Heart rate turbulence (HRT) is a characteristic heart rate pattern triggered by a ventricular premature contraction (VPC). It can be used to assess autonomic function and health risk for various conditions, e.g., coronary artery disease or cardiomyopathy. While comparability is essential for scientific analysis, especially for research focusing on clinical application, the methodology of HRT still varies widely in the literature. Particularly, the ECG measurement and parameter calculation of HRT differs, including the calculation of turbulence slope (TS). In this article, we focus on common variations in the number of intervals after the VPC that are used to calculate TS (#TSRR) posing two questions: 1) Does a change in #TSRR introduce noticeable changes in HRT parameter values and classification? and 2) Do larger values of turbulence timing (TT) enabled by a larger #TSRR still represent distinct HRT? We compiled a free-access data set of 1,080 annotated long-term ECGs provided by Physionet. HRT parameter values and risk classes were determined both with #TSRR 15 and 20. A standard local tachogram was created by averaging the tachograms of only the files with the best heart rate variability values. The shape of this standard VPC sequence was compared to all VPC sequences grouped by their TT value using dynamic time warping (DTW) in order to identify HRT shapes. When calculated with different #TSRR, our results show only a little difference between the number of files with enough valid VPC sequences to calculate HRT (<1%) and files with different risk classes (5 and 6% for HRT0-2 and HRTA-C, respectively). In the DTW analysis, the difference between averaged sequences with a specific TT and the standard sequence increased with increasing TT. Our analysis suggests that HRT occurs in the early intervals after the VPC and TS calculated from late intervals reflects common heart rate variability rather than a distinct response to the VPC. Even though the differences in classification are marginal, this can lead to problems in clinical application and scientific research. Therefore, we recommend uniformly using #TSRR 15 in HRT analysis.

1.2 ROC DCM, children * [120] 1.27 Quartile CHF = [187] 1.29 ROC MI * [183] 1.3 ROC MI * [182] 1.42 Quartile CHF = [127] 1.5 Quartile MI = [27] 1.695 ROC Acute decompensatedHF * [185] 2.0 Median Cardiomyopathy * [181] 2.0 ROC MI * [159] 2.12 Quartile CAD * [75] 3.0 Cox regression CardiovascularDisease + [99, 134, 144–146] 3.0 NS CardiovascularDisease + [74, 135] 3.2 ROC MI = [152] 3.32 ROC Diabetesmellitus = [184] 3.95 ROC Marfan syndrome * [13] 4.25 Quartile CAD * [21, 85, 179, 180] 4.9 Decile Unstable angina pectoris + [115] 12.1 ROC ACS * [186] NS ROC Myocardial ischemia * [38]

One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model demonstrates that even otherwise rigorous studies can be hard to reproduce due to missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing rationales behind equations. Many of these issues seem similar to problems that have been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow is independent of the model and can be translated to SBML, CellML, and other languages. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. We found the additional design and documentation effort well justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature.

NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.

Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.

Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context is challenging. To overcome these challenges model quality needs to be addressed at the (software-)engineering level. Instead of just declaring standard modeling languages, researchers need to be aware of the characteristics that make these languages desirable and they need to utilize them consistently. We therefore propose a list of desirable language characteristics and provide guidelines how to incorporate them in a model: In our opinion, a mathematical modeling language used in systems biology should be modular, human-readable, hybrid (i.e. support multiple formalisms), open, declarative, and allow to represent models graphically. We demonstrate the benefits of these characteristics by translating a monolithic model of the human cardiac conduction system to a modular version and extending it with a trigger for premature ventricular contractions. For this task we use the modeling language Modelica as an example, that has all the aforementioned characteristics, but is not well known in systems biology. Our experiment illustrates how each characteristic can have a substantial effect on the quality and reusability of the resulting model. When applied consistently, they facilitate and simplify the creation and especially the extension of the modular model. We therefore recommend to consider these guidelines when choosing a programming language for any biological modeling task.

Heart rate turbulence (HRT) is a biphasic reaction to a ventricular premature contraction (VPC) mainly mediated by the baroreflex. It can be used for risk stratification in different disease patterns. Despite existing standards there is a lot of variation in terms of measuring and calculating HRT, which complicates research and application. Objective: This systematic review outlines and evaluates the methodological spectrum of HRT research, especially filtering criteria, parameter calculation and thresholds. Approach: The analysis includes all research papers written in English that have been published before 12.10.2018, are listed on PubMed and involve calculation of HRT parameter values. Main results: HRT assessment is still being performed in various ways and important specifications of the methodology are not given in many articles. Nevertheless, some suggestions regarding HRT methodology can be made: a normalised turbulence slope should be used to uncouple the parameter from heart rate and frequency of extrasystoles. Filtering criteria as formerly reviewed in the guidelines should be met and mentioned. The minimal number of VPC snippets (VPCSs) as well as new cut-off values for different risks need to be further evaluated. Most importantly, the exact and complete methodology must be described to ensure reproducibility and comparability. Significance: Methodical variation hinders comparability of research and medical application. Our continuing questions help to further standardise the measurement and calculation of HRT and increase its value for medical risk stratification.

The Hodgkin-Huxley model of the squid giant axon has been used for decades as the basis of many action potential models. These models are usually communicated using just a list of equations or a circuit diagram, which makes them unnecessarily complicated both for novices and for experts. We present a modular version of the Hodgkin-Huxley model that is more understandable than the usual monolithic implementations and that can be easily reused and extended. Our model is written in Modelica using software engineering concepts, such as object orientation and inheritance. It retains the electrical analogy, but names and explains individual components in biological terms. We use cognitive load theory to measure understandability as the amount of items that have to be kept in working memory simultaneously. The model is broken down into small self-contained components in human-readable code with extensive documentation. Additionally, it features a hybrid diagram that uses biological symbols in an electrical circuit and that is directly tied to the model code. The new model design avoids many redundancies and reduces the cognitive load associated with understanding the model by a factor of 6. Extensions can be easily applied due to an unifying interface and inheritance from shared base classes. The model can be used in an educational context as a more approachable introduction to mathematical modeling in electrophysiology. Additionally the modeling approach and the base components can be used to make complex Hodgkin-Huxley-type models more understandable and reusable.

Modelica models can have a graphical icon defined as a bitmap or vector graphics. Vector graphics have several benefits, the most obvious being free scaling of images from icon to poster size. With OpenModelica there already exists one open source tool that can be used for editing these vector graphics icon annotations, but it does not reach the usability comfort of professional vector graphics editing tools. In this paper we present the Modelica Vector graphics Editor (MoVE), a standalone open source editor for Modelica’s vector graphics syntax that provides many convenience features inspired by the vector graphics editor Inkscape. These features include grouping, snap to grid, move to foreground/background, rotation handles, and drawing perfect circles and squares as well as horizontal and vertical lines when holding Shift. We hope that MoVE, as a part of the Modelica Tool Ensemble (MoTE), can enrich the open source ecosystem of Modelica by simplifying the creation of more elaborate vector graphics icons for Modelica models.

The Modelica language is becoming increasingly popular among scientists and engineers as platform for modelling physical or biological systems. Although Modelica is maintained as non-proprietary language by the Modelica Association, a considerable number of commercial implementations and development environments is complemented by a surprisingly small number of open source tools.

Both a Phonocardiogram (PCG) and an Electrocardiogram (ECG) are sequential measurements of heart activity used to distinguish normal from abnormal heart function. Although they measure different physical quantities, we show that training a long short-term memory network on the Physionet challenge using only the ECG data available for the MIT heart sounds database still yields a score of 0.74 compared to the reference score of 0.82 for a similar net trained on the PCG data.This finding suggests that it may be valuable to train a transformational neural network to produce an artificial ECG from a PCG. Such a transformational net would allow to harness the know-how of decades of research on ECG classification to improve PCG classification. Unfortunately, this task seems too hard for current state-of-the art architectures for neural networks given the data of the Physionet challenge 2016. However, it may be worthwhile to further pursue this approach using data with less variance in the ECG signals or a specialized network architecture.

A simulator of the baroreflex loop is implemented as a distributed system, including independent functional units with each of them running without synchronisation and in real-time. Individual components are build from extended equations of the well established Seidel-Herzel-model. The system includes Jive small computers representing Jive independent sub-models. Each component has a computer mouse connected that allow for real-time manipulation of simulation parameters in the respective part of the model. This way, numerical values of variables, such as neurotransmitter concentrations or breathing frequency, can easily be altered by turning the associated adjusting wheel. Virtual administration of drug substances and virtual disease simulations are performed and show that the asynchronous simulation is robust enough to be used as an intuitive model to study heart rate dynamics.

Systems biology is a field that requires complex multiscale models of systems that are evolved rather than engineered.No unifying theory exists for biology as it does for engineering domains.Thus, models appear in very diverse forms.Components can be genes, cells, organs or even whole ecosystems.These components can intuitively be represented as classes in an object-oriented language, making systems biology a perfect application for Modelica.However, we still only see very few models from this domain.In an attempt to change this, we show that Modelica can exactly reproduce the simulation results of a reference implementation of an established biological model of the human baroreflex.Our implementation highlights the strengths of Modelica like the event finding mechanism, which makes the model more precise.We also show that biological systems pose interesting challenges like signals with non-uniform delays and the interaction of complex rhythms.

In this paper a method for Early Recognition (ER) of Motion Templates (MTs) is presented. We define ER as an algorithm to provide recognition results before a motion sequence is completed. In our experiments we apply Long Short-Term Memory (LSTM) and optimize the training for the task of recognizing the motion template as early as possible. The evaluation has shown that the recognition accuracy for a frame-by-frame classification the LSTM achieves a recognition accuracy of 88% if no training data of the person him/herself is included, and 92% if the training data also contains motion sequences of the person. Furthermore, the average earliness - the number of time frames it takes before the LSTM correctly classifies a motion pattern - is around 24.77 frames, which is less than a second with the used tracking technology, i.e., the Microsoft Kinect.

This paper proposes a new approach for drawing mode detection in online handwriting. The system classifies groups of ink traces into several categories. The main contributions of this work are as follows. First, we improve and optimize several state-of-the-art recognizers by adding new features and applying feature selections. Second, we use several classifiers for the recognition. Third, we perform multiple classifier combination strategies for combining the outputs. Finally, a large experimental evaluation on two data sets is performed: the publicly available Touch&Write database which has been acquired on a pen-enabled multi-touch surface, and the publicly available IAMonDo-database which serves as a benchmark. In our experiments on the IAM-OnDo-database we achieved a recognition rate of 97%, which is much higher than other results reported in the literature. On the more balanced multi-touch surface data set we achieved a recognition rate of close to 98%.