Identifying experts in the field of visual arts using oculomotor signals

In this article, we aimed to present a system that enables identifying experts in the field of visual art based on oculographic data. The difference between the two classified groups of tested people concerns formal education. At first, regions of interest (ROI) were determined based on position of fixations on the viewed picture. For each ROI, a set of features (the number of fixations and their durations) was calculated that enabled distinguishing professionals from laymen. The developed system was tested for several dozen of users. We used k-nearest neighbors (k-NN) and support vector machine (SVM) classifiers for classification process. Classification results proved that it is possible to distinguish experts from non-experts

Joint Time-Frequency And Wavelet Analysis - An Introduction

A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f) algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in this paper. Unfortunately, there is no correlation between the width of the time-frequency window and its frequency content in the t/f analysis. This property is not valid in the case of a wavelet transform. A wavelet is a wave-like oscillation, which forms its own “wavelet window”. Compression of the wavelet narrows the window, and vice versa. Individual wavelet functions are well localized in time and simultaneously in scale (the equivalent of frequency). The wavelet analysis owes its effectiveness to the pyramid algorithm described by Mallat, which enables fast decomposition of a signal into wavelet components

A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal

Regulation of subcellular localization of muscle FBPase in cardiomyocytes. The decisive role of calcium ions

Glyconeogenesis, the synthesis of glycogen from carbohydrate precursors like lactate, seems to be an important pathway participating in replenishing glycogen in cardiomyocytes. Fructose-1,6-bisphosphatase (FBPase), an indispensible enzyme of glyconeogenesis, has been found in cardiomyocytes on the Z-line, in the nuclei and in the intercalated discs. Glyconeogenesis may proceed only when FBPase accumulates on the Z-line. Searching for the mechanism of a FBPase regulation we investigated the effects of the calcium ionophore A23187, a muscle relaxant dantrolene, glucagon, insulin and medium without glucose on the subcellular localization of this enzyme in primary culture of neonatal rat cardiomyocytes. Immunofluorescence was used for protein localization and the intracellular calcium concentration was measured with Fura. We found that the concentration of calcium ions was the decisive factor determining the localization of muscle FBPase on the Z-line. Calcium ions had no effect on the localization of the enzyme in the intercalated discs or in the nuclei, but accumulation of FBPase in the nuclei was induced by insulin

Eye-Tracking Analysis for Emotion Recognition

This article reports the results of the study related to emotion recognition by using eye-tracking. Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments. Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter. To ensure that the features were related to emotions, we investigated the influence of luminance and the dynamics of the presented movies. Three classes of emotions were considered: high arousal and low valence, low arousal and moderate valence, and high arousal and high valence. A maximum of 80% classification accuracy was obtained using the support vector machine (SVM) classifier and leave-one-subject-out validation method

Registration and Analysis of Acceleration Data to Recognize Physical Activity

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