Journal Article10.1109/81.904882
A comparison of waveform fractal dimension algorithms
TL;DR: This study demonstrates that a careful selection of fractal dimension algorithm is required for specific applications, and the most common methods of estimating the fractaldimension of biomedical signals directly in the time domain are analyzed and compared.
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Abstract: The fractal dimension of a waveform represents a powerful tool for transient detection. In particular, in analysis of electroencephalograms and electrocardiograms, this feature has been used to identify and distinguish specific states of physiologic function. A variety of algorithms are available for the computation of fractal dimension. In this study, the most common methods of estimating the fractal dimension of biomedical signals directly in the time domain (considering the time series as a geometric object) are analyzed and compared. The analysis is performed over both synthetic data and intracranial electroencephalogram data recorded during presurgical evaluation of individuals with epileptic seizures. The advantages and drawbacks of each technique are highlighted. The effects of window size, number of overlapping points, and signal-to-noise ratio are evaluated for each method. This study demonstrates that a careful selection of fractal dimension algorithm is required for specific applications.
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Citations
Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher's Criterion-Based Channel Selection.
TL;DR: The Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients are introduced and a Fisher's criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites.
Automatic sleep stage classification using physiological signals acquired by Dreem headband
TL;DR: The proposed model shows promising results, therefore the model can be implemented in Dreem headband to differentiate between sleep states efficiently and be applicable in clinical trial.
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Fractals and the analysis of waveforms
TL;DR: The fractal characterization may be especially useful for analyzing and comparing complex waveforms such as electroencephalograms (EEGs), where the x values increase monotonically.
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