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A Hybrid Deep Learning Approach for Heart Failure Stratification

https://doi.org/10.2139/ssrn.4768582
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/0002-8703(94)90059-0openalex-extracted
Heart rate variability: A measure of cardiac autonomic tone
inferredcites →https://doi.org/10.1016/j.carpath.2011.11.007openalex-extracted
The pathophysiology of heart failure
inferredcites →https://doi.org/10.1016/j.cmpb.2006.05.002openalex-extracted
Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals
inferredcites →https://doi.org/10.1016/j.cmpb.2015.08.007openalex-extracted
Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability
inferredcites →https://doi.org/10.1016/j.cmpb.2021.105940openalex-extracted
Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings
inferredcites →https://doi.org/10.1016/j.cmpb.2022.106951openalex-extracted
Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning
inferredcites →https://doi.org/10.1016/j.jjcc.2011.12.006openalex-extracted
Heart failure as an autonomic nervous system dysfunction
inferredcites →https://doi.org/10.1067/mhl.2002.124554openalex-extracted
Validity and reliability of the NYHA classes for measuring research outcomes in patients with cardiac disease
inferredcites →https://doi.org/10.1080/00140137308924484openalex-extracted
An Analysis of Heart Rate Variability
inferredcites →https://doi.org/10.1097/00001573-199801000-00006openalex-extracted
Heart rate variability
inferredcites →https://doi.org/10.1109/bhi.2017.7897295openalex-extracted
Estimation of New York Heart Association class in heart failure patients based on machine learning techniques
inferredcites →https://doi.org/10.1109/bibm.2018.8621518openalex-extracted
Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record
inferredcites →https://doi.org/10.1109/dicta.2016.7797091openalex-extracted
Understanding Data Augmentation for Classification: When to Warp?
inferredcites →https://doi.org/10.1109/icabme.2017.8167546openalex-extracted
Predicting heart failure class using a sequence prediction algorithm
inferredcites →https://doi.org/10.1109/icdm.2017.106openalex-extracted
Generating Synthetic Time Series to Augment Sparse Datasets
inferredcites →https://doi.org/10.1109/icomet48670.2020.9073799openalex-extracted
Classification of Mental Workload (MWL) using Support Vector Machines (SVM) and Convolutional Neural Networks (CNN)
inferredcites →https://doi.org/10.1109/ijcnn.2008.4633794openalex-extracted
Combination approach of SMOTE and biased-SVM for imbalanced datasets
inferredcites →https://doi.org/10.1109/jbhi.2013.2244902openalex-extracted
Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability
inferredcites →https://doi.org/10.1109/tbme.2010.2092776openalex-extracted
Remote Health Monitoring of Heart Failure With Data Mining via CART Method on HRV Features
inferredcites →https://doi.org/10.1109/tsmc.2017.2705582openalex-extracted
Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients
inferredcites →https://doi.org/10.1161/01.cir.101.23.e215openalex-extracted
PhysioBank, PhysioToolkit, and PhysioNet
inferredcites →https://doi.org/10.1161/circresaha.113.300268openalex-extracted
Epidemiology of Heart Failure
inferredcites →https://doi.org/10.1161/circresaha.114.302589openalex-extracted
The Autonomic Nervous System and Heart Failure
inferredcites →https://doi.org/10.1371/journal.pone.0165304openalex-extracted
A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement
inferredcites →https://doi.org/10.1371/journal.pone.0254841openalex-extracted
An empirical survey of data augmentation for time series classification with neural networks
inferredcites →https://doi.org/10.15420/cfr.2016:25:2openalex-extracted
Global Public Health Burden of Heart Failure
inferredcites →https://doi.org/10.1613/jair.953openalex-extracted
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.24963/ijcai.2021/631openalex-extracted
Time Series Data Augmentation for Deep Learning: A Survey
inferredcites →https://doi.org/10.3389/fphys.2012.00148openalex-extracted
Role of editing of R–R intervals in the analysis of heart rate variability
inferredcites →https://doi.org/10.3390/app9122544openalex-extracted
Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
inferredcites →https://doi.org/10.3390/s20010098openalex-extracted
Data Augmentation with Suboptimal Warping for Time-Series Classification
inferredcites →https://doi.org/10.3390/s22239190openalex-extracted
CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals
inferredcites →https://doi.org/10.48550/arxiv.1808.02455openalex-extracted
Data augmentation using synthetic data for time series classification with deep residual networks
inferredcites →https://doi.org/10.5220/0005595502260234openalex-extracted
SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based Undersampling
inferredcites →https://doi.org/10.7326/0003-4819-118-6-199303150-00008openalex-extracted
Heart Rate Variability
inferredcites →https://doi.org/10.5281/zenodo.1311684openalex-snapshot
Estimation Of New York Heart Association Class In Heart Failure Patients Based On Machine Learning Techniques
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