TY  - JOUR
A1  - Khalid, Madiha
A1  - Raza, Ali
A1  - Akhtar, Adnan
A1  - Rustam, Furqan
A1  - Brito Ballester, Julién
A1  - Rodríguez Velasco, Carmen Lilí
A1  - Díez, Isabel de la Torre
A1  - Ashraf, Imran
AV  - public
TI  - Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data
UR  - http://doi.org/10.1177/20552076241277185
JF  - DIGITAL HEALTH
VL  - 10
SN  - 2055-2076
ID  - uniromana15642
N2  - Objective
Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder.
Methods
This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features.
Results
The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR?+?SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection.
Conclusions
The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions.
Y1  - 2024/11//
KW  - Epileptic seizure detection
KW  -  machine learning
KW  -  deep learning
KW  -  feature engineering
KW  -  EEG data
ER  -