Evolving Reservoir-Enhanced Neural Architectures for Biomedical Signal Classification - Milagres, Bárbara; Silva, Guilherme; Negrão, Arthur; G. Júnior, Ederson N. F.; Vieira, Matheus; Silva, Pedro (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Neural Architecture Search (NAS) has become an effective approach for automating neural network design, including applications to health data. In this work, we extend the GeneticNAS framework by incorporating Reservoir Computing operations into the search space, enabling the evolution of architectures that combine convolutional layers with reservoir-based dynamics to better capture temporal dependencies in biomedical signals. We evaluate the proposed approach on two benchmark datasets: MIT-BIH Arrhythmia (ECG) and PhysioNet EEG for biometric identification. Experimental results show consistent improvements over the baseline, with gains of 2% and 5.5% in accuracy and F1-Score respectively for arrhythmia detection for EEG biometrics.