Golden standard or obsolete method? Review of ECG applications in clinical and experimental context
T Stracina, M Ronzhina, R Redina… - Frontiers in …, 2022 - frontiersin.org
Cardiovascular system and its functions under both physiological and pathophysiological
conditions have been studied for centuries. One of the most important steps in the …
conditions have been studied for centuries. One of the most important steps in the …
Classification of ECG using ensemble of residual CNNs with attention mechanism
This paper introduces a winning solution (team ISIBrno-AIMT) to the PhysioNet Challenge
2021. The method is based on the ResNet deep neural network architecture with a multi …
2021. The method is based on the ResNet deep neural network architecture with a multi …
Exploring convolutional neural network architectures for EEG feature extraction
The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
Opening the black box: interpretability of machine learning algorithms in electrocardiography
Recent studies have suggested that cardiac abnormalities can be detected from the
electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL …
electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL …
Multilabel 12-lead ECG classification based on leadwise grouping multibranch network
X Xie, H Liu, D Chen, M Shu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The 12-lead electrocardiogram (ECG) is widely used in the clinical diagnosis of
cardiovascular disease, and deep learning has become an effective approach to automatic …
cardiovascular disease, and deep learning has become an effective approach to automatic …
HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons
of death in the world. The timely, accurate, and effective prediction of heart diseases is …
of death in the world. The timely, accurate, and effective prediction of heart diseases is …
Classification of ECG using ensemble of Residual CNNs with or without attention mechanism
Objective. This paper introduces a winning solution (team ISIBrno-AIMT) to the official round
of PhysioNet Challenge 2021. The main goal of the challenge was a classification of ECG …
of PhysioNet Challenge 2021. The main goal of the challenge was a classification of ECG …
Predict alone, decide together: cardiac abnormality detection based on single lead classifier voting
Objective. A classifier based on weighted voting of multiple single-lead based models
combining deep learning (DL) representation and hand-crafted features was developed to …
combining deep learning (DL) representation and hand-crafted features was developed to …
[HTML][HTML] Multi-channel delineation of intracardiac electrograms for arrhythmia substrate analysis using implicitly regularized convolutional neural network with wide …
J Hejc, R Redina, J Kolarova, Z Starek - Biomedical Signal Processing and …, 2024 - Elsevier
Objective Automated segmentation of intracardiac electrograms and extraction of
fundamental cycle length intervals is crucial for reproducible arrhythmia substrate analysis …
fundamental cycle length intervals is crucial for reproducible arrhythmia substrate analysis …
Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation
PA Warrick, V Lostanlen, M Eickenberg… - Physiological …, 2022 - iopscience.iop.org
We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead
electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase …
electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase …