An Efficient Epilepsy Prediction Model on European Dataset With Model Evaluation Considering Seizure Types.

This paper develops a computationally efficient model for automatic patient-specific seizure prediction using a two-layer LSTM from multichannel intracranial electroencephalogram time-series data. We decrease the number of parameters by employing a smaller input size and fewer electrodes, thereby making the model a viable option for wearable and implantable devices. We test the proposed prediction model on 26 patients from the European iEEG dataset, which is the largest epileptic seizure dataset.

Video coding deep learning-based modeling for long life video streaming over next network generation.

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.

Relationship between epistaxis and hypertension: A cause and effect or coincidence?

Introduction: Epistaxis is the most common otorhinolaryngological emergency. Whether there is an association or cause and effect relationship between epistaxis and hypertension is a subject of longstanding controversy.

Objective: The aim of our study is to evaluate the relationship between epistaxis and hypertension.