medical-3

Atrial Fibrillation Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in atrial-fibrillation-detection-3

Trend

Dataset

Best Model

Actions

MIT-BIH AF

PhysioNet Challenge 2017

Libraries

i

Use these libraries to find atrial-fibrillation-detection-3 models and implementations

Datasets

AF Classification from a Short Single Lead ECG Recording - The PhysioNet Computing in Cardiology Challenge 2017

CPSC2021

IRIDIA-AF

Subtasks

No subtasks available.

Most implemented papers

Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

Yadong Zhang, Xin Chen•Tue Dec 08 2020

The TMF classification model has very good clinical interpretability and the patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.

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Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

G. Clifford, S. Shashikumar, Amit J. Shah, S. Nemati•Sun May 06 2018

The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors and the cross-domain generalizablity of the approach is demonstrated by adapting the learned model parameters from one recording modality to another with improved AF detection performance.

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End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

Igor Gotlibovych, Stuart Crawford, Dileep Goyal, Jiaqi Liu, Yaniv Kerem, D. Benaron, Defne Yilmaz, G. Marcus, Yihan Li•Thu Jul 26 2018

This work uses a prototype multi-sensor wearable device to collect over 180h of photoplethysmography data sampled at 20Hz and end-to-end learning to achieve state-of-the-art results in detecting AFib from raw PPG data, bringing large-scale atrial fibrillation screenings within imminent reach.

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Construe: a software solution for the explanation-based interpretation of time series

T. Teijeiro, P. Félix•Mon Mar 16 2020

A comprehensive knowledge base for the electrocardiogram (ECG) domain is provided, so it can be used directly as a tool for ECG analysis and has been successfully validated in several noteworthy problems, such as heartbeat classification or atrial fibrillation detection.

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Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM

Jiacheng Wang, Weiheng Li•Wed Nov 11 2020

This paper proposed, implemented, and compared an automated system using two different frameworks of the combination of convolutional neural network (CNN) and long-short term memory (LSTM) for classifying normal sinus signals, atrial fibrillation, and other noisy signals for ECG signal detection.

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End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices

Vineeth Vijayaraghavan, J. B. Sideshwar, T. Sachin Krishan, V. Nagarajan, Shanthakumar S•Mon Nov 22 2021

The feature engineering employed in this research catered to optimizing the resource-efficient classifier used in the proposed pipeline, which was able to outperform the best performing standard ML model by 105 × in terms of memory footprint with a mere trade-off of 2% classification accuracy.

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Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing

Hao Wen, Ji-Su Kang•Fri Apr 08 2022

A deep learning framework, namedtorch, is proposed, which gathers a large number of neural networks, both from literature and novel, for various ECG processing tasks, and establishes a convenient and modular way for automatic building and flexible scaling of the networks.

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Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning

G. Clifford, M. Reyna, Christoph Reich, F. Plesinger, M. Rohr, Andreas Höhl, T. Lilienthal, Tizian C. Dege, Veronika Bulkova, Christoph Hoog Antink•Sun Jun 12 2022

Results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’ are reported.

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Efficient ECG-Based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks

Leonie Basso, Zhao Ren, W. Nejdl•Wed Oct 26 2022

This work proposes to use lightweight convolutional neural networks with parameterised hypercomplex (PH) layers for AF detection based on ECGs and shows comparable performance to corresponding real-valued CNNs on two publicly available ECG datasets using significantly fewer model parameters.

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