Computer ScienceScientific Reports
Towards better heartbeat segmentation with deep learning classification
P. Silva, E. Luz, et al.
This paper presents an innovative real-time method for validating heartbeat segmentation using convolutional neural networks (CNNs), designed to minimize false positive alarms. With application evaluations on the MIT-BIH and CYBHI databases, conducted by Pedro Silva, Eduardo Luz, Guilherme Silva, Gladston Moreira, Elizabeth Wanner, Flavio Vidal, and David Menotti, this approach shows promise for real-time applications and could potentially be integrated into dedicated hardware.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
GEMTELLIGENCE: Accelerating gemstone classification with deep learning
T. Bendinelli, L. Biggio, et al.
Engineering and Technology
Towards understanding structure-property relations in materials with interpretable deep learning
T. Vu, M. Ha, et al.
Medicine and Health
Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens
C. Yoon, E. Park, et al.
Physics
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations
E. Tey, D. Moldovan, et al.
