Add to ORKGCopyright © 2013 Sun-Hee Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity.Therefore, it is important to find a suitable forecastingmethod that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique...
Epilepsy is the second most common neurological disorder, affecting 0.6–0.8 % of the world’s populat...
Epilepsy is a common neurological disease that affects a wide range of the world population and is n...
The random looking brain electrical activity patterns recorded as EEG is currently understood to be ...
Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable c...
International audienceEpilepsy is one of the most widespread neurological pathologies, which is char...
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underly...
In the context of time series analysis, forecasting time series is known as an important sub-study f...
Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many r...
OBJECTIVE Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Yet, ...
For several decades, researchers are aiming for the detection of precursors of epileptic seizures. A...
Objective Seizure unpredictability is rated as one of the most challenging aspects of living with ep...
This paper proposes an artificial intelligence system that continuously improves over time at event ...
It is now established that epilepsy is characterized by periodic dynamics that increase seizure like...
In this work, we explore the topic of forecasting the neural time series using machine-learning base...
None of the current epileptic seizure prediction methods can widely be accepted, due to their poor c...
Epilepsy is the second most common neurological disorder, affecting 0.6–0.8 % of the world’s populat...
Epilepsy is a common neurological disease that affects a wide range of the world population and is n...
The random looking brain electrical activity patterns recorded as EEG is currently understood to be ...
Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable c...
International audienceEpilepsy is one of the most widespread neurological pathologies, which is char...
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underly...
In the context of time series analysis, forecasting time series is known as an important sub-study f...
Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many r...
OBJECTIVE Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Yet, ...
For several decades, researchers are aiming for the detection of precursors of epileptic seizures. A...
Objective Seizure unpredictability is rated as one of the most challenging aspects of living with ep...
This paper proposes an artificial intelligence system that continuously improves over time at event ...
It is now established that epilepsy is characterized by periodic dynamics that increase seizure like...
In this work, we explore the topic of forecasting the neural time series using machine-learning base...
None of the current epileptic seizure prediction methods can widely be accepted, due to their poor c...
Epilepsy is the second most common neurological disorder, affecting 0.6–0.8 % of the world’s populat...
Epilepsy is a common neurological disease that affects a wide range of the world population and is n...
The random looking brain electrical activity patterns recorded as EEG is currently understood to be ...