#Atrial fibrillation vs flutter eeg manual#
However, manual revision of large amounts of ECG data is highly time-consuming, might be influenced by reviewers' subjectivity, and represents a significant financial burden in healthcare ( Lee et al., 2008 Kim et al., 2011 Ball et al., 2013). For this reason, it is a clinical recommendation to monitor stroke patients with long-term ECG recordings to assess the presence of brief AF episodes ( Hindricks et al., 2020).
Additionally, in case of delayed management of such brief episodes, they may evolve into longer AF episodes ( Hindricks et al., 2020). Atrial fibrillation is a progressive arrhythmia for which even brief episodes could represent a risk factor for thrombus formation and stroke ( Healey et al., 2012). However, less information is known for episodes shorter than 30 s, frequently known as brief AF. Guidelines for the management of AF episodes lasting more than 30 s are available ( Hindricks et al., 2020). Results: For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Ĭonclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.Ītrial fibrillation (AF) is the most common heart rhythm disorder found in clinical practice, and it is highly correlated with stroke ( Wolf et al., 1991). Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection.
#Atrial fibrillation vs flutter eeg plus#
Detection of AF is done using a sliding window of 10 beats plus 3 s. Materials and Methods: The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF.
This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning. However, some deep learning approaches do not provide analysis of the features used for classification. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis.
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