Nikola Jajcay

Technische Universität Berlin | TUB · Department of Software Engineering and Theoretical Computer Science

Background More than 64 million patients with heart failure (HF) are burdened with poor quality of life, frequent hospitalizations, and high readmission rates. While HF with reduced ejection fraction (HFrEF) accounts for approximately half of these patients, there is currently no effective tool for performing cost-effective and reliable early-stage...

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Background: Cardiogenic shock (CS) complicating acute coronary syndrome (ACS) is a life-threatening condition with mortality reaching 50% despite the use of mechanical circulatory support devices (MCS). It is hypothesized that early implantation of MCS before hemodynamic deterioration could prevent CS. For this purpose, we have developed and extern...

Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational m...

Purpose: The incidence of acute myocardial infarctions (AMI) shows circadian variation typically peaking during morning hours with a decline at night. However, this variation does not occur in patients with diabetes mellitus (DM). The night's decline of AMI may be partially explained by melatonin-related platelet inhibition. Whether this effect is...

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used...

Introduction Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The a...

Objectives: Purpose of this study was to evaluate properties of apelin, a peptide detectable in peripheral blood, for atrial fibrillation (AF) detection in a diverse population of patients covering a broad spectrum from healthy to polymorbid patients. Background: AF is the most common cardiac arrhythmia with constantly increasing incidence and p...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ministry of Education, Science, Research and Sport of the Slovak Republic VEGA Background Cardiogenic shock (CS) is a serious life-threatening condition affecting almost 10% of patients suffering from acute coronary syndrome (ACS). Des...

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep...

Computational modeling is a powerful tool for exploring brain mechanisms underlying neurophysiological observations. Nowadays, it is increasingly used to design and evaluate the effect of therapeutic interventions. In this contribution, the computational approach is used to investigate the effects of single pulse and repetitive electrical stimulati...

Artificial intelligence (AI) is here to stay. It is not a future anymore, and there are many particular problems in cardiology that are already being solved via machine learning (ML), and many more are to come. AI cannot solve complex tasks yet, and probably this will not change in the upcoming years. Therefore, cardiologists do not have to be afra...

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectom...

Background: Atrial fibrillation (AF) is associated with high risk of stroke preventable by timely initiation of anticoagulation. Currently available screening tools based on ECG are not optimal due to inconvenience and high costs. Aim of this study was to study the diagnostic value of apelin for AF in patients with high risk of stroke. Methods: We...

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in the memory formation. Here, we analyze a neural mass model of the thalamo-cortical loop of which the cortical node can generate slow oscillations (approx. 1 Hz) while its thalamic component can generate sleep spindl...

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e. the connectome...

Statistical inference of causal interactions and synchronization between dynamical phenomena evolving on different temporal scales is of vital importance for better understanding and prediction of natural complex systems such as the Earth’s climate. This article introduces and applies information theory diagnostics to phase and amplitude time serie...

Earth climate, in general, varies on many temporal and spatial scales. In particular, climate observables exhibit recurring patterns and quasi-oscillatory phenomena with different periods. Although these oscillations might be weak in amplitude, they might have a non-negligible influence on variability on shorter time-scales due to cross-scale inter...

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due...

Nonparametric detection of coupling delay in unidirectionally and bidirectionally coupled nonlinear dynamical systems is examined. Both continuous and discrete-time systems are considered. Two methods of detection are assessed-the method based on conditional mutual information- the CMI method (also known as the transfer entropy method) and the meth...

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure,...

A directed climate network is constructed by Granger causality analysis of air temperature time series from a regular grid covering the whole Earth. Using winner-takes-all network thresholding approach, a structure of a smooth information flow is revealed, hidden to previous studies. The relevance of this observation is confirmed by comparison with...

Earth climate, in general, varies on many temporal and spatial scales. In particular, air temperature exhibits recurring patterns and quasi-oscillatory phenomena with different periods. Although these oscillations are usually weak in amplitude, they might have non-negligible influence on temperature variability on shorter timescales due to cross-sc...

Air temperature variability on different time scales exhibits recurring patterns and quasi-oscillatory phenomena. Climate oscillations with the period about 7–8 years have been observed in many instrumental records in Europe. Although these oscillations are weak if considering their amplitude, they might have non-negligible influence on temperature...

Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Eartha € s climate to volcanic eruptions, extreme events or geoengineering. Here a data-driven approach is introduced based on a dimension reduction, causal reconstruction, and novel networ...

It is well established that the global climate is a complex phenomenon with dynamics driven by the interaction of a multitude of identifiable but intertwined subsystems. The identification, at some level, of these subsystems is an important step towards understanding climate dynamics. We present a method to determine the number of principal compone...

Complicated systems composed of many interacting subsystems are frequently studied as complex networks. In the simplest approach, a given real-world system is represented by an undirected graph composed of nodes standing for the subsystems and non-oriented unweighted edges for interactions present among the nodes; the characteristic properties of t...

Asian summer monsoon is a high-dimensional and highly complex phenomenon affecting more then one fifth of the world population in terms of agriculture, industry and society. It is one of the oldest weather observations and still it is only partially understood and difficult to predict. Asian summer monsoon exhibits wide spectrum of variability on e...

The Asian summer monsoon (ASM) is a high-dimensional and highly complex phenomenon affecting more than one fifth of the world population. The intraseasonal component of the ASM undergoes periods of active and break phases associated respectively with enhanced and reduced rainfall over the Indian subcontinent and surroundings. In this paper the nonl...