loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Egorov Konstantin 1 ; Sokolova Elena 1 ; Avetisian Manvel 1 and Tuzhilin Alexander 2

Affiliations: 1 Sberbank AI Lab, Moscow, Russia ; 2 New York University, New York City, U.S.A.

Keyword(s): ECG, Holter, Neural Networks, Segmentation, Classification.

Abstract: Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient. Its interpretation becomes a difficult and time-consuming task for the doctor who analyzes them because every heartbeat needs to be classified, thus requiring highly accurate methods for automatic interpretation. In this paper, we present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with encoder-decoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage in gradient boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features and further increases performance of our approach. As a part of this work we acquired 5095 Holter recordings of patients annotated by an experienced cardiologist. A committee of three cardiologists serv ed as a ground truth annotators for the 291 examples in the test set. We show that the proposed method outperforms the selected baselines, including two commercial-grade software packages and some methods previously published in the literature. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.119.248.62

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Konstantin, E.; Elena, S.; Manvel, A. and Alexander, T. (2021). Noise-resilient Automatic Interpretation of Holter ECG Recordings. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 208-214. DOI: 10.5220/0010258300002865

@conference{biosignals21,
author={Egorov Konstantin. and Sokolova Elena. and Avetisian Manvel. and Tuzhilin Alexander.},
title={Noise-resilient Automatic Interpretation of Holter ECG Recordings},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS},
year={2021},
pages={208-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010258300002865},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS
TI - Noise-resilient Automatic Interpretation of Holter ECG Recordings
SN - 978-989-758-490-9
IS - 2184-4305
AU - Konstantin, E.
AU - Elena, S.
AU - Manvel, A.
AU - Alexander, T.
PY - 2021
SP - 208
EP - 214
DO - 10.5220/0010258300002865
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic