Atrial Fibrillation Detection
14 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Atrial Fibrillation Detection
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Datasets
AF Classification from a Short Single Lead ECG Recording - The PhysioNet Computing in Cardiology Challenge 2017
IRIDIA-AF
Latest papers
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
Previous deep learning models learn from a single modality, either electrocardiogram (ECG) or photoplethysmography (PPG) signals.
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
subangkar/simsig
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In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals.
Learned Kernels for Sparse, Interpretable, and Efficient Medical Time Series Processing
sullychen/smolk
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Results: Our interpretable method achieves greater than 99% of the performance of the state-of-the-art methods on the PPG artifact detection task, and even outperforms the state-of-the-art on a challenging out-of-distribution test set, while using dramatically fewer parameters (2% of the parameters of Segade, and about half of the parameters of Tiny-PPG).
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8. 5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets.
Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
leibniz-future-lab/hypercomplexecg
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Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke.
Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning
ChristophReich1996/ECG_Classification
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Physiological Measurement 2022
During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes.
Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing
In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation.
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices
vishaln15/OptimizedArrhythmiaDetection
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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 $10^5\times$ in terms of memory footprint with a mere trade-off of 2% classification accuracy.
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
ydup/Anomaly-Detection-in-Time-Series-with-Triadic-Motif-Fields
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Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models.
Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM
liweiheng818/ECG-Signal-Analysis
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It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals.
