Construction of rules for seizure prediction based on approximate entropy

Zhang, Zhen; Chen, Ziyi; Zhou, Yi; Du, Sidan; Zhang, Yang; Tian, Mei; Tian, Xianghua

doi:10.1016/j.clinph.2014.02.017

Cited by 58 publications

(21 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This software application was previously presented in [52]. Current efforts in most entropy-based EEG studies are still mainly focused on the identification of certain patterns (such as seizure detection [53]). Other works employ entropy as a metric for diagnostic purposes, to discriminate among subjects with neural alterations (citeGarn2014 Bachiller2014.…”

Section: Discussionmentioning

confidence: 99%

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

106

View full text Add to dashboard Cite

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

show abstract

Section: Discussionmentioning

confidence: 99%

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

106

View full text Add to dashboard Cite

show abstract

“…The PA is defined in [41] as: 13) where Ns is the number of correctly predicted seizures and Na is the total number of seizures.…”

Section: Resultsmentioning

confidence: 99%

Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals Using Phase Correlation

Parvez

Paul

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and prediction. This paper proposes a new seizure prediction approach based on spatiotemporal relationship of EEG signals using phase correlation. This measures the relative change between current and reference vectors of EEG signals which can be used to identify preictal/ictal (before the actual seizure onset/ actual seizure period) and interictal (period between adjacent seizures) EEG signals to predict the seizure. The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy (i.e., 91.95%) and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.

show abstract

“…ApEn algorithm has some anti-noise ability itself, and thus effect of different filtering methods can be reduced [19,31]. The amount of data is not so demanding (500-4000 points is generally enough) and some fast computation of ApEn has been developed, which makes the execution time shorter than other nonlinear algorithm [32,33].…”

Section: The Apen Extraction Algorithmmentioning

confidence: 99%

“…One of them is approximate entropy (ApEn), a simple and straightforward nonlinear index, which can be used to measure the complexity of signals and quantify statistics [15][16][17][18]. In the past few years, ApEn has been extensively applied in the analysis of regularity and complexity of short-term physiological time series such as heart rate, blood pressure, and electroencephalogram signals [19]. For example, ApEn was used to recognize the alteration of different sleep stages [20] and serving as an assistant index to study hypothermia [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Neural Oscillations on Drosophila’s Subesophageal Ganglion Based on Approximate Entropy

Tian

Qiao

Zhou

et al. 2015

Entropy

Self Cite

View full text Add to dashboard Cite

Abstract:The suboesophageal ganglion (SOG), which connects to both central and peripheral nerves, is the primary taste-processing center in the Drosophila's brain. The neural oscillation in this center may be of great research value yet it is rarely reported. This work aims to determine the amount of unique information contained within oscillations of the SOG and describe the variability of these patterns. The approximate entropy (ApEn) values of the spontaneous membrane potential (sMP) of SOG neurons were calculated in this paper. The arithmetic mean (MA), standard deviation (SDA) and the coefficient of variation (CVA) of ApEn were proposed as the three statistical indicators to describe the irregularity and complexity of oscillations. The hierarchical clustering method was used to classify them. As a result, the oscillations in SOG were divided into five categories, including: (1) Continuous spike pattern; (2) Mixed Steady oscillation state has a low level of irregularity, and vice versa. The dopamine stimulation can distinctly cut down the complexity of the mixed oscillation pattern. The current study provides a quantitative method and some critera on mining the information carried in neural oscillations.

show abstract

Construction of rules for seizure prediction based on approximate entropy

Cited by 58 publications

References 24 publications

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals Using Phase Correlation

Analysis of Neural Oscillations on Drosophila’s Subesophageal Ganglion Based on Approximate Entropy

Contact Info

Product

Resources

About