Seizure prediction using adaptive neuro-fuzzy inference system

Rabbi, Ahmed; Azinfar, Leila; Fazel-Rezai, Reza

doi:10.1109/embc.2013.6609947

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…It was most applied for evaluating the EEG functional connectivity, seizure prediction of epilepsy patients in different brain regions [25,26]. However, few studies used the NI to characterize the EEG interdependence during anesthesia.…”

Section: Introductionmentioning

confidence: 99%

A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

Liang

Ren

Yan

et al. 2015

J Clin Monit Comput

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Electroencephalogram (EEG) synchronization is becoming an essential tool to describe neurophysiological mechanisms of communication between brain regions under general anesthesia. Different synchronization measures have their own properties to reflect the changes of EEG activities during different anesthetic states. However, the performance characteristics and the relations of different synchronization measures in evaluating synchronization changes during propofol-induced anesthesia are not fully elucidated. Two-channel EEG data from seven volunteers who had undergone a brief standardized propofol anesthesia were then adopted to calculate eight synchronization indexes. We computed the prediction probability (P K ) of synchronization indexes with Bispectral Index (BIS) and propofol effect-site concentration (C eff ) to quantify the ability of the indexes to predict BIS and C eff . Also, box plots and coefficient of variation were used to reflect the different synchronization changes and their robustness to noise in awake, unconscious and recovery states, and the Pearson correlation coefficient (R) was used for assessing the relationship among synchronization measures, BIS and C eff . Permutation cross mutual information (PCMI) and determinism (DET) could predict BIS and follow C eff better than nonlinear interdependence (NI), mutual information based on kernel estimation (KerMI) and cross correlation. Wavelet transform coherence (WTC) in α and β frequency bands followed BIS and C eff better than that in other frequency bands. There was a significant decrease in unconscious state and a significant increase in recovery state for PCMI and NI, while the trends were opposite for KerMI, DET and WTC. Phase synchronization based on phase locking value (PSPLV) in δ, θ, α and γ1 frequency bands dropped significantly in unconscious state, whereas it had no significant synchronization in recovery state. Moreover, PCMI, NI, DET correlated closely with each other and they had a better robustness to noise and higher correlation with BIS and C eff than other synchronization indexes. Propofol caused EEG synchronization changes during the anesthetic period. Different synchronization measures had individual properties in evaluating synchronization changes in different anesthetic states, which might be related to various forms of neural activities and neurophysiological mechanisms under general anesthesia.

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Section: Introductionmentioning

confidence: 99%

A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

Liang

Ren

Yan

et al. 2015

J Clin Monit Comput

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“…In each 20-second long window, the power spectrum of all 4 bipolar EEG recordings was estimated using Welch's method. Power was extracted in 9 spectral bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13-30 Hz), 4 gamma bands 29 (30-47, 53-75, 75-97, and 103-128 Hz), and their total. The power of the first 8 bands was divided by the total power, which resulted in 36 spectral power features.…”

Section: Feature Extractionmentioning

confidence: 99%

“…[6][7][8][9][10] Recently, some other studies used more complex classifiers in their seizure prediction analysis. [11][12][13] The threshold-crossing method and more complex classifiers can obtain significantly better prediction performance than the random predictor in their studies. 6,7,12 Theoretically, a more complex classifier should have the ability to improve prediction performance because it takes all extracted features into consideration simultaneously at certain time points.…”

Section: Introductionmentioning

confidence: 99%

Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?

Yang

Wang

2016

Clin EEG Neurosci

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Objective This study aims to verify whether the simple threshold-crossing method can work well enough to achieve the realistic application of seizure prediction on the basis of a large public database, and examines how a more complex classifier can improve prediction performance. It also verified whether the combination of multiple types of features with a complex classifier can improve prediction performance. Method Phase synchronization and spectral power features were extracted from electroencephalogram recordings. The threshold-crossing method and a support vector machine (SVM) were used to identify preictal and interictal samples. Based on the type of selected features and the manner of classification, 5 different methods were conducted on 19 patients. The performances of these methods were directly compared and tested using a random predictor. In-sample optimization problems were avoided in the feature and parameter selection procedure to obtain credible results. Results The threshold-crossing method could only obtain satisfying prediction results for approximately half of the selected patients. The SVM classifier could significantly improve prediction performance compared with the threshold-crossing method for both types of features. Although the average performance was further improved when both types of features were combined with the SVM classifier, the improvement was insignificant. Conclusion A complex classifier, such as the SVM, is recommended in a realistic prediction device, although it will increase the complexity of the device. Indeed, the simple threshold-crossing method performs well enough for some of the patients. The combination of phase synchronization and spectral power features is unnecessary because of the increased computation complexity.

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“…For classification, a drift towards machine learning is observed in recent research [24][25][26][27][28]. Gadhoumi et.al, used high frequency signals from brain to extract features such as wavelet entropy and energy, using sliding windows, to classify between preictal and interictal signals [30].…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning for seizure prediction: A revamped approach

A¹,

Nigam²,

Karnam³

et al. 2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Occurrence of multiple seizures is a common phenomenon observed in patients with epilepsy: a neurological malfunction that affects approximately 50 million people in the world. Seizure prediction is widely acknowledged as an important problem in the neurological domain, as it holds promise to improve the quality of life for patients with epilepsy. A noticeable number of clinical studies showed evidence of symptoms (patterns) before seizures and thus, there is large research on predicting seizures. There is very little existing literature that systematically illustrates the steps in machine learning for seizure prediction, limited training data and class imbalance are a few challenges. In this paper, we propose a novel way to overcome these challenges. We present the improved results for various classification algorithms. An average of 21.71% improvement in accuracy is attained using our approach.

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Seizure prediction using adaptive neuro-fuzzy inference system

Cited by 13 publications

References 17 publications

A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?

Machine learning for seizure prediction: A revamped approach

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