Higuchi Fractal Properties of Onset Epilepsy Electroencephalogram

Epilepsy is a medical term which indicates a common neurological disorder characterized by seizures, because of abnormal neuronal activity. This leads to unconsciousness or even a convulsion. The possible etiologies should be evaluated and treated. Therefore, it is necessary to concentrate not only on finding out efficient treatment methods, but also on developing algorithm to support diagnosis. Currently, there are a number of algorithms, especially nonlinear algorithms. However, those algorithms have some difficulties one of which is the impact of noise on the results. In this paper, in addition to the use of fractal dimension as a principal tool to diagnose epilepsy, the combination between ICA algorithm and averaging filter at the preprocessing step leads to some positive results. The combination which improved the fractal algorithm become robust with noise on EEG signals. As a result, we can see clearly fractal properties in preictal and ictal period so as to epileptic diagnosis

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

Higuchi fractal dimension applied to RR intervals in children with Attention Defi cit Hyperactivity Disorder

Background: Attention defi cit hyperactivity disorder (ADHD) is categorized by a lowered attention span, recklessness, and hyperactivity. Autonomic nervous system inequality has previously been studied using the same data by chaotic global techniques. We aim to compare the autonomic function of children with ADHD and controls by analyzing heart rate variability (HRV). Methods: 28 children with ADHD (22 boys, mean age 10.0 years ± 1.9 years) and 28 controls (15 boys, mean age 9.9 years ± 1.8 years) rested in supine position with spontaneous breathing for 20 minutes. Heart rate was recorded beat by beat. HRV analysis was performed by Higuchi Fractal Dimension technique. Results: ADHD promoted an increase in the Higuchi Fractal Dimension. The optimum value of Kmax was 10. Conclusion: ADHD signifi cantly altered cardiac autonomic modulation as measured by the Higuchi fractal dimension of HRV. It can therefore be stated that ADHD has increased the complexity of the HRV  signal through cardiac autonomic modulation

Spectral Asymmetry and Higuchi’s Fractal Dimension Measures of Depression Electroencephalogram

This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi’s fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P0.05). The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG

The effects of musical auditory stimulation on heart rate autonomic responses to driving: A prospective randomized case-control pilot study

Stress induced by driving has been revealed to increase the chances of cardiovascular complications and is involved or related to traffic accidents. In order to develop strategies to avoid health problems during driving we aimed to evaluate the acute effects of auditory stimulation with music on heart rate variability (HRV) during driving in congested urban traffic. This is a prospective cross-sectional randomized controlled pilot study conducted with five healthy women. Subjects were evaluated on two different random days, whose order of execution was established through a randomization process. In the music protocol the volunteers were exposed to music for the entire 20 min of traffic while in the control protocol the subjects performed the same procedures but were not exposed to any music. We noted that all Higuchi fractal dimension parameters except Kmax 10, Kmax 130 and Kmax 140 were reduced between pre-driving in the control protocol vs. driving in the control protocol. The same changes were noted between pre-driving in the music protocol vs. driving in the control protocol. In conclusion, musical auditory stimulation improved nonlinear HRV changes induced by driving

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Objectives: An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials: Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods: EEG data were investigated from two different angles: frequency domain—spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain—FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results: The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion: FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion: Our work suggests that FD is a promising measure to monitor the response to ASMs in FE

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Objectives: An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials: Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods: EEG data were investigated from two different angles: frequency domain—spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain—FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results: The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion: FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion: Our work suggests that FD is a promising measure to monitor the response to ASMs in FE

Complex measurements of heart rate variability in obese youths: Distinguishing autonomic dysfunction

Introduction. Heart rate variability (HRV) can be assessed from RR-intervals. These are derived from an electrocardiographic PQRST-signature and can deviate in a chaotic or irregular manner. In the past, techniques from statistical physics have allowed researchers to study such systems.Objective. This study planned to assess the heart rate dynamics in young obese subjects by nonlinear metrics to heart rate variability. Method. 86 subjects were split equally according to status. Heart rate was recorded with the subjects resting in a dorsal (prone) position for 30 minutes. The complexity of the RR-intervals was assessed by five Entropies, Detrended Fluctuation Analysis, Higuchi and Katz’s fractal dimensions Following inconclusive tests of normality we calculated the One-Way Analysis of Variance, Kruskal-Wallis, and the Effect Sizes by Cohen’s d significances. Results. It was established that Shannon, Renyi and Tsallis Entropies and the Higuchi and Katz’s fractal dimensions could significantly discriminate the two groups. The three entropies were higher in obese youths, suggesting less predictable sets of RR intervals (p<0.0001; d≈1.0). Whilst the Higuchi (p<0.003; d≈0.76) and Katz’s (p≈0.02; d≈0.57) fractal dimensions were lower in obese youths. Conclusion. As with chaotic globals an increase in response was detected by three measures of entropy in young obese. This is counter to the decreasing response detected by fractal dimensions. Chaotic globals and entropies are more dependable than fractal dimensions when assessing the responses to obesity

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

ObjectivesAn important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.MaterialsTwenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).MethodsEEG data were investigated from two different angles: frequency domain—spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain—FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.ResultsThe δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p &lt; 0.01 and DR-t2 vs. HC, p &lt; 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p &lt; 0.01 and DR-t2 vs. HC, p &lt; 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.DiscussionFD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.ConclusionOur work suggests that FD is a promising measure to monitor the response to ASMs in FE

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