Automatic detection of absence seizures with compressive sensing EEG

Abstract

Automatic detection of absence seizures with compressive sensing EEG, Neurocomputing, http://dx. AbstractAbsence epilepsy, a neurological disorder, is characterized by the recurrence of seizures, which have serious impact on the sufferers' daily life. The seizure detection has a great importance in the diagnosis and therapy of epileptic patients. Visual inspection of the electroencephalogram (EEG) signals for detection of interictal, pre-ictal and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. In this study, we proposed a novel automatic detection method based on the altered compressibility of EEG during the three states with compressive sensing. To evaluate the proposed method, segments of interictal, pre-ictal and ictal EEG segments (100 segments in each state) were used. Two entropies, namely the Sample Entropy (SE) and the permutation Entropy (PE), and Hurst Index (HI) were extracted respectively from the segments to compare with the proposed method. Significant features were selected using the ANOVA test. After evaluating the performance of the selected features by four classifiers (Decision Tree, K-Nearest Neighbor, Discriminant Analysis, Support Vector Machine) respectively, the results show that the proposed method can achieve the highest accuracy of 76.7%, which is higher than HI (55.3%), sample entropy (71%), and permutation entropy (73%). Hence, the altered compressibility of EEG with CS can act as a good biomarker for distinguish seizure-free, per-seizure and seizure state. In addition, compressive sensing requires less energy but has competitive compression ratio compared to traditional compression techniques, which enables our method to tele-monitoring of epilepsy patients using wireless body-area networks in personalized medicine.

Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

“…The EEG dataset was recorded from pediatric subjects with intractable seizures at Children's Hospital Boston. This database contains 22 subjects (17 females, ages 1.5-19; 5 males, ages [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and can be downloaded from the PhysioNet website: http://www .physionet.org/pn6/chbmit/. The International 10-20 system of EEG electrode positions and nomenclature was used to collect these EEG recordings.…”

“…DA performs classification by minimizing the within-class covariance and simultaneously maximizing the between-class covariance. There are two reasons why we choose DA in this work: (1) DA has preferable performance than other classifiers (such as decision tree and support vector machine) in seizure detection [3,15]; (2) DA is a nonparameter classification method, which is very convenient for clinicians to build the basis for patient-specific detection. In addition, quadratic discriminant analysis (QDA) is used to distinguish between seizure and seizure-free phases in this work, as QDA has more predictability power than linear discriminant analysis (LDA).…”

“…Frequent seizures bring individual's risk of continuing physical injuries and may even lead to death. Electroencephalography (EEG) is an effective and noninvasive technique for directly recording the brain electrical activity and diagnosing clinical brain diseases [3]. Abnormal epileptic waveforms appear in EEG signal during the epileptic seizure phase.…”

“…Though the entropy-based complexity methods have shown promising results on detection of epileptic seizures [3,12,15,16], it is still an open problem how to bring out the full potential of complexity to enhance the reliability and accuracy of EEG analysis. Meanwhile, some methods only have promising results for selected patients with seizure of single type (such as temporal lobe seizure); the reproducibility and reliability of the results have been questioned when being tested on other patients with numerous seizure types [17].…”

Global Synchronization of Multichannel EEG Based on Rényi Entropy in Children with Autism Spectrum Disorder

“…Currently, an increasing amount of evidence, from physiological and electrophysiological studies, has proven that abnormalities in the synchronous oscillatory activity of neurons may dominate in the pathophysiology of brain disorder [15,16,20,21], and these will be reflected in EEG signals. Therefore, to evaluate the cooperation strength between neurons or cortical networks, a common approach is to measure them from EEG signals.…”