Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computing in Cardiology 2021
ISBN
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ISSN
2325-887X
e-ISSN
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Number of pages
4
Pages from-to
1-4
Publisher name
Computing in Cardiology 2021
Place of publication
neuveden
Event location
Brno
Event date
Sep 12, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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