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

[11] Comparison of Convolutional Neural Network Architectures for Face Mask Detection
Yahya, Siti Nadia
Nordin, Muhammad Noor
Ramli, Aizat Faiz
Basarudin, Hafiz
Abu, Mohd Azlan
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (12) : 667 - 677
[12] Atrial fibrillation detection with signal decomposition and dilated residual neural network
Li, Yicheng
Xia, Yong
PHYSIOLOGICAL MEASUREMENT, 2023, 44 (10)
[13] Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network
Salinas-Martinez, Ricardo
de Bie, Johan
Marzocchi, Nicoletta
Sandberg, Frida
2020 COMPUTING IN CARDIOLOGY, 2020,
[14] Explainable detection of atrial fibrillation using deep convolutional neural network with UCMFB
B. Mohan Rao
Aman Kumar
Multimedia Tools and Applications, 2023, 82 : 40683 - 40700
[15] Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels
Chen, Brian
Javadi, Golara
Hamilton, Alexander
Sibley, Stephanie
Laird, Philip
Abolmaesumi, Purang
Maslove, David
Mousavi, Parvin
SCIENTIFIC REPORTS, 2022, 12 (01)
[16] Detection of atrial fibrillation from cardiac signal using convolutional neural network
Mohapatra S.K.
Mohanty M.N.
International Journal of Innovative Computing and Applications, 2022, 13 (03): : 172 - 179
[17] Detection of Atrial Fibrillation Using 1D Convolutional Neural Network
Hsieh, Chaur-Heh
Li, Yan-Shuo
Hwang, Bor-Jiunn
Hsiao, Ching-Hua
SENSORS, 2020, 20 (07)
[18] Atrial Fibrillation Detection and ECG Classification based on Convolutional Recurrent Neural Network
Limam, Mohamed
Precioso, Frederic
2017 COMPUTING IN CARDIOLOGY (CINC), 2017, 44
[19] A novel deep neural network for detection of Atrial Fibrillation using ECG signals
Subramanyan, Lokesh
Ganesan, Udhayakumar
KNOWLEDGE-BASED SYSTEMS, 2022, 258
[20] Detection of Atrial Fibrillation based on Synchronized Beats Image with Lightweight Neural Network
Thway, Khaing Su
Somboon, Pakpum
Teeramongkonrasmee, Arporn
2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022), 2022, : 341 - 344

← 1 2 3 4 5 →