Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction.

OBJECTIVE: In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS: Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS: Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS: We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE: To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurrent and unpredictable interruption of healthy brain function. Diagnosis of patients with epilepsy requires monitoring and visual inspection of long-term EEG by the neurologist, which is found to be a time-consuming procedure. Therefore, this study proposes an automated seizure detection model using a novel computationally efficient feature named sigmoid entropy derived from discrete wavelet transforms. The sigmoid entropy was estimated from the wavelet coefficients in each sub-band and classified using a non-linear support vector machine classifier with leave-one-subject-out cross-validation. The performance of the proposed method was tested with the Ramaiah Medical College and Hospital (RMCH) database, which consists of the 58 Hours of EEG from 115 subjects, the University of Bonn (UBonn), and CHB-MIT databases. Results showed that sigmoid entropy exhibits lower values for epileptic EEG in contrary to other existing entropy methods. We observe a seizure detection rate of 96.34%, a false detection rate of 0.5/h and a mean detection delay of 1.2 s for the RMCH database. The highest sensitivity of 100% and 94.21% were achieved for UBonn and CHB-MIT databases respectively. The performance comparison confirms that sigmoid entropy was found to be better and computationally efficient as compared to other entropy methods. It can be concluded that the proposed sigmoid entropy could be used as a potential biomarker for recognition and detection of epileptic seizures.

A Novel Approach for Real-Time Recognition of Epileptic Seizures Using Minimum Variance Modified Fuzzy Entropy.

OBJECTIVE: Validation of epileptic seizures annotations from long-term electroencephalogram (EEG) recordings is a tough and tedious task for the neurological community. It is a well-known fact that computerized qualitative methods thoroughly assess the complex brain dynamics toward seizure detection and proven as one of the acceptable clinical indicators. METHODS: This research study suggests a novel approach for real-time recognition of epileptic seizure from EEG recordings by a technique referred as minimum variance modified fuzzy entropy (MVMFzEn). Multichannel EEG recordings of 4.36 h of epileptic seizures and 25.74 h of normal EEG were considered. Signal processing techniques such as filters and independent component analysis were appropriated to reduce noise and artifacts. Unlike, the predefined fuzzy membership function, the modified fuzzy entropy utilizes relative energy as a membership function followed by scaling operation to obtain the feature. RESULTS: Results revealed that MVMFzEn drops abruptly during an epileptic activity and this fact was used to set a threshold. An automated threshold derived from MVMFzEn assesses the classification efficiency of the given data during validation. It was observed from the results that the proposed method yields a classification accuracy of 100% without the use of any classifier. CONCLUSION: The graphical user interface was designed in MATLAB to automatically label the normal and epileptic segments in the long-term EEG recordings. SIGNIFICANCE: The ground truth clinical validation using validation specificity and validation sensitivity confirms the suitability of the proposed technique for automated annotation of epileptic seizures in real time.

Extrapontine Myelinolysis and Reversible Parkinsonism After Hyponatremia Correction in a Case of Pituitary Adenoma: Hypopituitarism as a Predisposition for Osmotic Demyelination.

BACKGROUND: Osmotic demyelination syndrome commonly follows rapid correction of hyponatremia. Although pons is a common location, extrapontine locations, such as striatum and thalamus, have been reported. CASE DESCRIPTION: A 48-year-old woman presented with masked facies, shuffling gait, and pill-rolling tremors suggestive of acute-onset parkinsonism. Hyponatremia was diagnosed following a bout of diarrhea, which was corrected with hypertonic saline. Magnetic resonance imaging of the brain showed a giant pituitary adenoma. Hyperintensities on T2-weighted imaging were also seen at the level of pons and bilateral striatum. Central pontine myelinolysis and extrapontine myelinolysis were diagnosed. Hormonal assay showed hypocortisolism, secondary hypothyroidism, and hypogonadism. The patient was started on levodopa-carbidopa, steroids, and thyroxine. She underwent transnasal pituitary adenoma excision. At 6 months postoperatively, she had recovered completely with normal gait. Repeat imaging showed complete resolution of myelinolysis. At 36 months, she continued to have hypocortisolism and hypothyroidism requiring replacement. CONCLUSIONS: Extrapontine myelinolysis with parkinsonism and asymptomatic central pontine myelinolysis is rare with few cases described in the literature. Our patient had a pituitary adenoma with hyponatremia requiring sodium correction, and we believe that hypopituitarism might have predisposed her to osmotic demyelination. We reviewed relevant literature on extrapontine myelinolysis in suprasellar tumors and the pathophysiology. Hypopituitarism is an underrecognized cause of hyponatremia. When treating a patient with hyponatremia, knowing the pituitary function status is a prerequisite for the physician to prevent osmotic demyelination syndrome.

Anaesthetic management of a child with neurofibromatosis type 2 for multiple tumour decompressions.

Neurofibromatosis type 2 is a genetic disorder with autosomal dominant pattern. It can manifest as intracranial, spinal, ocular and cutaneous lesions. The lesions can extend to all the systems. We present an anaesthetic management of a paediatric patient with neurofibromatosis 2 for multiple spinal and thoracic tumour decompression.