Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU142182" target="_blank" >RIV/00216305:26220/21:PU142182 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cinc.org/archives/2021/pdf/CinC2021-179.pdf" target="_blank" >https://www.cinc.org/archives/2021/pdf/CinC2021-179.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2021.179" target="_blank" >10.22489/CinC.2021.179</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data

Popis výsledku v původním jazyce

Detection of cardiac arrhythmias is still an ongoing challenge. Here we focus on premature ventricular contraction (PVC) and premature atrial contraction (PAC) and introduce a deep-learning-based method for PVC/PAC localization in ECG. Our method is based on involving the time series with non-zero values corresponding to the ground truth PVC/PAC positions into the training process. To improve the efficiency of deep model training, the transition between the non-zero and zero areas in the train output time series was smoothed by introducing a Gaussian function. When applied to the new ECGs, the output signal (time series including Gaussians) is processed by a robust peak detector with Bayesian optimization of threshold, minimal distance and peak prominence. Positions of the detected peaks correspond to the desired PVC/PAC positions. The proposed method was evaluated on China Physiological Signal Challenge 2018 (CPSC2018) using own-created ground truth positions of PVC/PAC. The proposed method reached F1 score 0.923 and 0.688 for PAC and PVC, respectively, which is better than our previous results obtained via multiple instance learning-based method.

Název v anglickém jazyce

Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data

Popis výsledku anglicky

Detection of cardiac arrhythmias is still an ongoing challenge. Here we focus on premature ventricular contraction (PVC) and premature atrial contraction (PAC) and introduce a deep-learning-based method for PVC/PAC localization in ECG. Our method is based on involving the time series with non-zero values corresponding to the ground truth PVC/PAC positions into the training process. To improve the efficiency of deep model training, the transition between the non-zero and zero areas in the train output time series was smoothed by introducing a Gaussian function. When applied to the new ECGs, the output signal (time series including Gaussians) is processed by a robust peak detector with Bayesian optimization of threshold, minimal distance and peak prominence. Positions of the detected peaks correspond to the desired PVC/PAC positions. The proposed method was evaluated on China Physiological Signal Challenge 2018 (CPSC2018) using own-created ground truth positions of PVC/PAC. The proposed method reached F1 score 0.923 and 0.688 for PAC and PVC, respectively, which is better than our previous results obtained via multiple instance learning-based method.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computing in Cardiology 2021

ISBN

—

ISSN

2325-887X

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

Computing in Cardiology 2021

Místo vydání

neuveden

Místo konání akce

Brno

Datum konání akce

12. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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