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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.

Sensors 21(15), 5205 (2021). doi:10.3390/s21155205 special issue: "Special Issue "EEG Sensors for Biomedical Applications" / Special Issue Editors: Prof. Dr. Filippo Zappasodi, Guest Editor; Prof. Dr. Silvia Comani, Guest Editor; Dr. Patrique Fiedler, Guest Editor", Published by MDPI, Basel

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 [...]

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.