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- W4206249788 abstract "Atrial fibrillation (AF) is a common arrhythmia (0.5% worldwide prevalence) associated with an increased risk of various cardiovascular disorders, including stroke. Automated routine AF detection by Electrocardiogram (ECG) is based on the analysis of one-dimensional ECG signals and requires dedicated software for each type of device, limiting its wide use, especially with the rapid incorporation of telemedicine into the healthcare system. Here, we implement a machine learning method for AF classification using the region of interest (ROI) corresponding to the long DII lead automatically extracted from DI-COM 12-lead ECG images. We observed 94.3%, 98.9%, 99.1%, and 92.2% for sensitivity, specificity, AUC, and F1 score, respectively. These results indicate that the proposed methodology performs similar to one-dimensional ECG signals as input, but does not require a dedicated software facilitating the integration into clinical practice, as ECGs are typically stored in PACS as 2D images." @default.
- W4206249788 created "2022-01-26" @default.
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- W4206249788 date "2021-09-13" @default.
- W4206249788 modified "2023-10-17" @default.
- W4206249788 title "2D Image-Based Atrial Fibrillation Classification" @default.
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- W4206249788 doi "https://doi.org/10.23919/cinc53138.2021.9662735" @default.
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