A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection
We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-
inspired machine learning approach known as Echo State Networks. Our classifier has a low …
inspired machine learning approach known as Echo State Networks. Our classifier has a low …
Hyperbox-based machine learning algorithms: a comprehensive survey
With the rapid development of digital information, the data volume generated by humans
and machines is growing exponentially. Along with this trend, machine learning algorithms …
and machines is growing exponentially. Along with this trend, machine learning algorithms …
FM-ECG: A fine-grained multi-label framework for ECG image classification
Recently, increasingly more methods are proposed to automatically detect the abnormalities
in Electrocardiography (ECG). Despite their success on public golden standard datasets …
in Electrocardiography (ECG). Despite their success on public golden standard datasets …
Arrhythmic heartbeat classification using ensemble of random forest and support vector machine algorithm
S Bhattacharyya, S Majumder… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
In this article, an automated heartbeat classification has been proposed to prevent the
growing threats of cardiovascular diseases around the world. The MIT-BIH arrhythmia …
growing threats of cardiovascular diseases around the world. The MIT-BIH arrhythmia …
Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization
H Li, Z Lin, Z An, S Zuo, W Zhu, Z Zhang, Y Mu… - … Signal Processing and …, 2022 - Elsevier
Electrocardiogram (ECG) signals contain a significant amount of subtle information that can
be used to detect some types of heart dysfunction. The widespread availability of digital ECG …
be used to detect some types of heart dysfunction. The widespread availability of digital ECG …
Precision phenotyping in heart failure and pattern clustering of ultrasound data for the assessment of diastolic dysfunction
AMS Omar, S Narula, MA Abdel Rahman… - JACC: Cardiovascular …, 2017 - jacc.org
Objectives: The aim of this study was to investigate whether cluster analysis of left atrial and
left ventricular (LV) mechanical deformation parameters provide sufficient information for …
left ventricular (LV) mechanical deformation parameters provide sufficient information for …
ECG heartbeat classification using ensemble of efficient machine learning approaches on imbalanced datasets
Being electrocardiogram already an established method for analyzing cardiac health, it
gained many researchers' interests to classify heartbeats accurately. In spite of having …
gained many researchers' interests to classify heartbeats accurately. In spite of having …
Weighted random forests to improve arrhythmia classification
K Gajowniczek, I Grzegorczyk, T Ząbkowski, C Bajaj - Electronics, 2020 - mdpi.com
Construction of an ensemble model is a process of combining many diverse base predictive
learners. It arises questions of how to weight each model and how to tune the parameters of …
learners. It arises questions of how to weight each model and how to tune the parameters of …
Delineation of 12-lead ECG representative beats using convolutional encoder–decoders with residual and recurrent connections
V Krasteva, T Stoyanov, R Schmid… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
The aim of this study is to address the challenge of 12-lead ECG delineation by different
encoder–decoder architectures of deep neural networks (DNNs). This study compares four …
encoder–decoder architectures of deep neural networks (DNNs). This study compares four …
False alarm reduction in critical care
High false alarm rates in the ICU decrease quality of care by slowing staff response times
while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in …
while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in …