Heart rate variability analysis: Higuchi and Katz’s fractal dimensions in subjects with type 1 diabetes mellitus

Background and aims. Statistical markers are valuable when assessing physiological status over periods of time and in certain disease states. We assess if type 1 diabetes mellitus promote modification in the autonomic nervous system using the main two types of algorithms to estimate a Fractal Dimension: Higuchi and Katz. Material and methods. 46 adults were divided into two equal groups. The autonomic evaluation consisted of recording heart rate variability (HRV) for 30 minutes in supine position in absence of any other stimuli. Fractal dimensions ought then able to determine which series of interbeat intervals are derived from diabetics’ or not. We then equated results to observe which assessment gave the greatest significance by One-way analysis of variance (ANOVA1), Kruskal-Wallis technique and Cohen’s d effect sizes. Results. Katz’s fractal dimension is the most robust algorithm when assisted by a cubic spline interpolation (6 Hz) to increase the number of samples in the dataset. This was categorical after two tests for normality; then, ANOVA1, Kruskal-Wallis and Cohen’s d effect sizes (p≈0.01 and Cohen’s d=0.814143–medium effect size). Conclusion. Diabetes significantly reduced the chaotic response as measured by Katz’s fractal dimension. Katz’s fractal dimension is a viable statistical marker for subjectswith type 1 diabetes mellitus

Singularity detection of 2D signals using fractal dimension analysis of scale information

Fractal dimension (FD) analysis has been widely used in signal processing. The key issue in signal processing is the singularity detection. One of the main problems for FD analysis of signals is its susceptibility to measurement noise, likely obscuring the identification of singularities. To address this deficiency, a new physical quantity, named ‘the scale-window fractal dimension (SWFD)’, is proposed and a SWFD analysis method is formed to identify the singularities in the noisy 2D signal. With this method, the noisy 2D signal first is decomposed into sets of scale signals with the aid of 2D Gabor wavelet transforms; then SWFD estimates are calculated along every scale signals to form the FD surface. The singularities can be localized by the sudden changes in the spatial variation of the FD surface. As an application of the method, the identification of damage singularity for an experimental composite plate is performed with the mode shapes measured by a scanning laser vibrometer as the analyzed 2D signals. The results show that the SWFD analysis method has the prominent features of high accuracy of singularity localization and strong robustness to noise

Singularity detection of 2D signals using fractal dimension analysis of scale information

Fractal dimension (FD) analysis has been widely used in signal processing. The key issue in signal processing is the singularity detection. One of the main problems for FD analysis of signals is its susceptibility to measurement noise, likely obscuring the identification of singularities. To address this deficiency, a new physical quantity, named ‘the scale-window fractal dimension (SWFD)’, is proposed and a SWFD analysis method is formed to identify the singularities in the noisy 2D signal. With this method, the noisy 2D signal first is decomposed into sets of scale signals with the aid of 2D Gabor wavelet transforms; then SWFD estimates are calculated along every scale signals to form the FD surface. The singularities can be localized by the sudden changes in the spatial variation of the FD surface. As an application of the method, the identification of damage singularity for an experimental composite plate is performed with the mode shapes measured by a scanning laser vibrometer as the analyzed 2D signals. The results show that the SWFD analysis method has the prominent features of high accuracy of singularity localization and strong robustness to noise

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health.Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination.Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states.Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress

New complexity measures reveal that topographic loops of human alpha phase potentials are more complex in drowsy than in wake

A number of measures, stemming from nonlinear dynamics, exist to estimate complexity of biomedical objects. In most cases they are appropriate, but sometimes unconventional measures, more suited for specific objects, are needed to perform the task. In our present work, we propose three new complexity measures to quantify complexity of topographic closed loops of alpha carrier frequency phase potentials (CFPP) of healthy humans in wake and drowsy states. EEG of ten adult individuals was recorded in both states, using a 14-channel montage. For each subject and each state, a topographic loop (circular directed graph) was constructed according to CFPP values. Circular complexity measure was obtained by summing angles which directed graph edges (arrows) form with the topographic center. Longitudinal complexity was defined as the sum of all arrow lengths, while intersecting complexity was introduced by counting the number of intersections of graph edges. Wilcoxon’s signed-ranks test was used on the sets of these three measures, as well as on fractal dimension values of some loop properties, to test differences between loops obtained in wake vs. drowsy. While fractal dimension values were not significantly different, longitudinal and intersecting complexities, as well as anticlockwise circularity, were significantly increased in drowsy

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification

International audienceResearch on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithmsbefore using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals

Application of Higuchi fractal dimension and indepenedent component method in analysis of garden snail Br neuron bursting activity modulated by static magnetic field and ouabain.

Nelinearne i napredne statističke metode, pored linearnih metoda, zauzimaju sve značajnije mesto u analizi fizioloških signala, posebno u svetlu nelinearnog i haotičnog ponašanja bioloških sistema. Stoga je zajednička upotreba Higučijeve fraktalne dimenzije i analize nezavisnih komponentata (ICA), značajan i nov pristup u analizi signala a posebno u analizi aktivnosti jednog neurona. Najprepoznatljiviji tip spontane bioelektrične aktivnosti neurona beskičmenjaka i kičmenjaka jeste pojava akcionih potencijala u paketićima. U radu je po prvi put primenjen, jedinstven i inovativan pristup u razdvajanju komponenata spontane bioelektrične aktivnosti Br neurona vinogradskog puža i to na akcione potencijale (AP), intervale između akcionih potencijala (ISI) i tihe intervale bez aktivnosti (IBI) uz pomoć Higučijeve fraktalne dimenzije i aproksimacije Gausovim funkcijama. Taj metodološki pristup je omogućio praćenje uticaja konstantnog magnetnog polja i uabaina inhibitora Na+/K+ pumpe na promene u kompleksnosti spontane bioelektrične aktivnosti Br neurona. Sa druge strane, po prvi put je testirana upotreba ICA metode u različitim eksperimentalnim uslovima po AP, ISI i IBI komponentama spontane bioelektrične aktivnosti. Na taj način, u ovom radu demonstrirana je snaga zajedničke upotrebe navedenih metoda uz predlog da se proširi njihova upotreba za potrebe analize spontano aktivnih neurona različitih vrsta u fiziološkim i patološkim stanjima.Nonlinear and advanced statistical methods, in addition to linear methods, occupy a prominent place in the analysis of physiological signals, especially in light of the non-linear and chaotic behavior of biological systems. Therefore, the use of Higuchi fractal dimension and independent component analysis (ICA), reperesents a new approach to signal analysis, especially regarding the activities of one neuron. The most recognizable type of spontaneous bioelectric activity in neurons of invertebrates as well as vertebrates is the appearance of bursting activity. This study presents a unique and innovative approach to the separation of the components of spontaneous bioelectric activity of the garden snail Br neuron - action potential (AP), interspike interval (ISI) and interburst interval (IBI), by using Higuchi’s fractal dimension and Gaussian fitting functions. This methodological approach allows monitoring of the effect of static magnetic field and the inhibitor of the Na+/K+ pump, ouabain, on the changes in the complexity of the spontaneous bioelectric activity of the Br neuron. On the other hand, for the first time ICA method was tested in different experimental conditions on AP, ISI and IBI components of spontaneous bioelectric activity. This study demonstrates the power of the common use of the above mentioned methods and proposes to extend their use for the purpose of analyzing spontaneously active neurons of different species in physiological and patological conditions