Heart murmur detection from phonocardiogram recordings: The george b. moody physionet challenge 2022
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify
patients with heart murmurs, who may need follow-up diagnostic screening and treatment for …
patients with heart murmurs, who may need follow-up diagnostic screening and treatment for …
Clinical applications, methodology, and scientific reporting of electrocardiogram deep-learning models: A systematic review
Background The electrocardiogram (ECG) is one of the most common diagnostic tools
available to assess cardiovascular health. The advent of advanced computational …
available to assess cardiovascular health. The advent of advanced computational …
Disparate censorship & undertesting: A source of label bias in clinical machine learning
As machine learning (ML) models gain traction in clinical applications, understanding the
impact of clinician and societal biases on ML models is increasingly important. While biases …
impact of clinician and societal biases on ML models is increasingly important. While biases …
Abnormality classification from electrocardiograms with various lead combinations
Z Xu, Y Guo, T Zhao, Y Zhao, Z Liu… - Physiological …, 2022 - iopscience.iop.org
Objective. As cardiovascular diseases are a leading cause of death, early and accurate
diagnosis of cardiac abnormalities for a lower cost becomes particularly important. Given …
diagnosis of cardiac abnormalities for a lower cost becomes particularly important. Given …
Scaling representation learning from ubiquitous ecg with state-space models
Ubiquitous sensing from wearable devices in the wild holds promise for enhancing human
well-being, from diagnosing clinical conditions and measuring stress to building adaptive …
well-being, from diagnosing clinical conditions and measuring stress to building adaptive …
Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
Background. Twelve lead ECGs are a core diagnostic tool for cardiovascular diseases.
Here, we describe and analyse an ensemble deep neural network architecture to classify 24 …
Here, we describe and analyse an ensemble deep neural network architecture to classify 24 …
A Synthetic Electrocardiogram (ECG) Image Generation Toolbox to Facilitate Deep Learning-Based Scanned ECG Digitization
KK Shivashankara, R Sameni - arXiv preprint arXiv:2307.01946, 2023 - arxiv.org
Access to medical data is often limited as it contains protected health information (PHI).
There are privacy concerns regarding using records containing personally identifiable …
There are privacy concerns regarding using records containing personally identifiable …
PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines
A Dourson, R Santilli, F Marchesotti… - arXiv preprint arXiv …, 2023 - arxiv.org
Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but current
availability of veterinary cardiologists for ECG interpretation and diagnostic support is …
availability of veterinary cardiologists for ECG interpretation and diagnostic support is …
Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
Electrocardiograms (ECG) are widely employed as a diagnostic tool for monitoring electrical
signals originating from a heart. Recent machine learning research efforts have focused on …
signals originating from a heart. Recent machine learning research efforts have focused on …
LIFEDATA-A framework for traceable active learning projects
Active Learning has become a popular method for iteratively improving data-intensive
Artificial Intelligence models. However, it often presents a significant challenge when …
Artificial Intelligence models. However, it often presents a significant challenge when …