AIMC Topic: Electrodiagnosis
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Deep Learning on 1-D Biosignals: a Taxonomy-based Survey.
Yearbook of medical informatics
Aug 29, 2018
OBJECTIVES: Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep ...
Sparse representation of electrodermal activity with knowledge-driven dictionaries.
IEEE transactions on bio-medical engineering
Dec 4, 2014
Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven me...
A WaveNet-based model for predicting the electroglottographic signal from the acoustic voice signal.
The Journal of the Acoustical Society of America
Apr 1, 2025
The electroglottographic (EGG) signal offers a non-invasive approach to analyze phonation. It is known, if not obvious, that the onset of vocal fold contacting has a substantial effect on how the vocal folds vibrate and on the quality of the voice. G...
Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings.
Physiological reports
Aug 1, 2021
Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of mus...
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