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Found 8 papers, 2 papers with code
Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring
A frequent problem in statistical science is how to properly handle missing data in matched paired observations.
Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology
Next, we adapt or use existing models of variable selection, regression, and conformal inference to obtain new clinical findings about glucose changes five years ahead with the AEGIS data.
Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification
Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single lead ECG signals, emphasizing the importance of the interpretability of the classification results.
Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records
In this work we propose a new method for the rhythm classification of short single-lead ECG records, using a set of high-level and clinically meaningful features provided by the abductive interpretation of the records.
Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian Processes
The application of the method to real data demonstrates its ability to capture the behaviour of complex systems.
On the adoption of abductive reasoning for time series interpretation
Time series interpretation aims to provide an explanation of what is observed in terms of its underlying processes.
Using temporal abduction for biosignal interpretation: A case study on QRS detection
In this work, we propose an abductive framework for biosignal interpretation, based on the concept of Temporal Abstraction Patterns.
A method for context-based adaptive QRS clustering in real-time
The method processes the QRS complexes sequentially, grouping them into a dynamic set of clusters based on the information content of the temporal context.
