Automated epileptic seizure detection methods: a review study
Epilepsy is a neurological disorder with prevalence of about 1-2% of the world's population
(Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and …
(Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and …
Neural networks for computer-aided diagnosis in medicine: a review
This survey makes an overview of the most recent applications on the neural networks for
the computer-aided medical diagnosis (CAMD) over the past decade. CAMD can facilitate …
the computer-aided medical diagnosis (CAMD) over the past decade. CAMD can facilitate …
Epileptic seizure detection in EEGs using time–frequency analysis
The detection of recorded epileptic seizure activity in EEG segments is crucial for the
localization and classification of epileptic seizures. However, since seizure evolution is …
localization and classification of epileptic seizures. However, since seizure evolution is …
A hybrid intelligent system for medical data classification
In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network,
the Classification and Regression Tree, and the Random Forest model is proposed, and its …
the Classification and Regression Tree, and the Random Forest model is proposed, and its …
[HTML][HTML] The efficacy of machine-learning-supported smart system for heart disease prediction
The disease may be an explicit status that negatively affects human health. Cardiopathy is
one of the common deadly diseases that is attributed to unhealthy human habits compared …
one of the common deadly diseases that is attributed to unhealthy human habits compared …
Seizure detection from EEG signals using multivariate empirical mode decomposition
We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG
signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a …
signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a …
A review of EEG and MEG epileptic spike detection algorithms
FE Abd El-Samie, TN Alotaiby, MI Khalid… - IEEE …, 2018 - ieeexplore.ieee.org
Epilepsy is one of the most serious disorders that affect patients' daily lives. When seizures
occur, patients cannot control their behaviors, which can lead to serious injuries. With the …
occur, patients cannot control their behaviors, which can lead to serious injuries. With the …
Computerized epileptiform transient detection in the scalp electroencephalogram: Obstacles to progress and the example of computerized ECG interpretation
Computerized detection of epileptiform transients (ETs), also called spikes and sharp waves,
in the electroencephalogram (EEG) has been a research goal for the last 40years. A reliable …
in the electroencephalogram (EEG) has been a research goal for the last 40years. A reliable …
Epileptic seizure focus detection from interictal electroencephalogram: a survey
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the
localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG …
localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG …
Model-based spike detection of epileptic EEG data
Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic
electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for …
electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for …