Wavelet based automatic seizure detection in intracerebral electroencephalogram

Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Room 767, 3801 University Street, Quebec, Canada H3A 2B4.
Clinical Neurophysiology (Impact Factor: 3.1). 06/2003; 114(5):898-908. 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.

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    • "A wavelet decomposition results in components at multiple levels of resolution (computational microscopes) [56]. This system has been evaluated in both intracranial and surface EEG recordings [13] [57] [58]. Principal component analysis (PCA) Identify the set of principal components using orthogonal transformation and sorted by variance. "
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    ABSTRACT: Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.
    Epilepsy & Behavior 08/2014; 37C:291-307. DOI:10.1016/j.yebeh.2014.06.023 · 2.26 Impact Factor
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    • "For analysis and classification of seizure EEG signals timefrequency based methods also include wavelet transform [11] [12] [13] [14] [15] [16] [17] and multiwavelet transform [18]. "
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    ABSTRACT: The electroencephalogram (EEG) signals are commonly used signals for detection of epileptic seizures. In this paper, we present a new method for classification of two classes of EEG signals namely focal and non-focal EEG signals. The proposed method uses the sample entropies and variances of the intrinsic mode functions (IMFs) obtained by empirical mode decomposition (EMD) of EEG signals. The average sample entropy (ASE) of IMFs and average variance of instantaneous frequencies (AVIF) of IMFs for separate EEG signals have been used as features for classification of focal and non-focal EEG signals. These two parameters have been used as an input feature set to the least square support vector machine (LS-SVM) classifier. The experimental results for various IMFs of focal and non-focal EEG signals have been included to show the effectiveness of the proposed method. The proposed method has provided promising classification accuracy for classification of focal and non-focal seizure EEG signals when radial basis function (RBF) has been employed as a kernel with LS-SVM classifier.
    IEEE International Conference on Medical Biometrics, Shenzhen, China; 05/2014
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    • "In 1982 Gotman proposed a remarkable work on seizure detection [5]. Khan and Gotman proposed a wavelet based method for classification of epileptic and nonepileptic data [17]. In 2005 wavelet transform method and short time Fourier transform method were compared to determine their accuracy to determine the epileptic seizures. "
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    ABSTRACT: This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds.
    01/2014; 2014:450573. DOI:10.1155/2014/450573
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