Support vector machines for seizure detection in an animal model of chronic epilepsy

@article{Nandan2010SupportVM,
  title={Support vector machines for seizure detection in an animal model of chronic epilepsy},
  author={Manu Nandan and Sachin S. Talathi and Stephen M. Myers and William L. Ditto and Pramod P. Khargonekar and Paul R. Carney},
  journal={Journal of Neural Engineering},
  year={2010},
  volume={7},
  pages={036001}
}
We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 kHz) from two groups of rats with chronic epilepsy were used in this study. For each of these EEG datasets, we extracted three energy-based seizure detection features: mean energy, mean curve length… 

Clinical Validation High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With

This work proposes an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive features selection, and support vector machine for feature classification, which verified the high performance and usefulness of this cascade system for seizure detection.

High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validation

This work proposes an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive features selection, and support vector machine for feature classification, which verified the high performance and usefulness of this cascade system for seizure detection.

Automated seizure detection from multichannel EEG signals using Support Vector Machine and Artificial Neural Networks

The proposed method is a generalized seizure detection method which is not patient specific and has an average detection accuracy of nearly 75%.

A Realistic Seizure Prediction Study Based on Multiclass SVM

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented, which aims at the generation of alarms and reduced influence of false positives.

Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection

An adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity and a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.

A fast and efficient method for detection of seizure in electroencephalogram using log-energy entropy and support vector machine

It is found that the type of epilepsy is the major factor, which influenced the performance of the method, and the high performance makes it feasible also for real-time applications.

References

SHOWING 1-10 OF 44 REFERENCES

One-Class Novelty Detection for Seizure Analysis from Intracranial EEG

The novelty detection paradigm overcomes three significant limitations of competing methods: the need to collect seizure data, precisely mark seizure onset and offset times, and perform patient-specific parameter tuning for detector training.

Support vector machines for seizure detection

The use of SVMs achieves high sensitivity and at the same time shows an improvement in terms of computational speed in comparison with the other traditional systems.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function

A system to detect the onset of epileptic seizures in scalp EEG

An SVM-based system and its performance for detection of seizures in neonates

This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and

Seizure prediction: the long and winding road.

A critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms, and point towards possible future developments and propose methodological guidelines for future studies on seizure predictions.

Detecting Epileptic Seizures in Long-term Human EEG: A New Approach to Automatic Online and Real-Time Detection and Classification of Polymorphic Seizure Patterns

A novel procedure for generic, online, and real-time automatic detection of multimorphologic ictal-patterns in the human long-term EEG and its validation in continuous, routine clinical EEG recordings from 57 patients and additional 1,360 hours of seizure-free EEG data for the estimation of the false alarm rates.