ArticleBrain sciences2019
Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals.
Article in Brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.Computational intelligence and neuroscience · 2022Pooled it
- Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.Brain informatics · 2026Article
- Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals.Scientific reports · 2025Article
- Decoding covert visual attention of electroencephalography signals using continuous wavelet transform and deep learning approach.Scientific reports · 2025Article
- DyslexiaNet: Examining the Viability and Efficacy of Eye Movement-Based Deep Learning for Dyslexia Detection.Journal of eye movement research · 2025Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- Temporal Lobe Epilepsy Focus Detection Based on the Correlation Between Brain MR Images and EEG Recordings with a Decision Tree.Diagnostics (Basel, Switzerland) · 2024Article
- Real-Time Home Automation System Using BCI Technology.Biomimetics (Basel, Switzerland) · 2024Article
- A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.Sensors (Basel, Switzerland) · 2024Article
- Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection.Sensors (Basel, Switzerland) · 2024Article
- Positional multi-length and mutual-attention network for epileptic seizure classification.Frontiers in computational neuroscience · 2024Article
- A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals.Frontiers in computational neuroscience · 2024Article
- Residual and bidirectional LSTM for epileptic seizure detection.Frontiers in computational neuroscience · 2024Article
- Novel deep learning framework for detection of epileptic seizures using EEG signals.Frontiers in computational neuroscience · 2024Article
- A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection.Frontiers in computational neuroscience · 2024Article
- Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.Sensors (Basel, Switzerland) · 2023Article
- Identification of TLE Focus from EEG Signals by Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Multidomain Convolution Neural Network Models for Improved Event-Related Potential Classification.Sensors (Basel, Switzerland) · 2023Article
- Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.Journal of medical Internet research · 2023Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.
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