Article detail · 2015
A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers
Journal
IEEE Journal of Biomedical and Health Informatics- Year
- 2015
- Type
- article
Data source split
- YÖKSİS venue IEEE Journal of Biomedical and Health Informatics
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out this study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks. The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity, and specificity. The proposed method is tested using a benchmark EEG dataset, and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epilepsy diagnosis.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
215 citations
OpenAlex cited_by_count (cache / database)
15 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection 2020
- A hybrid CNN-LSTM model for pre-miRNA classification 2021
- A hybrid CNN-LSTM model for pre-miRNA classification 2021
- Computer‐Aided Diagnosis of Parkinson’s Disease Using Complex‐Valued Neural Networks and mRMR Feature Selection Algorithm 2015
- A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing 2019
- A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method 2019
- A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing 2018
- A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification 2020
- Artificial Intelligence (AI) and Ethics in Medicine at a Global Level: Benefits and Risks 2023
- Classification of Epileptic Seizures using Artificial Neural Network with Adaptive Momentum 2020