Semi-supervised training data selection improves seizure forecasting in canines with epilepsy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00346819" target="_blank" >RIV/68407700:21730/20:00346819 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.bspc.2019.101743" target="_blank" >https://doi.org/10.1016/j.bspc.2019.101743</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2019.101743" target="_blank" >10.1016/j.bspc.2019.101743</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Semi-supervised training data selection improves seizure forecasting in canines with epilepsy

Popis výsledku v původním jazyce

Objective: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training datatypically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defin-ing a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs areuniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data char-acteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, andselection of pre-ictal training data segments to reflect this could improve algorithm performance.Methods: We propose a semi-supervised technique to select pre-ictal training data most distinguishablefrom interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchicalclustering to identify optimal pre-ictal data epochs.Results: In this paper we compare the performance of a seizure forecasting algorithm with and withouthierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturallyoccurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW)(0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p < 0.001,n = 6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs.Conclusion: Hierarchical clustering is a helpful method for training data selection overall, but should beevaluated on a subject-wise basis.Significance: The clustering method can be used to optimize results of forecasting towards sensitivity orTIW or FPR, and therefore can be useful for epilepsy management.

Název v anglickém jazyce

Semi-supervised training data selection improves seizure forecasting in canines with epilepsy

Popis výsledku anglicky

Objective: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training datatypically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defin-ing a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs areuniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data char-acteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, andselection of pre-ictal training data segments to reflect this could improve algorithm performance.Methods: We propose a semi-supervised technique to select pre-ictal training data most distinguishablefrom interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchicalclustering to identify optimal pre-ictal data epochs.Results: In this paper we compare the performance of a seizure forecasting algorithm with and withouthierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturallyoccurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW)(0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p < 0.001,n = 6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs.Conclusion: Hierarchical clustering is a helpful method for training data selection overall, but should beevaluated on a subject-wise basis.Significance: The clustering method can be used to optimize results of forecasting towards sensitivity orTIW or FPR, and therefore can be useful for epilepsy management.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

57

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

—

Kód UT WoS článku

000512481800017

EID výsledku v databázi Scopus

2-s2.0-85074854563