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Found 6 papers, 0 papers with code
Learning ECG signal features without backpropagation
Representation learning has become a crucial area of research in machine learning, as it aims to discover efficient ways of representing raw data with useful features to increase the effectiveness, scope and applicability of downstream tasks such as classification and prediction.
Diagnostic Quality Assessment for Low-Dimensional ECG Representations
In a case study, we quantified the agreement between raw and reconstructed (denoised) ECG recordings by means of kappa-based statistical tests.
ECG Beat Representation and Delineation by means of Variable Projection
Methods: We therefore propose a model that is based on Hermite and sigmoid functions combined with piecewise polynomial interpolation for exact segmentation and low-dimensional representation of individual ECG beat segments.
Abnormal Road Surface Detection Using Wheel Sensor Data
In this article, we develop a measurement system for intelligent tires equipped with a 3-D piezoresistive force sensor.
Hybrid star properties from an extended linear sigma model
The equation of state provided by effective models of strongly interacting matter should comply with the restrictions imposed by current astrophysical observations on compact stars.
Nuclear Theory High Energy Astrophysical Phenomena High Energy Physics - Phenomenology
VPNet: Variable Projection Networks
Based on these advantages and the promising results obtained, we anticipate a profound impact on the broader field of signal processing, in particular on classification, regression and clustering problems.
