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Heart function monitoring, prediction and prevention of Heart Attacks: Using Artificial Neural Networks

https://doi.org/10.1109/ic3i.2014.7019580

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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An arrhythmia detector and heart rate estimator for overnight polysomnography studies

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ECG analysis: a new approach in human identification

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A neural network with asymmetric basis functions for feature extraction of ECG P waves

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Artificial neural networks for automatic ECG analysis

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ECG analysis using nonlinear PCA neural networks for ischemia detection

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Heart Rate Prediction Model Based on Physical Activities Using Evolutionary Neural Network

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Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems

inferredcites →https://doi.org/10.1109/tbme.2005.844024openalex-snapshot

High-Resolution Analysis of Ambulatory Electrocardiograms to Detect Possible Mechanisms of Premature Ventricular Beats

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Aging of Complex Heart Rate Dynamics

inferredcites →https://doi.org/10.1109/tbme.2006.880879openalex-snapshot

Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features

inferredcites →https://doi.org/10.1109/tbme.2012.2213597openalex-snapshot

ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection

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On the Detection of Myocadial Scar Based on ECG/VCG Analysis

inferredcites →https://doi.org/10.1109/tifs.2011.2108650openalex-snapshot

Privacy-Preserving ECG Classification With Branching Programs and Neural Networks

inferredcites →https://doi.org/10.1109/tnn.2007.900239openalex-snapshot

Block-Based Neural Networks for Personalized ECG Signal Classification

inferredcites →https://doi.org/10.1109/tnn.2008.2012031openalex-snapshot

Finding Features for Real-Time Premature Ventricular Contraction Detection Using a Fuzzy Neural Network System

inferredcites →https://doi.org/10.9756/bijsesc.4336openalex-snapshot

Heart Disease Prediction System Using Supervised Learning Classifier

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Contributions to interoperability, scalability and formalization of personal health systems

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