Neural Network Architectures Comparison for Atrial Fibrillation Detection

被引：0

作者：

Aguilar, Jaylenne ^{[1
]}

Tacuri-Pizha, Nelly ^{[1
]}

Cevallos-Bermeo, Gabriela ^{[1
]}

Villalba-Meneses, Fernando ^{[1
]}

Cruz-Varela, Jonathan ^{[1
]}

Teran-Grijalva, Cristhian ^{[2
]}

Cadena-Morejon, Carolina ^{[3
]}

Tirado-Espin, Andres ^{[3
,4
]}

Almeida-Galarraga, Diego ^{[1
,4
]}

机构：

[1] Univ Yachay Tech, Sch Biol Sci & Engn, Urcuqui, Ecuador

[2] Ejercito Ecuatoriano, Grp Fuerzas Especiales Grad Miguel Iturralde 27, Latacunga, Ecuador

[3] Univ Yachay Tech, Sch Math & Computat Sci, Urcuqui, Ecuador

[4] Univ Otavalo, Otavalo, Ecuador

来源：

2023 FOURTH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND SOFTWARE TECHNOLOGIES, ICI2ST 2023 | 2023年

关键词：

Atrial fibrillation detection; AT' diagnosis; AF detection with AIL; CLASSIFICATION;

D O I：

10.1109/ICI2ST62251.2023.00009

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting about 50,000 new people each year in Latin America. At' is characterized by irregular and rapid heartbeats that can lead to serious complications, such as stroke, heart failure, and all-cause mortality. Traditional methods for AF detection are time consuming and can be prone to human error. Therefore, this work reports the results from two methods using machine learning techniques to assist the diagnosis of Al' through 2 hybrid models of neural networks: The ID- CNN with BILSTN1 model and the NlobileNetV2 with BILSTM model which reached 81 and 75% accuracy respectively.

引用

页码：9 / 15

页数：7

共 50 条

[1] Atrial Fibrillation Detection with Convolutional Neural Network
Luo, Jingting
Fu, Canmiao
Bai, Mengjie
Zhao, Yong
[J]. PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018), 2018, : 94 - 98
[2] Atrial Fibrillation Detection Using a Feedforward Neural Network
Yunfan Chen
Chong Zhang
Chengyu Liu
Yiming Wang
Xiangkui Wan
[J]. Journal of Medical and Biological Engineering, 2022, 42 : 63 - 73
[3] Atrial Fibrillation Detection Using a Feedforward Neural Network
Chen, Yunfan
Zhang, Chong
Liu, Chengyu
Wang, Yiming
Wan, Xiangkui
[J]. JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, 2022, 42 (01) : 63 - 73
[4] Multiscale dilated convolutional neural network for Atrial Fibrillation detection
Xia, Lingnan
He, Sirui
Huang, Y-F
Ma, Hua
[J]. PLOS ONE, 2024, 19 (06):
[5] AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
Bambang Tutuko
Siti Nurmaini
Alexander Edo Tondas
Muhammad Naufal Rachmatullah
Annisa Darmawahyuni
Ria Esafri
Firdaus Firdaus
Ade Iriani Sapitri
[J]. BMC Medical Informatics and Decision Making, 21
[6] AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
Tutuko, Bambang
Nurmaini, Siti
Tondas, Alexander Edo
Rachmatullah, Muhammad Naufal
Darmawahyuni, Annisa
Esafri, Ria
Firdaus, Firdaus
Sapitri, Ade Iriani
[J]. BMC MEDICAL INFORMATICS AND DECISION MAKING, 2021, 21 (01)
[7] The atrial neural network as a substrate for atrial fibrillation
Mao, Jun
Scherlag, Benjamin J.
Liu, Yu
Fan, Youqi
Varma, Vandana
Stavrakis, Stavros
Po, Sunny S.
[J]. JOURNAL OF INTERVENTIONAL CARDIAC ELECTROPHYSIOLOGY, 2012, 35 (01) : 3 - 9
[8] Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network
Wei, Xiaoling
Li, Jimin
Zhang, Chenghao
Liu, Ming
Xiong, Peng
Yuan, Xin
Li, Yifei
Lin, Feng
Liu, Xiuling
[J]. JOURNAL OF PROBABILITY AND STATISTICS, 2019, 2019
[9] The atrial neural network as a substrate for atrial fibrillation
Jun Mao
Benjamin J. Scherlag
Yu Liu
Youqi Fan
Vandana Varma
Stavros Stavrakis
Sunny S. Po
[J]. Journal of Interventional Cardiac Electrophysiology, 2012, 35 : 3 - 9
[10] Industrial Anomaly Detection: A Comparison of Unsupervised Neural Network Architectures
Siegel, Barry
[J]. IEEE SENSORS LETTERS, 2020, 4 (08)

← 1 2 3 4 5 →