Mehmet Yigit Balik

Mehmet Yigit Balik

Doctoral Researcher in Machine Learning at Aalto University, Finland.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Interpretable ECG analysis for myocardial infarction detection through counterfactuals

Published in Biomedical Signal Processing and Control, 2025

In this study, we propose a novel method for utilizing counterfactual explanations in ECG analysis, specifically for the detection of myocardial infarction. Our approach leverages the PTB-XL dataset and incorporates systematic feature extraction and refinement techniques to enhance interpretability for clinicians. The Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method aims to bridge the gap between advanced data analysis and clinical decision-making.

Paper | Bibtex

Conference Papers

Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models

Published in Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025

We propose a new way of utlizing Gaussian process priors in deep generative models, specifically variational autoencoders, with global parameterization that avoids explicit kernels, runs in linear time, eliminates the amortization gap, overcomes the limitations of categorical inducing point optimization, enhances interpretability in the latent space by quantifying the contributions of different covariates/effects using Sobol indices, allows for standard mini‑batch training, and treats kernel hyperparameters probabilistically.

Paper | Bibtex

Interpretable Deep Learning for Myocardial Infarction Detection from ECG Signals

Published in 31st Signal Processing and Communications Applications Conference (SIU), 2023

In this study, we show that deep learning models can be trained to detect myocardial infarction (MI) from 12-lead ECG signals. We also show that the model can be made interpretable by using gradient class activation maps (Grad-CAMs) to highlight the segments of the ECG that contribute most to the decision.

Paper | Bibtex