Noise-resilient Automatic Interpretation of Holter ECG Recordings

Egorov Konstantin, Sokolova Elena, Avetisian Manvel, Tuzhilin Alexander

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 served 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.

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Paper Citation


in Harvard Style

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 - Volume 2: BIOSIGNALS, ISBN 978-989-758-490-9, pages 208-214. DOI: 10.5220/0010258302080214


in Bibtex Style

@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 - Volume 2: BIOSIGNALS,},
year={2021},
pages={208-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010258302080214},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - Noise-resilient Automatic Interpretation of Holter ECG Recordings
SN - 978-989-758-490-9
AU - Konstantin E.
AU - Elena S.
AU - Manvel A.
AU - Alexander T.
PY - 2021
SP - 208
EP - 214
DO - 10.5220/0010258302080214