The Experts below are selected from a list of 40473 Experts worldwide ranked by ideXlab platform
Heikki V Huikuri - One of the best experts on this subject based on the ideXlab platform.
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Fractal Analysis of heart rate variability and mortality after an acute myocardial infarction
American Journal of Cardiology, 2002Co-Authors: Jari Tapanainen, Timo H Makikallio, Lars Kober, Christian Torppedersen, Poul Erik Bloch Thomsen, Ainomaija Still, Kai S Lindgren, Heikki V HuikuriAbstract:The recently developed Fractal Analysis of heart rate (HR) variability has been suggested to provide prognostic information about patients with heart failure. This prospective multicenter study was designed to assess the prognostic significance of Fractal and traditional HR variability parameters in a large, consecutive series of survivors of an acute myocardial infarction (AMI). A consecutive series of 697 patients were recruited to participate 2 to 7 days after an AMI in 3 Nordic university hospitals. The conventional time-domain and spectral parameters and the newer Fractal scaling indexes of HR variability were analyzed from 24-hour RR interval recordings. During the mean follow-up of 18.4 +/- 6.5 months, 49 patients (7.0%) died. Of all the risk variables, a reduced short-term Fractal scaling exponent (alpha(1) <0.65), measured by detrended fluctuation Analysis, was the most powerful predictor of mortality (univariate relative risk 5.05, 95% confidence intervals [CI] 2.87 to 8.89, p <0.001). A low scaling exponent alpha(1) predicted death in the patients with and without depressed left ventricular function (p <0.001 and p <0.01, respectively). Several other HR variability parameters also predicted mortality in univariate analyses, but in a multivariate Analysis after adjustments for clinical variables and left ventricular ejection fraction, alpha(1) was the most significant independent HR variability index that predicted subsequent mortality (relative risk 3.90, 95% CI 2.03 to 7.49, p <0.001). Short-term Fractal scaling Analysis of HR variability is a powerful predictor of mortality among patients surviving an acute myocardial infarction.
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Fractal Analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction
American Journal of Cardiology, 1999Co-Authors: Timo H Makikallio, Ary L Goldberger, Soren Hoiber, Lars Kober, Christian Torppedersen, Chungkang Peng, Heikki V HuikuriAbstract:Abstract A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and Fractal Analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic Analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term Fractal-like correlation properties of RR intervals (exponent α) and power-law scaling (exponent β) were studied in 159 patients with depressed LV function (ejection fraction
Timo H Makikallio - One of the best experts on this subject based on the ideXlab platform.
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Fractal Analysis of heart rate variability and mortality after an acute myocardial infarction
American Journal of Cardiology, 2002Co-Authors: Jari Tapanainen, Timo H Makikallio, Lars Kober, Christian Torppedersen, Poul Erik Bloch Thomsen, Ainomaija Still, Kai S Lindgren, Heikki V HuikuriAbstract:The recently developed Fractal Analysis of heart rate (HR) variability has been suggested to provide prognostic information about patients with heart failure. This prospective multicenter study was designed to assess the prognostic significance of Fractal and traditional HR variability parameters in a large, consecutive series of survivors of an acute myocardial infarction (AMI). A consecutive series of 697 patients were recruited to participate 2 to 7 days after an AMI in 3 Nordic university hospitals. The conventional time-domain and spectral parameters and the newer Fractal scaling indexes of HR variability were analyzed from 24-hour RR interval recordings. During the mean follow-up of 18.4 +/- 6.5 months, 49 patients (7.0%) died. Of all the risk variables, a reduced short-term Fractal scaling exponent (alpha(1) <0.65), measured by detrended fluctuation Analysis, was the most powerful predictor of mortality (univariate relative risk 5.05, 95% confidence intervals [CI] 2.87 to 8.89, p <0.001). A low scaling exponent alpha(1) predicted death in the patients with and without depressed left ventricular function (p <0.001 and p <0.01, respectively). Several other HR variability parameters also predicted mortality in univariate analyses, but in a multivariate Analysis after adjustments for clinical variables and left ventricular ejection fraction, alpha(1) was the most significant independent HR variability index that predicted subsequent mortality (relative risk 3.90, 95% CI 2.03 to 7.49, p <0.001). Short-term Fractal scaling Analysis of HR variability is a powerful predictor of mortality among patients surviving an acute myocardial infarction.
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Fractal Analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction
American Journal of Cardiology, 1999Co-Authors: Timo H Makikallio, Ary L Goldberger, Soren Hoiber, Lars Kober, Christian Torppedersen, Chungkang Peng, Heikki V HuikuriAbstract:Abstract A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and Fractal Analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic Analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term Fractal-like correlation properties of RR intervals (exponent α) and power-law scaling (exponent β) were studied in 159 patients with depressed LV function (ejection fraction
Sebastian Stach - One of the best experts on this subject based on the ideXlab platform.
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Fractal Analysis of afm images of the surface of bowman s membrane of the human cornea
Annals of Biomedical Engineering, 2015Co-Authors: ştefan ţălu, Sebastian Stach, Vivian M Sueiras, Noel M ZiebarthAbstract:The objective of this study is to further investigate the ultrastructural details of the surface of Bowman’s membrane of the human cornea, using atomic force microscopy (AFM) images. One representative image acquired of Bowman’s membrane of a human cornea was investigated. The three-dimensional (3-D) surface of the sample was imaged using AFM in contact mode, while the sample was completely submerged in optisol solution. Height and deflection images were acquired at multiple scan lengths using the MFP-3D AFM system software (Asylum Research, Santa Barbara, CA), based in IGOR Pro (WaveMetrics, Lake Oswego, OR). A novel approach, based on computational algorithms for Fractal Analysis of surfaces applied for AFM data, was utilized to analyze the surface structure. The surfaces revealed a Fractal structure at the nanometer scale. The Fractal dimension, D, provided quantitative values that characterize the scale properties of surface geometry. Detailed characterization of the surface topography was obtained using statistical parameters, in accordance with ISO 25178-2: 2012. Results obtained by Fractal Analysis confirm the relationship between the value of the Fractal dimension and the statistical surface roughness parameters. The surface structure of Bowman’s membrane of the human cornea is complex. The analyzed AFM images confirm a Fractal nature of the surface, which is not taken into account by classical surface statistical parameters. Surface Fractal dimension could be useful in ophthalmology to quantify corneal architectural changes associated with different disease states to further our understanding of disease evolution.
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afm imaging and Fractal Analysis of surface roughness of aln epilayers on sapphire substrates
Applied Surface Science, 2014Co-Authors: Dinara Dallaeva, Sebastian Stach, ştefan ţălu, Pavel Tománek, Pavel Skarvada, Lubomir GrmelaAbstract:Abstract The paper deals with AFM imaging and characterization of 3D surface morphology of aluminum nitride (AlN) epilayers on sapphire substrates prepared by magnetron sputtering. Due to the effect of temperature changes on epilayer's surface during the fabrication, a surface morphology is studied by combination of atomic force microscopy (AFM) and Fractal Analysis methods. Both methods are useful tools that may assist manufacturers in developing and fabricating AlN thin films with optimal surface characteristics. Furthermore, they provide different yet complementary information to that offered by traditional surface statistical parameters. This combination is used for the first time for measurement on AlN epilayers on sapphire substrates, and provides the overall 3D morphology of the sample surfaces (by AFM imaging), and reveals Fractal characteristics in the surface morphology (Fractal Analysis).
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surface roughness characterization of poly methylmethacrylate films with immobilized eu iii β diketonates by Fractal Analysis
International Journal of Polymer Analysis and Characterization, 2014Co-Authors: ştefan ţălu, Sebastian Stach, Joana Zaharieva, M Milanova, D Todorovsky, Stefano GiovanzanaAbstract:The structural complexity of the 3-D surface of poly(methylmethacrylate) films with immobilized europium β-diketonates was studied by atomic force microscopy and Fractal Analysis. Fractal Analysis of surface roughness revealed that the 3-D surface has Fractal geometry at the nanometer scale. Poly(methylmethacrylate) (PMMA) as immobilization matrix is dense and uniform, and a tendency for formation of chain structures was observed. Fractal Analysis can quantify key elements of 3-D surface roughness such as the Fractal dimensions D f determined by the morphological envelopes method of the Eu(DBM)3 and Eu(DBM)3 · dpp nanostructures, which are not taken into account by traditional surface statistical parameters.
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characterization of surface roughness of pt schottky contacts on quaternary n al0 08in0 08ga0 84n thin film assessed by atomic force microscopy and Fractal Analysis
Journal of Materials Science: Materials in Electronics, 2014Co-Authors: ştefan ţălu, Sebastian Stach, Alaa J Ghazai, Abu Hassan, Z Hassan, Mihai ţăluAbstract:The purpose of this study was to analyze surface topography of Pt Schottky contacts on quaternary n-Al0.08In0.08Ga0.84N thin film. To understand how the effect of temperature changes the layers surface, the surface topography was characterized through atomic force microscopy (AFM) and Fractal Analysis. Pt Schottky contacts grown on nanostructure Al0.08In0.08Ga0.84N thin film grown by molecular beam epitaxy technique on sapphire substrate at annealing temperatures range of 300–500 °C were used. AFM Analysis was performed in contact mode, on square areas of 10 × 10 μm2, by using a Nanosurf Easyscan 2 AFM system. Detailed surface characterization of the surface topography was obtained using statistical parameters of 3D surface roughness, according with ISO 25178-2: 2012, provided by the AFM software. The results revealed that the high quality Schottky contact with the Schottky barrier heights and ideality factor of 0.76 and 1.03 respectively can be obtained under 30 min annealing at 400 °C in N2 ambience. The surface roughness of Pt Schottky contacts on quaternary n-Al0.08In0.08Ga0.84N thin film revealed a Fractal structure at nanometer scale. Results obtained by Fractal Analysis confirm the relationship between the value of the Fractal dimension and the statistical surface roughness parameters. AFM and Fractal Analysis are accurate tools that may assist manufacturers in developing Pt Schottky contacts on quaternary n-Al0.08In0.08Ga0.84N thin film with optimal surface characteristics and provides different yet complementary information to that offered by traditional surface statistical parameters.
Christian Torppedersen - One of the best experts on this subject based on the ideXlab platform.
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Fractal Analysis of heart rate variability and mortality after an acute myocardial infarction
American Journal of Cardiology, 2002Co-Authors: Jari Tapanainen, Timo H Makikallio, Lars Kober, Christian Torppedersen, Poul Erik Bloch Thomsen, Ainomaija Still, Kai S Lindgren, Heikki V HuikuriAbstract:The recently developed Fractal Analysis of heart rate (HR) variability has been suggested to provide prognostic information about patients with heart failure. This prospective multicenter study was designed to assess the prognostic significance of Fractal and traditional HR variability parameters in a large, consecutive series of survivors of an acute myocardial infarction (AMI). A consecutive series of 697 patients were recruited to participate 2 to 7 days after an AMI in 3 Nordic university hospitals. The conventional time-domain and spectral parameters and the newer Fractal scaling indexes of HR variability were analyzed from 24-hour RR interval recordings. During the mean follow-up of 18.4 +/- 6.5 months, 49 patients (7.0%) died. Of all the risk variables, a reduced short-term Fractal scaling exponent (alpha(1) <0.65), measured by detrended fluctuation Analysis, was the most powerful predictor of mortality (univariate relative risk 5.05, 95% confidence intervals [CI] 2.87 to 8.89, p <0.001). A low scaling exponent alpha(1) predicted death in the patients with and without depressed left ventricular function (p <0.001 and p <0.01, respectively). Several other HR variability parameters also predicted mortality in univariate analyses, but in a multivariate Analysis after adjustments for clinical variables and left ventricular ejection fraction, alpha(1) was the most significant independent HR variability index that predicted subsequent mortality (relative risk 3.90, 95% CI 2.03 to 7.49, p <0.001). Short-term Fractal scaling Analysis of HR variability is a powerful predictor of mortality among patients surviving an acute myocardial infarction.
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Fractal Analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction
American Journal of Cardiology, 1999Co-Authors: Timo H Makikallio, Ary L Goldberger, Soren Hoiber, Lars Kober, Christian Torppedersen, Chungkang Peng, Heikki V HuikuriAbstract:Abstract A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and Fractal Analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic Analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term Fractal-like correlation properties of RR intervals (exponent α) and power-law scaling (exponent β) were studied in 159 patients with depressed LV function (ejection fraction
Lars Kober - One of the best experts on this subject based on the ideXlab platform.
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Fractal Analysis of heart rate variability and mortality after an acute myocardial infarction
American Journal of Cardiology, 2002Co-Authors: Jari Tapanainen, Timo H Makikallio, Lars Kober, Christian Torppedersen, Poul Erik Bloch Thomsen, Ainomaija Still, Kai S Lindgren, Heikki V HuikuriAbstract:The recently developed Fractal Analysis of heart rate (HR) variability has been suggested to provide prognostic information about patients with heart failure. This prospective multicenter study was designed to assess the prognostic significance of Fractal and traditional HR variability parameters in a large, consecutive series of survivors of an acute myocardial infarction (AMI). A consecutive series of 697 patients were recruited to participate 2 to 7 days after an AMI in 3 Nordic university hospitals. The conventional time-domain and spectral parameters and the newer Fractal scaling indexes of HR variability were analyzed from 24-hour RR interval recordings. During the mean follow-up of 18.4 +/- 6.5 months, 49 patients (7.0%) died. Of all the risk variables, a reduced short-term Fractal scaling exponent (alpha(1) <0.65), measured by detrended fluctuation Analysis, was the most powerful predictor of mortality (univariate relative risk 5.05, 95% confidence intervals [CI] 2.87 to 8.89, p <0.001). A low scaling exponent alpha(1) predicted death in the patients with and without depressed left ventricular function (p <0.001 and p <0.01, respectively). Several other HR variability parameters also predicted mortality in univariate analyses, but in a multivariate Analysis after adjustments for clinical variables and left ventricular ejection fraction, alpha(1) was the most significant independent HR variability index that predicted subsequent mortality (relative risk 3.90, 95% CI 2.03 to 7.49, p <0.001). Short-term Fractal scaling Analysis of HR variability is a powerful predictor of mortality among patients surviving an acute myocardial infarction.
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Fractal Analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction
American Journal of Cardiology, 1999Co-Authors: Timo H Makikallio, Ary L Goldberger, Soren Hoiber, Lars Kober, Christian Torppedersen, Chungkang Peng, Heikki V HuikuriAbstract:Abstract A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and Fractal Analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic Analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term Fractal-like correlation properties of RR intervals (exponent α) and power-law scaling (exponent β) were studied in 159 patients with depressed LV function (ejection fraction
