Juan C Mier1,2, Yejin Kim3, Xiaoqian Jiang3, Guo-Qiang Zhang3,4, Samden Lhatoo4. 1. Department of Chemical Engineering, University of Houston, Houston, TX, USA. JMier2@uh.edu. 2. Department of Computer Science, University of Houston, Houston, TX, USA. JMier2@uh.edu. 3. School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. 4. Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Abstract
BACKGROUND: Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021-1031, 2008; Nashef in Epilepsia 38:6-8, 1997). METHODS: This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687-5265, 2011). RESULTS: The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. CONCLUSION: The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient's SUDEP risk.
BACKGROUND: Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021-1031, 2008; Nashef in Epilepsia 38:6-8, 1997). METHODS: This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687-5265, 2011). RESULTS: The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. CONCLUSION: The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient's SUDEP risk.
Authors: Vernon J Lawhern; Amelia J Solon; Nicholas R Waytowich; Stephen M Gordon; Chou P Hung; Brent J Lance Journal: J Neural Eng Date: 2018-06-22 Impact factor: 5.379
Authors: Zhe Sage Chen; Aaron Hsieh; Guanghao Sun; Gregory K Bergey; Samuel F Berkovic; Piero Perucca; Wendyl D'Souza; Christopher J Elder; Pue Farooque; Emily L Johnson; Sarah Barnard; Russell Nightscales; Patrick Kwan; Brian Moseley; Terence J O'Brien; Shobi Sivathamboo; Juliana Laze; Daniel Friedman; Orrin Devinsky Journal: Front Neurol Date: 2022-03-18 Impact factor: 4.086