Wavelet based automatic seizure detection in intracerebral electroencephalogram

ArticleinClinical Neurophysiology 114(5):898-908 · June 2003with19 Reads
DOI: 10.1016/S1388-2457(03)00035-X · Source: PubMed
Automatic seizure detection is often used during long-term monitoring, and is particularly important during intracerebral investigations. Existing methods make many false detections, particularly in intracerebral electroencephalogram (EEG) because of frequent large amplitude rhythmic activity bursts that are non-epileptiform. To develop a seizure detection method for intracerebral monitoring that is as sensitive as existing methods but has fewer false detections. To capture the rhythmic nature of seizure discharges, we developed a wavelet-based method, examining how different frequency ranges fluctuate compared to the background. In particular, the system remembers rhythmic bursts occurring commonly in the background to avoid detecting them as seizures. The method was evaluated on test data from 11 patients, including 229 h and 66 seizures, and its performance compared to the method of Gotman (Electroencephalogr clin Neurophysiol 76 (1990) 317). Detection sensitivity was unchanged at close to 90%, but false detections were reduced from 2.4 to 0.3/h. Perfect sensitivity is unlikely because the morphology of seizure discharges is so variable. Nevertheless, the 87% sensitivity obtained in the combined training and testing data is quite high. We reduced the average false alarm rate to one per 3 h of recording, or 6 per 24-h period. Given how rapidly one can decide visually that a detection is erroneous, false detections should not cause any burden to the reviewer. In intracerebral EEG it is possible to detect seizures automatically with high sensitivity and high specificity.
    • "Thus the six levels DWT with Daubechies order 4 is performed in this study. The relative energy (or ratio of coefficients), e i of each DWT level, with i = 1, 2, ..., 6, are computed as in [18]. It is termed as the feature set, DW T ratio . "
    Full-text · Conference Paper · Aug 2016 · IEEE Transactions on Biomedical Engineering
    • "Frequency is a key characteristic that has been used in the literature to define abnormality in brain signals. In [7] , the authors present a waveletbased algorithm to examine how different frequency ranges in iEEG fluctuate from the background. Authors in [8] propose an algorithm based on artificial neural networks for classification of EEG signals into healthy, ictal, and interictal. "
    [Show abstract] [Hide abstract] ABSTRACT: Real-time detection of seizure activities in epileptic patients is crucial and can help improve patients’ quality of life. Accurate evaluation, pre-surgery assessments, seizure prevention, and emergency alerts for medical aid all depend on the rapid detection of the onset of seizures. A new method of feature selection and classification for rapid and precise epileptic seizure detection is discussed. In this solution, informative components of Electroencephalogram (EEG) data are extracted and an automatic method is presented using Infinite Independent Component Analysis (I-ICA) to select efficiently independent features. The feature space is divided into subspaces via random selection, and multi-channel Support Vector Machines (SVMs) are used to classify the subspaces; then, the result of each classifier is combined by majority voting to find the final output. To evaluate the solution, a benchmark clinical intracranial EEG (iEEG) of eight patients with temporal and extratemporal lobe epilepsy has been considered in a multi-tier cloud-computing architecture. Via the leave-one-out cross-validation, accuracy, sensitivity, specificity, and false positive and false negative ratios of the proposed method are 0.95, 0.96, 0.94, 0.06, and 0.04, respectively, which confirm the effectiveness of the proposed solution. Index Terms—Brain-Computer Interface; Cloud Computing; Electroencephalogram; Epileptic Seizures; Pervasive Computing.
    Full-text · Conference Paper · Jul 2016 · IEEE Transactions on Biomedical Engineering
    • "Since the EEG is non-stationary in general, it is most appropriate to use the time-frequency domain methods like the discrete wavelet transforms (DWT) analysis to describe EEG in the time and frequency domain. In this study we select Daubechies-4 wavelet for the analysis of epileptic EEG [20]. Using wavelet decomposition, we first decomposed the EEG data into the four frequency sub-bands of í µí»¿ (0Hz − 4 Hz), í µí¼ƒ (4Hz − 7Hz), í µí»¼ (8HZ − 15HZ), and í µí»½ (16Hz − 30Hz). "
    Full-text · Article · Apr 2016
Show more

  • undefined · undefined
  • undefined · undefined
  • undefined · undefined