Utilizing Deep Learning Methods for Heart Defect Identification via Electrocardiogram (ECG): A Literature Review
In recent years the application of Deep Learning is widely used in various fields of science,
such as in the military, agriculture, health and even in other fields. In the field of health many …
such as in the military, agriculture, health and even in other fields. In the field of health many …
Realtime Atrial Fibrillation Detection System For IoMT Using Hybrid Machine Learning Classification
Atrial fibrillation (AF) is a type of cardiac arrhythmia in which the atria contract rapidly and
irregularly, resulting in an irregular heartbeat. AF can lead to decreased blood flow to the …
irregularly, resulting in an irregular heartbeat. AF can lead to decreased blood flow to the …
ECG classification exercise health analysis algorithm based on GRU and convolutional neural network
W Ji, D Zhu - IEEE Access, 2024 - ieeexplore.ieee.org
Sudden cardiac death (SCD) is one of the main causes of death in athletes during exercise
and physical activity. By analyzing the results of the ECG classification, doctors can promptly …
and physical activity. By analyzing the results of the ECG classification, doctors can promptly …
ECG classification using Artificial Intelligence: Model Optimization and Robustness Assessment
The Electrocardiogram is one of the more complete exams for diagnosing pathologies
regarding the cardiovascular system. Therefore, and based on the rudimentary methods of …
regarding the cardiovascular system. Therefore, and based on the rudimentary methods of …
Classification of Continuous ECG Segments-Performance Analysis of a Deep Learning Model
LCN Barbosa, D Lopes, I Escrivães… - 2023 45th Annual …, 2023 - ieeexplore.ieee.org
Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of
heart diseases. It is a complex and non-linear signal, which is the first option to preliminary …
heart diseases. It is a complex and non-linear signal, which is the first option to preliminary …
AI-based detection for Remote Electrocardiogram Monitoring System
M Garcia, S Kumar - 2024 IEEE World AI IoT Congress (AIIoT), 2024 - ieeexplore.ieee.org
With the rapid advancement of artificial intelligence (AI) and remote patient monitoring
technologies, there is a growing interest in develo** robust and efficient AI-based …
technologies, there is a growing interest in develo** robust and efficient AI-based …
[CITAS][C] Dropout-based CNN Ensemble Autoencoder Model for Anomaly Detection in Time Series Data
KT Oh, H Park, GH Lee, R Santiago, SC Kim… - 대한전자공학회 학술 …, 2023 - dbpia.co.kr
The role of deep learning in the digital healthcare industry is becoming increasingly
important. Anomaly detection techniques using human biological data are especially …
important. Anomaly detection techniques using human biological data are especially …