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Found 6 papers, 5 papers with code
CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models
Despite the excelling performance of machine learning models, understanding the decisions of machine learning models remains a long-standing goal.
Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care
Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy.
Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals
In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format.
Diffusion-based Conditional ECG Generation with Structured State Space Models
Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data.
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines.
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence
Explanation results obtained from the LIME, SmoothGrad, and Grad-CAM highlight important features of different spectrograms and their relevance to classification.
