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arXiv Machine Learning By Timothy C Sweeney-Fanelli, Ajan Ahmed, Masudul Imtiaz

Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models

Read the original on arXiv Machine Learning →

The paper evaluates deep learning models for electrocardiogram‑based emotion recognition, focusing on generalization across datasets rather than dataset‑specific performance. It introduces two open‑source tools—ARRC for standardized benchmarking and ARDT for inter‑dataset training—to merge three public AER datasets (CUADS, ASCERTAIN, DREAMER) into a more variable benchmark. Using these tools, the authors compare three prominent deep learning architectures and two CNN baselines with hyperparameter tuning and 10‑fold cross‑validation, revealing trade‑offs between accuracy and model complexity and providing a reproducible benchmark for future research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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