Multiscale sample entropy for time resolved epileptic seizure detection and fingerprinting

Conigliaro, Davide; Manganotti, Paolo; Menegaz, Gloria

doi:10.1109/icassp.2014.6854268

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…The coarse grain procedure inspired a lot of derivatives of existing entropy measures. Conigliaro, Manganotti, and Menegaz [ 75 ] investigated the potential of multiscale sample entropy in seizure detection whereby the ‘multiscale’ analysis is done with a stationary wavelet transform instead of a simple moving average with multiple window lengths. It was found that the sample entropies in the δ and γ bands account for the main changes of signal structure during seizures, similar to the discovery in [ 41 ].…”

Section: Complexity In Epileptic Seizure Monitoringmentioning

confidence: 99%

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Mei

Xian

Chen

et al. 2018

Sensors

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Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.

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Section: Complexity In Epileptic Seizure Monitoringmentioning

confidence: 99%

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Mei

Xian

Chen

et al. 2018

Sensors

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show abstract

“…The initial step involves pre-processing raw EEG signals to eliminate artifacts originating from muscle or eye movements and electrical noise [6]. Then, time domain [7], frequency domain [8], temporospatial [9], and/or nonlinear [10] features can be extracted, and feature selection [11] could also be performed. Finally, machine learning models such as Logistic Regression (LR) [9] and Multilayer Perceptron (MLP) [12] can be used for seizure classification.…”

Section: Introductionmentioning

confidence: 99%

Mixture of Experts for EEG-Based Seizure Subtype Classification

Du,

Peng,

Liu

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…In a typical scenario with traditional machine learning approaches, EEG artifacts, e.g., eye/muscle movements and electrical noise, are first removed by band-pass filtering and detrending [7], [8]. Then, time domain features [9], frequency domain features [10], temporalspatial features [11], [12], or nonlinear features [13], can be extracted, selected [14], and finally sent to a classifier, e.g., support vector machine (SVM) [10], [15], logistic regression (LR) [12], or multilayer perceptron (MLP) [16], for classification [17].…”

Section: Introductionmentioning

confidence: 99%