Standard

A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm. / Markov, Nikita; Ushenin, Konstantin; Bozhko, Yakov.
Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 072-075.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Markov, N, Ushenin, K & Bozhko, Y 2023, A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm. in Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 072-075, 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Екатеринбург, Russian Federation, 15/05/2023. https://doi.org/10.1109/USBEREIT58508.2023.10158853

APA

Markov, N., Ushenin, K., & Bozhko, Y. (2023). A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book (pp. 072-075). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT58508.2023.10158853

Vancouver

Markov N, Ushenin K, Bozhko Y. A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 072-075 doi: 10.1109/USBEREIT58508.2023.10158853

Author

Markov, Nikita ; Ushenin, Konstantin ; Bozhko, Yakov. / A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm. Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 072-075

BibTeX

@inproceedings{f0c72457ee5c4617aead90fb235821f8,
title = "A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm",
abstract = "Atrial fibrillation (AF) is one of the most common heart diseases in population. Timely diagnosis of AF is challenging due to the asymptomatic and episodic nature of the disease. It is therefore necessary to develop methods that can identify patients with AF using electrocardiographic data of normal sinus rhythm when there's no abnormal rhythm present in recordings. Models based on convolutional neural networks have been successful using 12-lead ECGs with high sampling rates. We believe it is possible to solve the problem with more generalised biomarker data of heart rate variability. We consider recurrent neural networks for this task. In this paper, we consider a convolutional recurrent neural network (CRNN) model with LSTM layers and compare its classification performance to a convolutional neural network (CNN) with a global average pooling operator. In addition, we generate attention maps of the CRNN model and demonstrate the peculiarities of its decision mechanism. Open PhysioNet data is used for model training; data from a local clinical hospital is used for model validation. In general, the CRNN model shows better patient classification results and provides interpretable attention maps with GradCAM++ method.",
author = "Nikita Markov and Konstantin Ushenin and Yakov Bozhko",
year = "2023",
month = may,
day = "15",
doi = "10.1109/USBEREIT58508.2023.10158853",
language = "English",
pages = "072--075",
booktitle = "Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) ; Conference date: 15-05-2023 Through 17-05-2023",

}

RIS

TY - GEN

T1 - A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm

AU - Markov, Nikita

AU - Ushenin, Konstantin

AU - Bozhko, Yakov

PY - 2023/5/15

Y1 - 2023/5/15

N2 - Atrial fibrillation (AF) is one of the most common heart diseases in population. Timely diagnosis of AF is challenging due to the asymptomatic and episodic nature of the disease. It is therefore necessary to develop methods that can identify patients with AF using electrocardiographic data of normal sinus rhythm when there's no abnormal rhythm present in recordings. Models based on convolutional neural networks have been successful using 12-lead ECGs with high sampling rates. We believe it is possible to solve the problem with more generalised biomarker data of heart rate variability. We consider recurrent neural networks for this task. In this paper, we consider a convolutional recurrent neural network (CRNN) model with LSTM layers and compare its classification performance to a convolutional neural network (CNN) with a global average pooling operator. In addition, we generate attention maps of the CRNN model and demonstrate the peculiarities of its decision mechanism. Open PhysioNet data is used for model training; data from a local clinical hospital is used for model validation. In general, the CRNN model shows better patient classification results and provides interpretable attention maps with GradCAM++ method.

AB - Atrial fibrillation (AF) is one of the most common heart diseases in population. Timely diagnosis of AF is challenging due to the asymptomatic and episodic nature of the disease. It is therefore necessary to develop methods that can identify patients with AF using electrocardiographic data of normal sinus rhythm when there's no abnormal rhythm present in recordings. Models based on convolutional neural networks have been successful using 12-lead ECGs with high sampling rates. We believe it is possible to solve the problem with more generalised biomarker data of heart rate variability. We consider recurrent neural networks for this task. In this paper, we consider a convolutional recurrent neural network (CRNN) model with LSTM layers and compare its classification performance to a convolutional neural network (CNN) with a global average pooling operator. In addition, we generate attention maps of the CRNN model and demonstrate the peculiarities of its decision mechanism. Open PhysioNet data is used for model training; data from a local clinical hospital is used for model validation. In general, the CRNN model shows better patient classification results and provides interpretable attention maps with GradCAM++ method.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85164974864

U2 - 10.1109/USBEREIT58508.2023.10158853

DO - 10.1109/USBEREIT58508.2023.10158853

M3 - Conference contribution

SP - 72

EP - 75

BT - Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

Y2 - 15 May 2023 through 17 May 2023

ER -

ID: 41992997