convolutional neural networks

The Deep-Match Framework: R-Peak Detection in Ear-ECG

The Deep-Match Framework: R-Peak Detection in Ear-ECG
The Deep-Match Framework: R-Peak Detection in Ear-ECG 789 444 IEEE Transactions on Biomedical Engineering (TBME)
In this work we created an efficient and interpretable deep-learning “matched filter” for precise R-peak detection in wearable ear-ECG signals with poor signal-to-noise ratios. read more
Quantification and Analysis of Laryngeal Closure from Endoscopic Videos

Quantification and Analysis of Laryngeal Closure from Endoscopic Videos
Author(s)3: Jianyu Lin, Emil S. Walsted, Vibeke Backer, James H. Hull, Daniel S. Elson
Quantification and Analysis of Laryngeal Closure from Endoscopic Videos 170 177 IEEE Transactions on Biomedical Engineering (TBME)

At present, there are no objective techniques to quantify and describe laryngeal obstruction, and the reproducibility of subjective manual quantification methods is insufficient, resulting in diagnostic inaccuracy and a poor…

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Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks
Author(s)3: Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the…

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