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The Efficacy of the Connect America Fund in Addressing US Internet Access Inequities
Published in ACM SIGCOMM ’24, 2024
In this article, a novel time-frequency (TF) domain deep learning approach is proposed for the automated detection of atrial fibrillation (AF) using multi-lead ECG signals. The wavelet-based synchrosqueezing transform (WSST) is used to evaluate the TF image of each lead ECG signal. Furthermore, the modified vision transformer (ViT) model detects AF arrhythmia using the WSST-based TF images of the three best lead ECG signals. The proposed approach is tested using a public database’s multi-lead ECG time series. The results reveal that the suggested WSST-based modified ViT model has obtained the accuracy, precision, recall, and F1-score values of 95.50%, 93.36%, 98.00%, and 0.957, respectively.
Recommended citation: Haarika Manda , Varshika Srinivasavaradhan , Laasya Koduru, Kevin Zhang, Xuanhe Zhou, Udit Paul†, Elizabeth Belding, Arpit Gupta, Tejas N. Narechania‡ . 2024. The Efficacy of the Connect America Fund in Addressing US Internet Access Inequities. In ACM SIGCOMM 2024 Conference (ACM SIGCOMM ’24), August 4–8, 2024, Sydney, NSW, Australia. ACM, New York, NY, USA, 22 pages. https: //doi.org/10.1145/3651890.3672272 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10068090
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