Application of Machine Learning To Epileptic Seizure Detection

By Ali H. Shoeb, John V. Guttag. International Conference on Machine Learning, 2010

648 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/57e4afe9ca74414fa02f2e0a929b64dc9a03334d

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Citations in the corpus

EEGtoText: Learning to Write Medical Reports from EEG Recordings (2019, background): “Deep Learning in EEG Analysis Recent years, the analysis of EEG signals has gone beyond traditional two step EEG feature engineering and classification (Lin et al., 2008; Alomari et al., 2014; Shoeb and Guttag, 2010; Al-Fahoum and Al-Fraihat, 2014) to end-toend learning.”
Focal Onset Seizure Prediction Using Convolutional Networks (2018, methodology): “2) CHB-MIT EEG Dataset: The CHB-MIT database contains scalp EEG data collected from 22 patients with 9–42 recordings for each patient, originally collected for the seizure detection problem [38].”
Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology (2018, methodology): “applied ML to clinical notes [20], physiological waveforms [10, 43], structured EHR data [44], radiologic images [45], and even”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “Two common metrics, good detection rate (GDR) and FPR per hour (FPR/h), are calculated based on the intersection of detection results and annotations [VI17, SG10a, SLUC15].”
Applications of Artificial Intelligence in Machine Learning: Review and Prospect (2015, unknown): “[25]”
Epileptic Seizure Detection Based on EEG Signals and CNN (2018, background): “…detection is challenging because the electrical activity of the brain is mediated by numerous classes of neurons with overlapping characteristics (Shoeb and Guttag, 2010), and improvements in the detection performance by extracting more eﬀective features and excluding irrelevant features or…”
A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “Shoeb and Guttag [41] performed seizure detection on their arranged dataset of Child Hospital Bostan, MIT (CHB-MIT) [60] using SVM with the vector feature and achieved the estimated accuracy of 96%.”
A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, unknown): “Shoeb and Guttag [7] designed an EEG seizure detector and studied on Children’s Hospital Boston-Massachusetts Institute”