Relevant bibliographies by topics / Higuchi fractal dimension

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Higuchi fractal dimension'

Author: Grafiati
Published: 3 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Higuchi fractal dimension"

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Wanliss, J., R. Hernandez Arriaza, G. Wanliss, and S. Gordon. "OPTIMIZATION OF THE HIGUCHI METHOD." International Journal of Research -GRANTHAALAYAH 9, no. 11 (2021): 202–13. http://dx.doi.org/10.29121/granthaalayah.v9.i11.2021.4393.

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Background and Objective: Higuchi’s method of determining fractal dimension (HFD) occupies a valuable place in the study of a wide variety of physical signals. In comparison to other methods, it provides more rapid, accurate estimations for the entire range of possible fractal dimensions. However, a major difficulty in using the method is the correct choice of tuning parameter (kmax) to compute the most accurate results. In the past researchers have used various ad hoc methods to determine the appropriate kmax choice for their particular data. We provide a more objective method of determining, a priori, the best value for the tuning parameter, given a particular length data set. Methods: We create numerous simulations of fractional Brownian motion to perform Monte Carlo simulations of the distribution of the calculated HFD. Results: Experimental results show that HFD depends not only on kmax but also on the length of the time series, which enable derivation of an expression to find the appropriate kmax for an input time series of unknown fractal dimension. Conclusion: The Higuchi method should not be used indiscriminately without reference to the type of data whose fractal dimension is examined. Monte Carlo simulations with different fractional Brownian motions increases the confidence of evaluation results.
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Bykova, M. O., and V. A. Balandin. "Methodological features of the analysis of the fractal dimension of the heart rate." Russian Technological Journal 11, no. 2 (2023): 58–71. http://dx.doi.org/10.32362/2500-316x-2023-11-2-58-71.

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Objectives. The aim of the present work is to determine the fractal dimension parameter calculated for a sequence of R–R intervals in order to identify the boundaries of its change for healthy and sick patients, as well as the possibility of its use as an additional factor in the detection of cardiac pathology.Methods. In order to determine the fractal dimension parameter, the Hurst-, Barrow-, minimum coverage area-, and Higuchi methods are used. For assessing the stationarity of a number of electrocardiography (ECG) intervals, a standard method is used to compare arithmetic averages and variances from samples of the total data array of ECG intervals. To identify differences in fractal dimensions of healthy and sick patients, this parameter was ranked. Using the Kolmogorov–Smirnov two-sample criterion, the difference between the distribution laws in the samples for healthy and sick patients is shown.Results. Among the considered methods for calculating the fractal dimension, the Higuchi method demonstrates the smallest data spread between healthy patients. By ranking the calculated fractional dimension values, it was possible to identify the difference between this parameter for healthy and sick patients. The difference in the distribution of fractal dimension of healthy and sick patients is shown to be statistically significant for the coverage and Higuchi methods. At the same time, when using the traditional Hurst method, there is no reason to reject the null hypothesis that two groups of patients belong to the same general population.Conclusions. Based on the obtained data, the difference between the fractal dimension indicators of the duration of R–R intervals of healthy and sick patients is shown to be statistically significant when using the Higuchi method. The fractal dimensions of healthy and sick patients can be effectively distinguished by ranking samples. The results of the research substantiate prospects for further studies aimed at using fractal characteristics of the heart rhythm to identify abnormalities of the latter, which can serve as an additional factor in determining heart pathologies.
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MORENO-GOMEZ, ALEJANDRO, JOSE M. MACHORRO-LOPEZ, JUAN P. AMEZQUITA-SANCHEZ, CARLOS A. PEREZ-RAMIREZ, MARTIN VALTIERRA-RODRIGUEZ, and AURELIO DOMINGUEZ-GONZALEZ. "FRACTAL DIMENSION ANALYSIS FOR ASSESSING THE HEALTH CONDITION OF A TRUSS STRUCTURE USING VIBRATION SIGNALS." Fractals 28, no. 07 (2020): 2050127. http://dx.doi.org/10.1142/s0218348x20501273.

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During the last years, civil infrastructure has experienced an increasing development to satisfy the society’s demands such as communication, transportation, work and living spaces, among others. In this sense, the development and application of methods to guarantee the structure optimal operation, known as Structural Health Monitoring schemes, are necessary in order to avoid economic and human losses. Modern schemes employ the structure vibration response as any damage will modify the structure physical properties, which will be reflected in the vibration response. Thus, by measuring the waveform changes of the response, the structure condition can be determined. Considering this fact, this paper investigates the effectiveness of Katz fractal dimension, Higuchi fractal dimension, Box fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension which are nonlinear measurements to extract features of vibration signals in order to determine the health condition of a 3D 9-bay truss-type bridge. The obtained results show that the algorithms corresponding to Higuchi and Petrosian fractal dimension algorithms exceed the other nonlinear measurements in efficiency to discriminate between a healthy structure and a damage produced by corrosion.
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Benavides-Bravo, Francisco Gerardo, Dulce Martinez-Peon, Ángela Gabriela Benavides-Ríos, Otoniel Walle-García, Roberto Soto-Villalobos, and Mario A. Aguirre-López. "A Climate-Mathematical Clustering of Rainfall Stations in the Río Bravo-San Juan Basin (Mexico) by Using the Higuchi Fractal Dimension and the Hurst Exponent." Mathematics 9, no. 21 (2021): 2656. http://dx.doi.org/10.3390/math9212656.

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When conducting an analysis of nature’s time series, such as meteorological ones, an important matter is a long-range dependence to quantify the global behavior of the series and connect it with other physical characteristics of the region of study. In this paper, we applied the Higuchi fractal dimension and the Hurst exponent (rescaled range) to quantify the relative trend underlying the time series of historical data from 17 of the 34 weather stations located in the Río Bravo-San Juan Basin, Mexico; these data were provided by the National Water Commission CONAGUA) in Mexico. In this way, this work aims to perform a comparative study about the level of persistency obtained by using the Higuchi fractal dimension and Hurst exponent for each station of the basin. The comparison is supported by a climate clustering of the stations, according to the Köppen classification. Results showed a better fitting between the climate of each station and its Higuchi fractal dimension obtained than when using the Hurst exponent. In fact, we found that the more the aridity of the zone the more the persistency of rainfall, according to Higuchi’s values. In turn, we found more relation between the Hurst exponent and the accumulated amount of rainfall. These are relations between the climate and the long-term persistency of rainfall in the basin that could help to better understand and complete the climatological models of the study region. Trends between the fractal exponents used and the accumulated annual rainfall were also analyzed.
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Li, Yuxing, Shuai Zhang, Lili Liang, and Qiyu Ding. "Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals." Fractal and Fractional 8, no. 1 (2024): 56. http://dx.doi.org/10.3390/fractalfract8010056.

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Fractal dimension, as a common nonlinear dynamics metric, is extensively applied in biomedicine, fault diagnosis, underwater acoustics, etc. However, traditional fractal dimension can only analyze the complexity of the time series given a single channel at a particular scale. To characterize the complexity of multichannel time series, multichannel information processing was introduced, and multivariate Higuchi fractal dimension (MvHFD) was proposed. To further analyze the complexity at multiple scales, multivariate multiscale Higuchi fractal dimension (MvmHFD) was proposed by introducing multiscale processing algorithms as a technology that not only improved the use of fractal dimension in the analysis of multichannel information, but also characterized the complexity of the time series at multiple scales in the studied time series data. The effectiveness and feasibility of MvHFD and MvmHFD were verified by simulated signal experiments and real signal experiments, in which the simulation experiments tested the stability, computational efficiency, and signal separation performance of MvHFD and MvmHFD, and the real signal experiments tested the effect of MvmHFD on the recognition of multi-channel mechanical signals. The experimental results show that compared to other indicators, A achieves a recognition rate of 100% for signals in three features, which is at least 17.2% higher than for other metrics.
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Sabrine, Ben, and Aguili Taoufik. "Application of Fractal Dimension for Cardiac Arrhythmias Classification." Computational Biology and Bioinformatics 12, no. 1 (2024): 12–17. http://dx.doi.org/10.11648/j.cbb.20241201.12.

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Fractal analysis is crucial for understanding complex, irregular patterns found in nature, finance, and various scientific fields. It helps to reveal self-similarity, where structures repeat at different scales, providing insights into chaotic systems like weather patterns, stock markets, and biological growth. By applying fractal analysis, researchers can model phenomena that traditional geometric methods cannot easily describe, enabling better predictions and deeper comprehension of dynamic systems. The Fractals are a fascinating mathematical tool for modeling the roughness of nature and understanding structure of such complex objects. They are considered a tool for understanding the world. In general, fractal objects are characterized by the fractal dimension. The application of fractal geometry to the analysis of ECG time series data is examined in this paper. A method based on the assessment of the Fractal Dimension (FD) of ECG recordings is suggested for the identification of cardiac diseases. In this work, and in order to exploit the fractal dimension to analyze fractal signals, the notion of fractal dimension is defined by presenting methods for calculating this dimension such as Higuchi algorithm, Katz method, regularization, box-counting etc… Each of them has its own advantages and disadvantages. This study has shown that the electrocardiogram (ECG) is a fractal signal. This allows to classify heartbeats founded on the concept of fractals. The main aim is to develop a digital technique to analyze ECG signals in order to make an accurate diagnosis of cardiovascular diseases.
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Garner, David M., Naiara Maria de Souza, and Luiz Carlos M. Vanderlei. "Heart Rate Variability Analysis: Higuchi and Katz’s Fractal Dimensions in Subjects with Type 1 Diabetes Mellitus." Romanian Journal of Diabetes Nutrition and Metabolic Diseases 25, no. 3 (2018): 289–95. http://dx.doi.org/10.2478/rjdnmd-2018-0034.

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Abstract 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 subjects with type 1 diabetes mellitus.
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Kumar, Sanjeev, Amod Kumar, Anjan Trikha, Sneh Anand, and Prashanth Gantla. "Higuchi fractal dimension as a measure of analgesia." International Journal of Medical Engineering and Informatics 4, no. 1 (2012): 66. http://dx.doi.org/10.1504/ijmei.2012.045304.

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Pamela, Yolanda Gandes, and Dwi Juniati. "KLASIFIKASI JENIS DELPHINIDAE (LUMBA-LUMBA) DENGAN DIMENSI FRAKTAL MENGGUNAKAN METODE HIGUCHI DAN KNN (K-NEAREST NEIGHBOR)." MATHunesa: Jurnal Ilmiah Matematika 9, no. 1 (2021): 204–11. http://dx.doi.org/10.26740/mathunesa.v9n1.p204-211.

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The delphinidae family belongs to the Cetacean Order and is a member of the Odontocetes. The delphinidae family has characterized by the physical characteristics and frequency ranges of sound signals produced. Living in the sea and being a rare animal makes delphinidae very difficult to find and if we want to be classified, we have to capture and analyze the physicality of delphinidae. By using the fractal dimension we can analyze the sounds of the delphinidae family based on the characteristics of their sound signals to classify them. In this research, members of the delphinidae family will be classified using the Higuchi and K-Nearest Neighbor methods. By using 80 data, namely Common Dolphin 18 data, Killer Whale 20 data, Fraser's Dolphin 20 data, and Long-Finned Pilot Whale 22 data, the data used is .wav. In the first step, the Pre-Processing process will be carried out, then the feature extraction process will be carried out using the Discrete Wavelet Transform type mother wavelet Daubechies db4 wavelet with level 5 decomposition and Fast Fourier Transform. Then we will find the fractal dimension value using the Higuchi method. After obtaining the fractal dimension, the data will be divided into Training data and Testing data using k-cross validation with k value experiments namely 2, 4, 8, and 10. After the data is divided the data will be classified using K-Nearest Neighbor with an experimental K value. namely 1, 3, 5 and 7. In this study, the highest accuracy value was 82.5% with Kmax = 50, k = 8, and K = 3.Thus it can be concluded, the Higuchi and K-Nearest Neighbor methods can be used to classify members of the family delphinidae
 Keywords: Delphinidae, classification of member of family delphinidae, Higuchi fractal dimension, KNN
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Gvozdarev, Alexey, and Roman Parovik. "On the Relationship between the Fractal Dimension of Geomagnetic Variations at Altay and the Space Weather Characteristics." Mathematics 11, no. 16 (2023): 3449. http://dx.doi.org/10.3390/math11163449.

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The fractal dimension of geomagnetic field component variations (horizontal—H, vertical—Z and magnetic declination—D) at the Baigazan magnetic station at Russian Altay, for the period 2011–2013, were calculated using the Higuchi method. The daily variation of Higuchi Fractal Dimension (HFD) for the D, H, Z components of the geomagnetic field were investigated, and its contribution to the variability of HFD was found to be from 30 to 40 percent of the total variance. A correlation analysis of the fractal dimension of the variations of the D, H, Z components with the Auroral Electrojet (AE) index and solar wind characteristics was carried out. Negative correlations with logarithms of the AE-index, interplanetary magnetic field (IMF) strength and solar wind velocity were found. About 25 percent of the HFD variance is controlled by the variability of these characteristics. Pair and partial correlation coefficients for these parameters were calculated for every month of 2011–2013.
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Dissertations / Theses on the topic "Higuchi fractal dimension"

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Cusenza, Monica. "Fractal analysis of the EEG and clinical applications." Doctoral thesis, Università degli studi di Trieste, 2012. http://hdl.handle.net/10077/7394.

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2010/2011<br>Most of the knowledge about physiological systems has been learned using linear system theory. The randomness of many biomedical signals has been traditionally ascribed to a noise-like behavior. An alternative explanation for the irregular behavior observed in systems which do not seem to be inherently stochastic is provided by one of the most striking mathematical developments of the past few decades, i.e., chaos theory. Chaos theory suggests that random-like behavior can arise in some deterministic nonlinear systems with just a few degrees of freedom. One of the most evocative aspects of deterministic chaos is the concept of fractal geometry. Fractal structure, characterized by self-similarity and noninteger dimension, is displayed in chaotic systems by a subset of the phase space known as strange attractor. However, fractal properties are observed also in the unpredictable time evolution and in the 1/f^β power-law of many biomedical signals. The research activities carried out by the Author during the PhD program are concerned with the analysis of the fractal-like behavior of the EEG. The focus was set on those methods which evaluate the fractal geometry of the EEG in the time domain, in the hope of providing physicians and researchers with new valuable tools of low computational cost for the EEG analysis. The performances of three widely used techniques for the direct estimation of the fractal dimension of the EEG were compared and the accuracy of the fBm scaling relationship, often used to obtain indirect estimates from the slope of the spectral density, was assessed. Direct estimation with Higuchi's algorithm turned out to be the most suitable methodology, producing correct estimates of the fractal dimension of the electroencephalogram also on short traces, provided that minimum sampling rate required to avoid aliasing is used. Based on this result, Higuchi's fractal dimension was used to address three clinical issues which could involve abnormal complexity of neuronal brain activity: 1) the monitoring of carotid endarterectomy for the prevention of intraoperative stroke, 2) the assessment of the depth of anesthesia to monitor unconsciousness during surgery and 3) the analysis of the macro-structural organization of the EEG in autism with respect to mental retardation. The results of the clinical studies suggest that, although linear spectral analysis still represents a valuable tool for the investigation of the EEG, time domain fractal analysis provides additional information on brain functioning which traditional analysis cannot achieve, making use of techniques of low computational cost.<br>La maggior parte delle conoscenze acquisite sui sistemi fisiologici si deve alla teoria dei sistemi lineari. Il comportamento pseudo stocastico di molti segnali biomedici è stato tradizionalmente attribuito al concetto di rumore. Un'interpretazione alternativa del comportamento irregolare rilevato in sistemi che non sembrano essere intrinsecamente stocastici è fornita da uno dei più sorprendenti sviluppi matematici degli ultimi decenni: la teoria del caos. Tale teoria suggerisce che una certa componente casuale può sorgere in alcuni sistemi deterministici non lineari con pochi gradi di libertà. Uno degli aspetti più suggestivi del caos deterministico è il concetto di geometria frattale. Strutture frattali, caratterizzate da auto-somiglianza e dimensione non intera, sono rilevate nei sistemi caotici in un sottoinsieme dello spazio delle fasi noto con il nome di attrattore strano. Tuttavia, caratteristiche frattali possono manifestarsi anche nella non prevedibile evoluzione temporale e nella legge di potenza 1/f^β tipiche di molti segnali biomedici. Le attività di ricerca svolte dall'Autore nel corso del dottorato hanno riguardato l'analisi del comportamento frattale dell'EEG. L'attenzione è stata rivolta a quei metodi che affrontano lo studio della geometria frattale dell'EEG nel dominio del tempo, nella speranza di fornire a medici e ricercatori nuovi strumenti utili all'analisi del segnale EEG e caratterizzati da bassa complessità computazionale. Sono state messe a confronto le prestazioni di tre tecniche largamente utilizzate per la stima diretta della dimensione frattale dell'EEG e si è valutata l'accuratezza della relazione di scaling del modello fBm, spesso utilizzata per ottenere stime indirette a partire dalla pendenza della densità spettrale di potenza. Il metodo più adatto alla stima della dimensione frattale dell'elettroencefalogramma è risultato essere l'algoritmo di Higuchi, che produce stime accurate anche su segmenti di breve durata a patto che il segnale sia campionato alla minima frequenza di campionamento necessaria ad evitare il fenomeno dell'aliasing. Sulla base di questo risultato, la dimensione frattale di Higuchi è stata utilizzata per esaminare tre questioni cliniche che potrebbero coinvolgere una variazione della complessità dell'attività neuronale: 1) il monitoraggio dell'endoarterectomia carotidea per la prevenzione dell'ictus intraoperatorio, 2) la valutazione della profondità dell'anestesia per monitorare il livello di incoscienza durante l'intervento chirurgico e 3) l'analisi dell'organizzazione macro-strutturale del EEG nell'autismo rispetto alla condizione di ritardo mentale. I risultati degli studi clinici suggeriscono che, sebbene l'analisi spettrale rappresenti ancora uno strumento prezioso per l'indagine dell'EEG, l'analisi frattale nel dominio del tempo fornisce informazioni aggiuntive sul funzionamento del cervello che l'analisi tradizionale non è in grado di rilevare, con il vantaggio di impiegare tecniche a basso costo computazionale.<br>XXIV Ciclo<br>1984
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Book chapters on the topic "Higuchi fractal dimension"

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Bachmann, M., A. Suhhova, J. Lass, and H. Hinrikus. "Revealing Small Hidden Changes in Human EEG by Higuchi’s Fractal Dimension." In IFMBE Proceedings. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34197-7_12.

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Lawal, Alaba Joy, Otasowie Owolafe, and Aderonke F. Thompson. "Audio Steganalysis Using Fractal Dimension and Convolutional Neural Network (CNN) Model." In Emerging Technologies and Security in Cloud Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2081-5.ch015.

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The rate at which secret messages are being transmitted through various digital signal media is alarming; these operations are done in an unsuspicious manner and users transmit these messages without knowledge of the embedded secret messages. Audio steganalysis deals with detecting the presence of secret messages in audio messages. Some of the existing steganalysis methods are laden with having prior knowledge of the steganography methods adopted in embedding the secret message in an audio signal, which reduces the detection efficiency. Consequently, this research developed a Higuchi-based audio steganalysis method that detects secret messages without having prior knowledge of the embedding techniques used. The algorithm reduces the fractal dimension of the audio signal to extract relevant features, while convolutional neural network was used as classifier. The research records high accuracy (96%) when compared with previous research. The accuracy of the developed system shows its effectiveness in detecting embedded messages without prior knowledge of the deployed steganography method.
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D'Addio Gianni, Corbi Graziamaria, Accardo Agostino, et al. "Fractal Behaviour of Heart Rate Variability Reflects Severity in Stroke Patients." In Studies in Health Technology and Informatics. IOS Press, 2009. https://doi.org/10.3233/978-1-60750-044-5-794.

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Non-linear parameters obtained from heart rate variability (HRV) analysis has recently been recognized to provide valuable information for physiological interpretation of heart rate fluctuation. Among the numerous non-linear parameters related to the fractal behaviour of the HRV signal, two classes have gained wide interest in the last years: the beta exponent based on the 1/f-like relationship, starting from the spectral power, and that based on fractal dimension. In order to evaluate the relationship between lesion's severity and fractal behaviour, 20 first-ever stroke subjects and 10 healthy subjects were studied. Patients were divided in two groups according to single or multiple medium cerebral artery lesions. All subjects underwent 24-hour Holter recording analysed by fractal and 1/f-like techniques. Differently from methods usually used in literature to evaluate the fractal dimension (FD), in this work the FD was extracted by using the Higuchi's algorithm that permits to calculate the parameter directly from the HRV sequences in the time domain. Results show that fractal analysis contains relevant information related to different HRV dynamics that permits to separate normal subjects from stroke patients. FD is also able to distinguish between normal and stroke subjects with different lesion's severity.
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Priya, Pradip Kumar, Ram Pratap Yadav, Hari Pratap Bhasker, Anil Kumar, and Kusum Lata Pandey. "Investigation of Substrate-effect on BaF2 Thin Films: A Study of Fractal Nature." In Materials Science: A Field of Diverse Industrial Applications. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815051247123010007.

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BaF2 thin films of thickness 20 nm are prepared using the electron beam evaporation technique (at room temperature) on glass, silicon (Si) as well as aluminum (Al) substrate, respectively. These substrates play a crucial role in the evolution of thin film surface morphology. The thin films grown far from equilibrium have self-affine nature which is reminiscent of fractal behaviour. The surface morphology of films is recorded by atomic force microscopy (AFM). Scaling law analysis is performed on AFM images to confirm that the thin film surfaces under investigation have self-affine nature. The concept of fractal geometry is applied to explore-how different substrates affect the surface morphology of films. The fractal dimension of horizontal as well as vertical sections of AFM images are extracted by applying Higuchi’s algorithm. Value of Hurst exponent (H) for each sample is estimated from fractal dimension. It is found to be greater than 0.5 for Al as well as glass substrates, indicating that the height fluctuations at neighboring pixels are correlated positively. However, for Si substrate, its value is less than 0.5 which suggests that the height fluctuations at neighboring pixels are not positively correlated.
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Accardo Agostino, Cusenza Monica, De Felice Alberto, Fornasa Elisa, and D'Addio Giovanni. "Ultradian Rhythms During Day and Night in Normal and COPD Subjects." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-101-4-1120.

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The analysis of heart rate variability (HRV) is a powerful tool in the study of the autonomic control of the heart. While circadian HRV rhythms have widely been characterized by traditional spectral measures, ultradian oscillations are not commonly investigated. In this study the identification of HRV ultradian rhythms is assigned to a quantitative measure characterizing the fractal-like behavior of the time series: the fractal dimension (FD). In order to assess ultradian regulation in Chronic Obstructive Pulmonary Disease (COPD) 24-h Holter ECG recordings of 52 COPD and 10 normal (healthy) subjects were analyzed. The FD was calculated by Higuchi's algorithm both during daytime and nighttime to highlight possible wake-sleep states differences. All subjects showed a similar common rhythm (0.06mHz) that persists with generally higher amplitude during night-time. A further rhythm becomes predominant in normal subjects in the day-to-night transition (0.15mHz), probably under the influence of the REM/non-REM ultradian sleep cycle. A very large difference between night and day amplitudes of this rhythm and of the next one (at about 0.22mHz) characterize the HRV fractal dimension of the Normal in respect of COPD. In conclusion, the FD could be used as a marker of ultradian cardiac autonomic regulation providing new insights into autonomic physiology of normal and COPD patients.
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Conference papers on the topic "Higuchi fractal dimension"

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Brari, Zayneb, Ines Bouzouita, and Safya Belghith. "Higuchi versus Katz fractal dimensions based features extraction method for epilepsy diagnosis using EEG signals." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10639000.

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Chatterton, Steven, Paolo Pennacchi, Andrea Vania, Phuoc Vinh Dang, and Filippo Cangioli. "Diagnostics of Rolling Element Bearings by Means of the Higuchy Fractal Dimension." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46609.

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In the field of rolling element bearing, the degradation of bearing health could be detected by means of suitable damage indexes. Band-Kurtosis index, that is the kurtosis value of the band-filtered signal, is often assumed. The critical point of this approach is the selection of a suitable filter band. In the paper, the use of a chaos metrics, namely the Higuchi fractal dimension as damage indicator is described. The trend of this index is compared with the common approach of band-kurtosis indicator for an experimental case of a rolling element bearing in which the defect developed until a permanent failure.
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Topcu, Cagdas, Merve Bedeloglu, Arzu Akgul, et al. "Higuchi fractal dimension analysis of surface EMG signals and determination of active electrode positions." In 2014 18th National Biomedical Engineering Meeting (BIYOMUT). IEEE, 2014. http://dx.doi.org/10.1109/biyomut.2014.7026348.

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Wijayanto, Inung, Rudy Hartanto, and Hanung Adi Nugroho. "Higuchi and Katz Fractal Dimension for Detecting Interictal and Ictal State in Electroencephalogram Signal." In 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2019. http://dx.doi.org/10.1109/iciteed.2019.8929940.

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Al-nuaimi, Ali H., Emmanuel Jammeh, Lingfen Sun, and Emmanuel Ifeachor. "Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037320.

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Krakovska, Hana, and Anna Krakovska. "Problems of Estimating Fractal Dimension by Higuchi and DFA Methods for Signals That Are a Combination of Fractal and Oscillations." In 2021 13th International Conference on Measurement. IEEE, 2021. http://dx.doi.org/10.23919/measurement52780.2021.9446804.

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Rahmat, Abdullah Basuki, and Keiji Iramina. "Classification of multiclass eeg signal related to mental task using higuchi fractal dimension and 10-Statistic Parameters - Support Vector Machine." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7372967.

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Marri, Kiran, and Ramakrishnan Swaminathan. "Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9828.

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Abstract:
The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.
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Barbosa, Lara Martins, Ana Maria Amarillo Bertone, and Anielle G. Vaz Coelho. "Fisiologia fractal: a dimensão de Higuchi e suas aplicações." In XXXV CNMAC - Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2015. http://dx.doi.org/10.5540/03.2015.003.01.0075.

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Peya, Zahrul Jannat, Md Ferdous, M. A. H. Akhand, Mohammed Golam Zilani, and Nazmul Siddique. "ASD Detection using Higuchi’s Fractal Dimension from EEG." In 2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON). IEEE, 2021. http://dx.doi.org/10.1109/becithcon54710.2021.9893548.

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