Semi-supervised Learning for Epileptic Focus Localization Using Deep Convolutional Autoencoder

Daoud, Hisham; Bayoumi, Magdy

doi:10.1109/biocas.2019.8919222

Cited by 6 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only when a pathologically abnormal iEEG signal is detected can an abnormal lead be traced back and the localization of the epileptic zone be determined. Based on clinical studies, Liu et al [ 43 ] considered high-frequency oscillations a reliable biomarker for seizure onset zone identification, and Patrick Chauvel’s team [ 44 , 45 ] used signal patterns, such as fast activity, low-frequency suppression, and preictal spikes, as fingerprints for epileptogenic zone positioning [ 11 , 13 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

et al. 2021

View full text Add to dashboard Cite

Surgical intervention or the control of drug-refractory epilepsy requires accurate analysis of invasive inspection intracranial EEG (iEEG) data. A multi-branch deep learning fusion model is proposed to identify epileptogenic signals from the epileptogenic area of the brain. The classical approach extracts multi-domain signal wave features to construct a time-series feature sequence and then abstracts it through the bi-directional long short-term memory attention machine (Bi-LSTM-AM) classifier. The deep learning approach uses raw time-series signals to build a one-dimensional convolutional neural network (1D-CNN) to achieve end-to-end deep feature extraction and signal detection. These two branches are integrated to obtain deep fusion features and results. Resampling is employed to split the imbalanced epileptogenic and non-epileptogenic samples into balanced subsets for clinical validation. The model is validated over two publicly available benchmark iEEG databases to verify its effectiveness on a private, large-scale, clinical stereo EEG database. The model achieves high sensitivity (97.78%), accuracy (97.60%), and specificity (97.42%) on the Bern–Barcelona database, surpassing the performance of existing state-of-the-art techniques. It is then demonstrated on a clinical dataset with an average intra-subject accuracy of 92.53% and cross-subject accuracy of 88.03%. The results suggest that the proposed method is a valuable and extremely robust approach to help researchers and clinicians develop an automated method to identify the source of iEEG signals.

show abstract

Section: Discussionmentioning

confidence: 99%

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the past few decades, with the advancements in the field of medical science and technology now the treatments available for seizure control have improved a lot [12,23]. Some of treatments presently available includes use AEDs which are anti-epilepsy medicine [7]. But in 70% of cases these drugs are not one of successful techniques especially when the effected part of brain is severe.…”

Section: Introductionmentioning

confidence: 99%

Automated human mind reading using EEG signals for seizure detection

Ranga

Gupta

Meena

et al. 2020

Journal of Medical Engineering & Technology

View full text Add to dashboard Cite

Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments available for seizure control have improved a lot with the advancements in the field of medical science and technology. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is performed before surgery and also to predict seizure at the time operation which is useful in neuro stimulation device. But in most of cases visual examination is done by neurologist in order to detect and classify patterns of the disease but this requires a lot of pre-domain knowledge and experience. This all in turns put a pressure on neurosurgeons and leads to time wastage and also reduce their accuracy and efficiency. There is a need of some automated systems in arena of information technology like use of neural networks in deep learning which can assist neurologists. In the present paper, a model is proposed to give an accuracy of 98.33% which can be used for development of automated systems. The developed system will significantly help neurologists in their performance.

show abstract

Traffic emission estimation under incomplete information with spatiotemporal convolutional GAN

Zhao

Cao

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

Semi-supervised Learning for Epileptic Focus Localization Using Deep Convolutional Autoencoder

Cited by 6 publications

References 13 publications

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

Automated human mind reading using EEG signals for seizure detection

Traffic emission estimation under incomplete information with spatiotemporal convolutional GAN

Contact Info

Product

Resources

About