Classification of EEG data sets with Hilbert transform

Şeker, Mesut; Özerdem, Mehmet Siraç

doi:10.1109/siu.2016.7496148

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To estimate Alpha Power, we followed previously employed strategies ( Şeker and Özerdem, 2016 ). For each 15 s segment we filtered the signal between 8 and 12 Hz (Butterworth filter, fourth order) and calculated its Hilbert transform (analytical signal).…”

Section: Methodsmentioning

confidence: 99%

The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction

Biggs

Boncompte

Pedemonte

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

BackgroundImproving anesthesia administration for elderly population is of particular importance because they undergo considerably more surgical procedures and are at the most risk of suffering from anesthesia-related complications. Intraoperative brain monitors electroencephalogram (EEG) have proved useful in the general population, however, in elderly subjects this is contentious. Probably because these monitors do not account for the natural differences in EEG signals between young and older patients. In this study we attempted to systematically characterize the age-dependence of different EEG measures of anesthesia hypnosis.MethodsWe recorded EEG from 30 patients with a wide age range (19–99 years old) and analyzed four different proposed indexes of depth of hypnosis before, during and after loss of behavioral response due to slow propofol infusion during anesthetic induction. We analyzed Bispectral Index (BIS), Alpha Power and two entropy-related EEG measures, Lempel-Ziv complexity (LZc), and permutation entropy (PE) using mixed-effect analysis of variances (ANOVAs). We evaluated their possible age biases and their trajectories during propofol induction.ResultsAll measures were dependent on anesthesia stages. BIS, LZc, and PE presented lower values at increasing anesthetic dosage. Inversely, Alpha Power increased with increasing propofol at low doses, however this relation was reversed at greater effect-site propofol concentrations. Significant group differences between elderly patients (>65 years) and young patients were observed for BIS, Alpha Power, and LZc, but not for PE.ConclusionBIS, Alpha Power, and LZc show important age-related biases during slow propofol induction. These should be considered when interpreting and designing EEG monitors for clinical settings. Interestingly, PE did not present significant age differences, which makes it a promising candidate as an age-independent measure of hypnotic depth to be used in future monitor development.

show abstract

Section: Methodsmentioning

confidence: 99%

The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction

Biggs

Boncompte

Pedemonte

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…Hjorth Complexity and other Hjorth parameters are commonly used as a feature extraction technique for the classification of EEG data [33], [34]. [35], [36].…”

Section: Feature Extractionmentioning

confidence: 99%

Feature Engineering for an Efficient Motor Related EcoG BCI System

Jain

Jaiman

Baths

2023

Preprint

View full text Add to dashboard Cite

Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG) signals require efficient recognition of spatio-temporal patterns from a multi electrodes sensor array. Such signals are excellent candidates for automated pattern recognition through machine learning algorithms. However, the available data is limited due to the operative procedure required for such dataset creation. The importance of different temporal signatures and individual electrodes can be analyzed through feature extraction techniques. But, the variability of the signal due to non-stationarity is ignored while extracting features and which features to use can be challenging to figure out by visual inspection. In this study, we introduce the signal split parameter to account for the variability of the signal, and we use genetic selection, which allows the selection of the optimal combination of features from a pool of 8 different feature sets.

show abstract

“…Es importante mencionar que la HT y la FFT son complementarias, siendo efectivas para señales de larga duración, aunque la HT tiene dificultades para distinguir entre transiciones de estado fisiológicas y discontinuidades espurias (deslizamientos de fase). La TH es eficaz para el análisis de señales de EEG, con aplicaciones en detección de ritmos cardíacos, clasificación de niveles de atención, sincronización neuronal y reconocimiento de patrones neurológicos (Le Van Quyen y otros, 2001) (Benitez y otros, 2001) (Şeker, Mesut and Özerdem, Mehmet Siraç, 2016). Los resultados de estos estudios subrayan la versatilidad de la TH, evidenciando su validez y eficacia para la extracción y clasificación de características de las señales de EEG, así como su capacidad para combinar con otras técnicas, como la Transformada de Hilbert-Huang, para mejorar el análisis (Dzianok y otros, 2021) (Peng y otros, 2020).…”

Section: Transformada De Hilbertunclassified

Exploration of source/sink phase connectivity patterns for the study of language processing through inter-channel causality of EEG signals

Rodríguez Rodríguez

View full text Add to dashboard Cite

Classification of EEG data sets with Hilbert transform

Cited by 7 publications

References 0 publications

The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction

The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction

Feature Engineering for an Efficient Motor Related EcoG BCI System

Exploration of source/sink phase connectivity patterns for the study of language processing through inter-channel causality of EEG signals

Contact Info

Product

Resources

About