Comput. Biol. Med. - Evaluation of automatic feature detection algorithms in EEG: application to interburst intervals.

Tópicos

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Resumo

In this paper, we present a new method to compare and improve algorithms for feature detection in neonatal EEG. The method is based on the algorithms ability to compute accurate statistics to predict the results of EEG visual analysis. This method is implemented inside a Java software called EEGDiag, as part of an e-health Web portal dedicated to neonatal EEG. EEGDiag encapsulates a component-based implementation of the detection algorithms called analyzers. Each analyzer is defined by a list of modules executed sequentially. As the libraries of modules are intended to be enriched by its users, we developed a process to evaluate the performance of new modules and analyzers using a database of expertized and categorized EEGs. The evaluation is based on the Davies-Bouldin index (DBI) which measures the quality of cluster separation, so that it will ease the building of classifiers on risk categories. For the first application we tested this method on the detection of interburst intervals (IBI) using a database of 394 EEG acquired on premature newborns. We have defined a class of IBI detectors based on a threshold of the standard deviation on contiguous short time windows, inspired by previous work. Then we determine which detector and what threshold values are the best regarding DBI, as well as the robustness of this choice. This method allows us to make counter-intuitive choices, such as removing the 50 Hz filter (power supply) to save time.

Resumo Limpo

paper present new method compar improv algorithm featur detect neonat eeg method base algorithm abil comput accur statist predict result eeg visual analysi method implement insid java softwar call eegdiag part ehealth web portal dedic neonat eeg eegdiag encapsul componentbas implement detect algorithm call analyz analyz defin list modul execut sequenti librari modul intend enrich user develop process evalu perform new modul analyz use databas expert categor eeg evalu base daviesbouldin index dbi measur qualiti cluster separ will eas build classifi risk categori first applic test method detect interburst interv ibi use databas eeg acquir prematur newborn defin class ibi detector base threshold standard deviat contigu short time window inspir previous work determin detector threshold valu best regard dbi well robust choic method allow us make counterintuit choic remov hz filter power suppli save time

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