Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.

Sensors (Basel, Switzerland) - 27 May 2026

Gader Mohamed Amin, Karmani Sourour, Djemal Ridha, Sakuyama Carlos Valderrama

Abstract excerpt

Heart failure (HF) remains a major global health challenge, necessitating accurate yet accessible diagnostic tools. While the left ventricular ejection fraction (LVEF) is the primary metric for classifying HF into preserved (HFpEF), mid-range (HFmrEF), and reduced (HFrEF) phenotypes, conventional imaging modalities such as echocardiography are resource intensive. In contrast, the electrocardiogram (ECG) offers a...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Artificial Intelligence

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electrocardiography

Share to Heart Failure

Share to Phenotype

Share to Signal Processing, Computer-Assisted

Source

Open in PubMed Open full text

Identifiers

PMID: 42280915
DOI: 10.3390/s26113397
PMCID: PMC13258966

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.PMID 42280915Read ↗

Artificial Intelligence-Enabled Electrocardiogram for Elevated Left Ventricular Filling PressureSemantic proximity Read ↗

Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational StudySemantic proximity Read ↗

Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches.Semantic proximity Read ↗

An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare StudySemantic proximity Read ↗

Rationale and design of the PACIFIC-PRESERVED (PhenomApping, ClassIFication and Innovation for Cardiac dysfunction in patients with heart failure and PRESERVED left ventricular ejection fraction) study.Semantic proximity Read ↗

Multiethnic Validation of Artificial Intelligence-Enhanced Electrocardiographic Image Analysis in Detecting Cardiac Structural and Functional Abnormalities: A UK Biobank StudySemantic proximity Read ↗

Machine Learning, Deep Learning, and Hybrid Approaches for ECG-Based Cardiac Arrhythmia Detection: Techniques, Challenges, and Future PerspectivesSemantic proximity Read ↗

Unraveling diagnostic co-morbidity makeup of each HF category as characteristically derived by ECG- and ECHO-findingsSemantic proximity Read ↗

Multimodal Artificial Intelligence Approaches for the Diagnosis and Understanding of Heart Failure: A Contemporary ReviewSemantic proximity Read ↗

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogramSemantic proximity Read ↗

Composite Artificial Intelligence–Enabled Electrocardiogram for Detection and Prediction of Structural Heart DiseaseSemantic proximity Read ↗

Diagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine learningSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.