Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals

Automatic classification of heart rhythm disturbances using an electrocardiogram is a reliable way to timely detect diseases of the cardiovascular system. The need to automate this process is to increase the number of electrocardiogram signals. Classification methods based on the use of neural netwo...

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Автори:	Kiladze, M. R., Lyakhova, U. A., Ляхова, У. А., Nagornov, N. N., Нагорнов, Н. Н., Lyakhov, P. A., Ляхов, П. А.
Інші автори:	Киладзе, М. Р.
Формат:	Статья
Мова:	English
Опубліковано:	2023
Предмети:	linear perceptron PhysioNet/Computing in Cardiology Challenge 2021 LSTM network Metadata Neural network classification
Онлайн доступ:	https://dspace.ncfu.ru/handle/20.500.12258/26224
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id	ir-20.500.12258-26224
record_format	dspace
spelling	ir-20.500.12258-262242023-12-29T12:26:01Z Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals Kiladze, M. R. Lyakhova, U. A. Ляхова, У. А. Nagornov, N. N. Нагорнов, Н. Н. Lyakhov, P. A. Ляхов, П. А. Киладзе, М. Р. linear perceptron PhysioNet/Computing in Cardiology Challenge 2021 LSTM network Metadata Neural network classification Automatic classification of heart rhythm disturbances using an electrocardiogram is a reliable way to timely detect diseases of the cardiovascular system. The need to automate this process is to increase the number of electrocardiogram signals. Classification methods based on the use of neural networks provide a high percentage of arrhythmia recognition. However, known classification methods do not take into account patient characteristics. The work proposes a multimodal neural network that takes into account the age and gender characteristics of the patient. It includes a Long short-term memory (LSTM) network for feature extraction on twelve-channel electrocardiogram signals and a linear neural network for processing patient metadata such as age and gender. Extraction of electrocardiogram signal features occurs in parallel with metadata processing. The last unifying layer of the proposed multimodal neural network integrates heterogeneous data and features of electrocardiogram signals obtained using an LSTM network. The developed multimodal neural network was verified using the PhysioNet/Computing in Cardiology Challenge 2021 ECG database. The simulation results showed that the proposed multimodal neural network achieves a recognition accuracy of 63%, which is 2 percentage points higher compared to state-of-the-art methods. 2023-12-29T12:21:10Z 2023-12-29T12:21:10Z 2023 Статья Kiladze, M.R., Lyakhova, U.A., Lyakhov, P.A., Nagornov, N.N., Vahabi, M. Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals // IEEE Access. - 2023. - 11. - pp. 133744-133754. - DOI: 10.1109/ACCESS.2023.3335176 http://hdl.handle.net/20.500.12258/26224 en IEEE Access application/pdf application/pdf
institution	СКФУ
collection	Репозиторий
language	English
topic	linear perceptron PhysioNet/Computing in Cardiology Challenge 2021 LSTM network Metadata Neural network classification
spellingShingle	linear perceptron PhysioNet/Computing in Cardiology Challenge 2021 LSTM network Metadata Neural network classification Kiladze, M. R. Lyakhova, U. A. Ляхова, У. А. Nagornov, N. N. Нагорнов, Н. Н. Lyakhov, P. A. Ляхов, П. А. Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals
description	Automatic classification of heart rhythm disturbances using an electrocardiogram is a reliable way to timely detect diseases of the cardiovascular system. The need to automate this process is to increase the number of electrocardiogram signals. Classification methods based on the use of neural networks provide a high percentage of arrhythmia recognition. However, known classification methods do not take into account patient characteristics. The work proposes a multimodal neural network that takes into account the age and gender characteristics of the patient. It includes a Long short-term memory (LSTM) network for feature extraction on twelve-channel electrocardiogram signals and a linear neural network for processing patient metadata such as age and gender. Extraction of electrocardiogram signal features occurs in parallel with metadata processing. The last unifying layer of the proposed multimodal neural network integrates heterogeneous data and features of electrocardiogram signals obtained using an LSTM network. The developed multimodal neural network was verified using the PhysioNet/Computing in Cardiology Challenge 2021 ECG database. The simulation results showed that the proposed multimodal neural network achieves a recognition accuracy of 63%, which is 2 percentage points higher compared to state-of-the-art methods.
author2	Киладзе, М. Р.
author_facet	Киладзе, М. Р. Kiladze, M. R. Lyakhova, U. A. Ляхова, У. А. Nagornov, N. N. Нагорнов, Н. Н. Lyakhov, P. A. Ляхов, П. А.
format	Статья
author	Kiladze, M. R. Lyakhova, U. A. Ляхова, У. А. Nagornov, N. N. Нагорнов, Н. Н. Lyakhov, P. A. Ляхов, П. А.
author_sort	Kiladze, M. R.
title	Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals
title_short	Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals
title_full	Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals
title_fullStr	Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals
title_full_unstemmed	Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals
title_sort	multimodal neural network for recognition of cardiac arrhythmias based on 12-load electrocardiogram signals
publishDate	2023
url	https://dspace.ncfu.ru/handle/20.500.12258/26224
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Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals

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