🎯 Feel happy to receive the acceptance of 𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 from 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 𝐢𝐧 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 (𝐐𝟐, 𝐈𝐅:𝟐.𝟒)📣
The paper we submitted was formally accepted (𝐏𝐏𝐆𝟐𝐑𝐞𝐬𝐩𝐍𝐞𝐭: 𝐀 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐑𝐞𝐬𝐩𝐢𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐢𝐠𝐧𝐚𝐥 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬 𝐚𝐧𝐝 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐏𝐡𝐨𝐭𝐨𝐩𝐥𝐞𝐭𝐡𝐲𝐬𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐲 (𝐏𝐏𝐆) 𝐒𝐢𝐠𝐧𝐚𝐥) in 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 𝐢𝐧 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞, 𝐒𝐩𝐫𝐢𝐧𝐠𝐞𝐫 (𝐐𝟐, 𝐈𝐅:𝟐.𝟒)
This work was done in collaboration with 𝐐𝐚𝐭𝐚𝐫 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 (𝐐𝐚𝐭𝐚𝐫) and Kuwait College of Science and Technology (Kuwait).
The proposed PPG2RespNet draws inspiration from the UNet and UNet++ models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to extract respiratory signals autonomously and efficiently. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory
My sincere thanks go out to my close collaborator, Muhammad E. H. Chowdhury, Ph.D. from Qatar University, as well as the other authors for their support, tireless efforts, and hard work toward this publication in a highly regarded medical journal. I would like to thank the team for the successful attempt.
Hope to see the online version soon...
Ultrasound product specialist - Middle East &Africa at TMSP
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