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

[21] Explainable detection of atrial fibrillation using deep convolutional neural network with UCMFB
Rao, B. Mohan
Kumar, Aman
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (26) : 40683 - 40700
[22] MGNN: A multiscale grouped convolutional neural network for efficient atrial fibrillation detection
Liu, Sen
Wang, Aiguo
Deng, Xintao
Yang, Cuiwei
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 148
[23] Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels
Brian Chen
Golara Javadi
Alexander Hamilton
Stephanie Sibley
Philip Laird
Purang Abolmaesumi
David Maslove
Parvin Mousavi
Scientific Reports, 12
[24] A NEURAL-NETWORK SYSTEM FOR DETECTION OF ATRIAL-FIBRILLATION IN AMBULATORY ELECTROCARDIOGRAMS
CUBANSKI, D
CYGANSKI, D
ANTMAN, EM
FELDMAN, CL
JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY, 1994, 5 (07) : 602 - 608
[25] Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network
Salinas-Martinez, Ricardo
de Bie, Johannes
Marzocchi, Nicoletta
Sandberg, Frida
FRONTIERS IN PHYSIOLOGY, 2021, 12
[26] DAAT: A New Method to Train Convolutional Neural Network on Atrial Fibrillation Detection
Zhang, Jian
Liu, Juan
Li, Pei-Fang
Feng, Jing
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12465 LNAI : 280 - 290
[27] Comparison of Deep Neural Network Architectures for Fault Detection in Tennessee Eastman Process
Chadha, Gavneet Singh
Schwung, Andreas
2017 22ND IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2017,
[28] Neural Network Approach for T-wave End Detection: A Comparison of Architectures
Suarez Leon, Alexander A.
Matos Molina, Danelia
Vazquez Seisdedos, Carlos R.
Goovaerts, Griet
Vandeput, Steven
Van Huffel, Sabine
2015 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), 2015, 42 : 589 - 592
[29] Robust ECG Signal Classification for Detection of Atrial Fibrillation Using a Novel Neural Network
Xiong, Zhaohan
Stiles, Martin K.
Zhao, Jichao
2017 COMPUTING IN CARDIOLOGY (CINC), 2017, 44
[30] Detection of atrial fibrillation using variable length genetic algorithm and convolutional neural network
Al Qaraghuli, Hawraa
Sheibani, Reza
Tabatabaee, Hamid
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2022, 34 (10):

← 1 2 3 4 5 →