Comparison between spectral and fractal EEG analyses of sleeping newborns

Accardo, Agostino; Affinito, M.; Carrozzi, Marco; Cisint, S.; Bouquet, F

doi:10.1109/iembs.1998.747191

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Thus their frequency spectra exhibit a 1/f-like profile, so the characterizing EEG data from a viewpoint of local irregularity using fractal theory might be useful [12]. In the past decade, the fractal dimension (FD) analysis of EEG data has been applied to various kinds of biomedical researches, such as the routine detection of dementia [12,13], EEG analysis of sleeping newborns [14], fractal spectral analysis of preepileptic seizures [15], and the clinical problems of acute stroke detection [16].…”

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confidence: 99%

Fractal-Based EEG Data Analysis of Body Parts Movement Imagery Tasks

Phothisonothai

Nakagawa

2007

J. Physiol. Sci

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Abstract:The objective of this study is to analyze the spontaneous electroencephalographic (EEG) data corresponding to body parts movement imagery tasks in terms of fractal properties. We proposed the six algorithms of fractal dimension (FD) estimators; box-counting algorithm, Higuchi algorithm, variance fractal algorithm, detrended fluctuation analysis, power spectral density analysis, and critical exponent analysis. The different parts of human body movement imagination such as feet, tongue, and index finger are proposed for use as the tasks in this experiment.The EEG data were recorded from three healthy subjects (2 males and 1 female). The experimental results are useful in the measurement of FD changes in EEG data and present different characteristics in terms of variability. The probability density function (PDF) is also applied to show that the FD distribution is along each electrode. This study proposes that the performances of each method can extract information from the EEG data of imagined movement.

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confidence: 99%

Fractal-Based EEG Data Analysis of Body Parts Movement Imagery Tasks

Phothisonothai

Nakagawa

2007

J. Physiol. Sci

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“…• Hurst Exponent: It is used to measure the self-similarity in a time series. It evaluates the presence or absence of long range dependencies and irregularity in a time series [61]. Its value ranges between 0 and 1; higher values show a smoother signal with less roughness.…”

Section: ) Non-linear Entropy Based Featuresmentioning

confidence: 99%

A Systematic Review of Literature on Automated Sleep Scoring

Alsolai

Qureshi

Iqbal³

et al. 2022

IEEE Access

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Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

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“…Texture features based on fractal dimensions have been widely applied in image analysis for texture classification (Lee et al, 2003). In the last decade, feature extraction characterized by fractal dimension of EEG signals has been applied to various kinds of biomedical signal analyses such as routine detection of dementia (Henderson et al, 2000), seizure onset detection in epilepsy (Esteller et al, 1999;Gangadhar et al, 1995;Kirlangic et al, 2001) and EEG analyses of sleeping newborns (Accardo et al, 1998). However, not many fractal cases have been reported for BCI from EEG signals (Boostani and Moradi, 2004;Craig et al, 2005).…”

Section: Introductionmentioning

confidence: 99%