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

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