ArticleSensors (Basel, Switzerland)2019
Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07051356 (Advancing Proactive Care in Advanced Heart Failure), which is not on this map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Advancing Proactive Care in Advanced Heart Failure: Integrating AI and Continuous Remote Monitoring for Early Detection of Heart Failure Deterioration
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Predicting ventricular arrhythmia in myocardial ischemia using deep learning.Heart rhythm O2 · 2026Article
- Frailty identification using a sensor-based upper-extremity function test: a deep learning approach.Scientific reports · 2025Article
- Artificial Intelligence in Diagnosis of Heart Failure.Journal of the American Heart Association · 2025Review
- Fluid volume status detection model for patients with heart failure based on machine learning methods.Heliyon · 2025Article
- Machine learning-based screening of heart failure using the integrated features of electrocardiogram and phonocardiogram: a multicenter study in China.Frontiers in cardiovascular medicine · 2025Article
- Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.ESC heart failure · 2024Article
- Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study.JMIR cardio · 2024Article
- A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection.Frontiers in cardiovascular medicine · 2024Article
- Multifractal foundations of biomarker discovery for heart disease and stroke.Scientific reports · 2023Article
- CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2022Article
- Smart Consumer Wearables as Digital Diagnostic Tools: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach.IEEE journal of biomedical and health informatics · 2022Article
- Decision Support Systems in HF based on Deep Learning Technologies.Current heart failure reports · 2022Review
- Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure.Journal of pharmacokinetics and pharmacodynamics · 2022Review
- Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.International journal of environmental research and public health · 2021Article
- A Machine Learning Approach for Chronic Heart Failure Diagnosis.Diagnostics (Basel, Switzerland) · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients' health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.