Application of machine learning to epileptic seizure onset detection and treatment

By Ali H. Shoeb. 2009

991 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/432e5d09f080c95286dc461f419b0685b41eb7e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. A. Shoeibi
2. Hisham G. Daoud
3. Marjane Khodatars
4. Magdy A. Bayoumi
5. Tharindu Fernando
6. Abbas Khosravi
7. Navid Ghassemi
8. M. Jafari

Citations in the corpus

• Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “This framework has been validated on both the Freiburg intracranial EEG (iEEG) [93] and CHB-MIT scalp EEG (sEEG) datasets [94], and achieves approximately 81 % sensitivity in a leave-one-subject-out cross validation setting.”
• The Temple University Hospital EEG Data Corpus (2016, methodology): “The CHB-MIT database contains data from 22 subjects, mostly pediatric (Shoeb, 2009).”
• A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, unknown): “org/physiobank/ database/chbmit/), which is described in [23].”
• Focal Onset Seizure Prediction Using Convolutional Networks (2018, methodology): “In this study, we trained our model and evaluated its performance on two independent datasets: (i) data collected from The Mount Sinai Epilepsy Center at the Mount Sinai Hospital (MSSM), and (ii) a subset of the public CHB-MIT EEG database (CHB-MIT) [34], [35].”
• An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, methodology): “Some of the proposed methods use spectral (Tzallas et al., 2012) and temporal aspects of information from EEG signals (Shoeb, 2009).”
• A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “All signals were recorded with a sampling frequency of 256 Hz digitized at 16-bit resolution and stored in an EDF file [Sho09].”
• Efficient Epileptic Seizure Prediction Based on Deep Learning (2019, background): “In this paper, we trained the proposed models and evaluated their performance on the CHB-MIT EEG dataset recorded at Children’s Hospital Boston [18], [19] which is publicly available [20].”
• Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, methodology): “Frequency (Hz) Flint-Hills [13] 10 59 Continues intracranial ling term ECoG 1419 249 Hauz Khas [13] 10 NA Scalp EEG (sEEG) NA 200 Freiburg [20] 21 87 Intracranial Electroencephalography (IEEG) 708 256 CHB-MIT [21] 22 163 sEEG 844 256 Kaggle [22] 5 dogs 48 IEEG 627 400 2 patients 5 KHz Bonn [23] 10 N”
• Learning Robust Features using Deep Learning for Automatic Seizure Detection (2016, methodology): “Extensive work has been done on this dataset facilitating the comparison of methods (Tzallas et al., 2012; Shoeb, 2009).”
• Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer (2025, unknown): “The CHB-MIT scalp EEG dataset includes data from 22 pediatric patients with intractable seizures [43].”