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Vitaliy Kolodyazhniy

Vitaliy Kolodyazhniy
Roche · Pharma Research and Early Development (pRED)

PhD
Data Science, Digital Biomarkers, Sleep Research

About

44
Publications
5,652
Reads
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716
Citations
Citations since 2017
4 Research Items
413 Citations
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2017201820192020202120222023020406080
2017201820192020202120222023020406080
2017201820192020202120222023020406080
Introduction
Additional affiliations
November 2018 - October 2022
Roche
Position
  • Principal Scientist
Description
  • Sleep monitoring-based digital biomarkers in clinical studies
February 2013 - October 2018
Ziemer Ophthalmic Systems AG
Position
  • Group Leader
Description
  • Algorithm design and software development for image-guided femtosecond laser-assisted ocular surgeries.
January 2011 - January 2013
University of Salzburg
Position
  • Senior Researcher
Description
  • Biosignal acquisition and analysis for emotion research.
Education
September 1999 - June 2002
Kharkiv National University of Radioelectronics
Field of study
  • Control Engineering, Computer Science, Computational Intelligence
September 1993 - July 1998
Kharkiv National University of Radioelectronics
Field of study
  • Electrical Engineering, Computer Science, Control Engineering

Publications

Publications (44)
Article
Full-text available
Background: Circadian and sleep-homeostatic mechanisms regulate timing and quality of wakefulness. To enhance wakefulness, daily consumption of caffeine in the morning and afternoon is highly common. However, the effects of such a regular intake pattern on circadian sleep-wake regulation are unknown. Thus, we investigated if daily daytime caffeine...
Preprint
Full-text available
To enhance wakefulness, daily consumption of caffeine in the morning and afternoon is highly common. However, it is unknown whether such a regular intake pattern affects timing and quality of wakefulness, as regulated by an interplay of circadian and sleep-homeostatic mechanisms. Thus, we investigated the effects of daily caffeine intake and its wi...
Article
Full-text available
We tested the effect of different lights as a countermeasure against sleep-loss decrements in alertness, melatonin and cortisol profile, skin temperature and wrist motor activity in healthy young and older volunteers under extendend wakefulness. 26 young [mean (SE): 25.0 (0.6) y)] and 12 older participants [(mean (SE): 63.6 (1.3) y)] underwent 40-h...
Article
Sex differences in emotional reactivity have been studied primarily for negative but less so for positive stimuli; likewise, sex differences in the psychophysiological response-patterning during such stimuli are poorly understood. Thus, the present study examined sex differences in response to negative/positive and high/low arousing films (classifi...
Article
Various studies have assessed autonomic and respiratory underpinnings of panic attacks, yet the psychophysiological functioning of panic disorder (PD) patients has rarely been examined under naturalistic conditions at times when acute attacks were not reported. We hypothesized that emotional activation in daily life causes physiologically demonstra...
Article
Full-text available
Rapid eye movement (REM) sleep has been postulated to facilitate emotional processing of negative stimuli. However, empirical evidence is mixed and primarily based on self-report data and picture-viewing studies. This study used a full-length aversive film to elicit intense emotion on one evening, and an emotionally neutral control film on another...
Article
Full-text available
Sleep is regulated in a time-of-day dependent manner and profits working memory. However, the impact of the circadian timing system as well as contributions of specific sleep properties to this beneficial effect remains largely unexplored. Moreover, it is unclear to which extent inter-individual differences in sleep-wake regulation depend on circad...
Article
Full-text available
Sleep loss affects human behavior in a nonuniform manner, depending on the cognitive domain and also the circadian phase. Besides, evidence exists about stable interindividual variations in sleep loss-related performance impairments. Despite this evidence, only a few studies have considered both circadian phase and neurobehavioral domain when inves...
Article
Full-text available
Recently, we developed a novel method for estimating human circadian phase with noninvasive ambulatory measurements combined with subject-independent multiple regression models and a curve-fitting approach. With this, we were able to estimate circadian phase under real-life conditions with low subject burden, i.e., without need of constant routine...
Conference Paper
Full-text available
A multilayer spline-based fuzzy neural network (MS-FNN) is proposed. It is based on the concept of multilayer perceptron (MLP) with B-spline receptive field functions (Spline Net). In this paper, B-splines are considered in the framework of fuzzy set theory as membership functions such that the entire network can be represented in form of fuzzy rul...
Data
Full-text available
A multilayer spline-based fuzzy neural network (MS-FNN) is proposed. It is based on the concept of multilayer perceptron (MLP) with B-spline receptive field functions (Spline Net). In this paper, B-splines are considered in the framework of fuzzy set theory as membership functions such that the entire network can be represented in form of fuzzy rul...
Article
The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-ne...
Article
Full-text available
Reliable detection of circadian phase in humans using noninvasive ambulatory measurements in real-life conditions is challenging and still an unsolved problem. The masking effects of everyday behavior and environmental input such as physical activity and light on the measured variables need to be considered critically. Here, we aimed at developing...
Conference Paper
Full-text available
This paper presents a digital, transistor level implemented neo-fuzzy neural network. This type of neural network is particularly well suited for real-time applications like those encountered in signal processing and nonlinear system identification. We consider in detail a flexible reconfigurable circuit of a single nonlinear synapse of this networ...
Chapter
The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering, which is realized with a novel robust evolving recursive fuzzy clustering algorithm that can process incoming observations online (possibly in real-time...
Conference Paper
Full-text available
A novel cascaded multiresolution spline-based fuzzy neural network (CMS-FNN) with a very fast constructive training algorithm is introduced. Structurally the CMS-FNN resembles the known cascade-correlation architecture but differs from it in the type of neurons as well as in the training algorithm. The neurons in the CMS-FNN are a generalization of...
Data
Full-text available
A novel cascaded multiresolution spline-based fuzzy neural network (CMS-FNN) with a very fast constructive training algorithm is introduced. Structurally the CMS-FNN resembles the known cascade-correlation architecture but differs from it in the type of neurons as well as in the training algorithm. The neurons in the CMS-FNN are a generalization of...
Conference Paper
Full-text available
A spline-based modification of the previously developed Neuro-Fuzzy Kolmogorov's Network (NFKN) is proposed. In order to improve the approximation accuracy, cubic B-splines are substituted for triangular membership functions. The network is trained with a hybrid learning rule combining least squares estimation for the output layer and gradient desc...
Article
Full-text available
A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an exp...
Conference Paper
Full-text available
We revisit the problem of representing a high-dimensional data set by a distance-preserving projection onto a two-dimensional plane. This problem is solved by well-known techniques, such as multidimensional scaling. There, the data is projected onto a flat plane and the Euclidean metric is used for distance calculation. In real topographic maps, ho...
Chapter
Full-text available
This chapter presents new results on modeling 24 hour (circadian) human heart rate data collected with the LifeShirt system using a variety of linear regression and neural network models. Such modeling is important in biopsychology, chronobiology, and chronomedicine where signals collected continuously from human subjects for one or several days ne...
Conference Paper
The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering which is realized with a novel robust recursive fuzzy clustering algorithm that can process incoming observations online, and is stable with respect to ou...
Chapter
A novel Neuro-Fuzzy Kolmogorov's Network (NFKN) is considered. The NFKN is based on the famous Kolmogorov's superposition theorem (KST) and is the development of the previously proposed Fuzzy Kolmogorov's Network (FKN). Modifications of the FKN architecture include multiple outputs as required for classification problems with more than two classes,...
Chapter
Full-text available
The problem of fuzzy clustering on the basis of the probabilistic and possibilistic approaches under the presence of outliers in data is considered. Robust recursive fuzzy clustering algorithms are proposed, which optimize the objective function suitable for clustering data with heavy-tailed distribution density. Advantages of the proposed algorith...
Chapter
In this chapter, the problems of identification, modeling, and forecasting of chaotic signals are discussed. These problems are solved with the use of the conventional techniques of computational intelligence as radial basis neural networks and learning neuro-fuzzy architectures, as well as novel hybrid structures based on the Kolmogorov’s superpos...
Conference Paper
A recursive learning algorithm based on the rough sets approach to parameter estimation for radial basis function neural networks is proposed. The algorithm is intended for the pattern recognition and classification problems. It can also be applied to neuro control, identification, and emulation.
Conference Paper
Full-text available
A new computationally efficient learning algorithm for a hybrid system called further Neuro-Fuzzy Kolmogorov’s Network (NFKN) is proposed. The NFKN is based on and is the development of the previously proposed neural and fuzzy systems using the famous superposition theorem by A.N. Kolmogorov (KST). The network consists of two layers of neo-fuzzy ne...
Conference Paper
In the paper, a novel Neuro-Fuzzy Kolmogorov’s Network (NFKN) is considered. The NFKN is based on and is the development of the previously proposed neural and fuzzy systems using the famous Kolmogorov’s superposition theorem (KST). The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer param...
Conference Paper
Full-text available
In the paper, a fuzzy filter with finite impulse response (FIR) is considered. The filter is based on the neo-fuzzy neuron architecture. A modification of the neo-fuzzy neuron is proposed, which can be implemented on the basis of conventional FIR-filters and contains fewer membership functions in comparison with the basic architecture. A practical...
Conference Paper
Full-text available
A novel fuzzy neural network, called Fuzzy Kolmogorov’s Network (FKN), is proposed. The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and computationally efficient procedures. The validity of theoretical results and the advantages of the F...
Conference Paper
Full-text available
A nonlinear self-tuning controller is proposed, which realizes the generalized minimum variance control law. Neo-fuzzy neuron is used as the nonlinear plant model. Zero steady-state error in control of nonlinear systems with a priori unknown structure and parameters is guaranteed. The advantages of the proposed approach consist in lower computation...
Conference Paper
A novel fuzzy neural network, called Fuzzy Kolmogorov’s Network (FKN), is considered. The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and computationally efficient procedures. Two-level structure of the rule base helps the FKN avoid the...
Conference Paper
A combined learning algorithm for a self-organizing map (SOM) is proposed. The algorithm accelerates information processing due to the rational choice of the learning rate parameter, and can work when the number of clusters is unknown, as well as when the clusters are overlapping. This is achieved via the introduction of fuzzy inference that deter...
Article
Full-text available
The article addresses the problem of adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both the antecedent and consequent parts of the fuzzy rules is proposed. The algorithm is derived from the Hartley and Marquardt methods. A characteristic feature of the proposed algorithm is t...
Conference Paper
In this paper, an architecture of a learning probabilistic neural network is considered. A learning algorithm for the non-conventional activation function parameters is proposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function paramete...
Conference Paper
Full-text available
In the paper, a new optimal learning algo- rithm for a neo-fuzzy neuron (NFN) is pro- posed. The algorithm is characteristic in that it provides online tuning of not only the synaptic weights, but also the member- ship functions parameters. The proposed al- gorithm has both the tracking and filtering properties, so the NFN can be effectively used f...
Conference Paper
Full-text available
In this paper, an architecture of a resource- allocating learning probabilistic neural net- work is considered. Construction and learn- ing algorithms are proposed. The advan- tages of this network lie in the possibility of classification of data with substantially overlapping clusters. The construction al- gorithm significantly reduces the size of...
Chapter
In this paper, an architecture of a fuzzy probabilistic neural network is considered. A learning algorithm for the activation function parameters is proposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accu...
Conference Paper
The paper addresses the problem of online adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both antecedent and consequent parts of fuzzy rules is proposed. The algorithm is derived from the well-known Marquardt procedure and uses approximation of the Hessian matrix. A characteri...

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Projects

Project (1)
Archived project
Investigation of the circadian clocks in single cells and in humans. For more details, see www.euclock.org