Syst 7(2):361-420, 2008) as a canonical form for such systems. Based on results previously obtained in Krupa et al. (2008), we characterise completely the mixed-mode dynamics of the resulting three time-scale extended Bonhoeffer-van der Pol oscillator from the point of view of geometric singular perturbation theory, thus complementing the findings reported in Sekikawa et al. (2010). In particular, we specify in detail the mixed-mode patterns that are observed upon variation of a bifurcation parameter which is naturally obtained by combining two of the original parameters in the system, and we derive asymptotic estimates for the corresponding parameter intervals. We thereby also disprove a conjecture of Tu (SIAM J Appl Math 49(2): 331-343, 1989), where it was postulated that no stable periodic orbits of mixed-mode type can be observed in an equivalent extension of the Bonhoeffer-van der Pol equations.