Automatic seizure detection is of great significance for epilepsy long-term monitoring, diagnosis, and rehabilitation, and it is the key to closed-loop brain stimulation. This paper presents a novel wavelet-based automatic seizure detection method with high sensitivity. The proposed method first conducts wavelet decomposition of multi-channel intracranial EEG (iEEG) with five scales, and selects three frequency bands of them for subsequent processing. Effective features are extracted, such as relative energy, relative amplitude, coefficient of variation and fluctuation index at the selected scales, and then these features are sent into the support vector machine for training and classification. Afterwards a postprocessing is applied on the raw classification results to obtain more accurate and stable results. Postprocessing includes smoothing, multi-channel decision fusion and collar technique. Its performance is evaluated on a large dataset of 509 h from 21 epileptic patients. Experiments show that the proposed method could achieve a sensitivity of 94.46% and a specificity of 95.26% with a false detection rate of 0.58/h for seizure detection in long-term iEEG.

Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.

No abstract

Reliable prediction of forthcoming seizures will be a milestone in epilepsy research. A method capable of timely predicting the occurrence of seizures could significantly improve the quality of life for epilepsy patients and open new therapeutic approaches. Seizures are usually characterized by generalized spike wave discharges. With the advent of seizures, the variation of spike rate (SR) will have different manifestations. In this study, a seizure prediction approach based on spike rate is proposed and evaluated. Firstly, a low-pass filter is applied to remove the high frequency artifacts in electroencephalogram (EEG). Then, the morphology filter is used to detect spikes and compute SR, and SR is smoothed with an average filter. Finally, the performance of smoothed SR (SRm) in EEG during interictal, preictal, and ictal periods is analyzed and employed as an index for seizure prediction. Experiments with long-term intracranial EEGs of 21 patients show that the proposed seizure prediction approach achieves a sensitivity of 75.8% with an average false prediction rate of 0.09/h. The low computational complexity of the proposed approach enables its possibility of applications in an implantable device for epilepsy therapy.