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The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.
Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults.
#4 best model for Atrial Fibrillation Detection on MIT-BIH AF
The first approach used a pretrained DenseNet model to extract features that were then classified using Support Vector Machines, and the second approach used the spectrograms as direct input into a convolutional network.
#6 best model for Atrial Fibrillation Detection on MIT-BIH AF
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i. e., time varying spectrum) and deep convolutional networks.
The complexity of the patterns associated with Atrial Fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the current signal processing and shallow machine learning approaches to get accurate AF detection results.
The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities.
#2 best model for Atrial Fibrillation Detection on MIT-BIH AF
We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data.
The article focuses on determining the predictive uncertainty of a model on the example of atrial fibrillation detection problem by a single-lead ECG signal.
We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance.
Results Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation.
#3 best model for Atrial Fibrillation Detection on MIT-BIH AF