Online Prediction of Lead Seizures from iEEG Data.

We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, the machine learning part of the system is implemented using the group learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with the non-stationarity of a noisy iEEG signal. They include: (1) periodic retraining of the SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; and (3) introducing a new adaptive post-processing technique for combining many predictions made for 20 s windows into a single prediction for a 4 h segment. Application of the proposed system requires only two lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). The proposed system achieves accurate prediction of lead seizures during long-term test periods, 3-16 lead seizures during a 169-364 day test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. METHODS: Our work emphasizes the understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during preprocessing and postprocessing are considered and investigated for their effect on seizure prediction accuracy. RESULTS: Our empirical results show that the proposed support vector machine-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90-100%, and the false-positive rate is about 0-0.3 times per day. The results also suggest that good prediction is subject specific (dog or human), in agreement with earlier studies. CONCLUSION: Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5-7 seizures. SIGNIFICANCE: The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages.

Hemoperitoneum due to Splenic Laceration Caused by Colonoscopy: A Rare and Catastrophic Complication.

Numerous studies suggest that in asymptomatic patients, routine follow-up CT is not indicated due to the insignificant findings found on these patients. A 53-year-old man, who denied any underlying disease before, underwent colonoscopy for routine health examination. Sudden onset of abdominal pain around left upper quarter was mentioned at our emergency department. Grade II spleen laceration was found on CT scan. Splenic injury was found few hours later on the day of colonoscopy. It might result from the extra tension between the spleen and splenic flexure which varies from different positions of patients.