Evolving Reservoir-Enhanced Neural Architectures for Biomedical Signal Classification

• Bárbara Milagres UFOP
• Guilherme Silva UFOP
• Arthur Negrão UFOP
• Ederson N. F. G. Júnior UFOP
• Matheus Vieira UFOP
• Pedro Silva UFOP

DOI: https://doi.org/10.5753/sbcas.2026.21314

Abstract

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.

References

Amberkar, A., Awasarmol, P., Deshmukh, G., and Dave, P. (2018). Speech recognition using recurrent neural networks. In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pages 1–4.

Chu, X., Zhang, B., and Xu, R. (2021). Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search. In Proceedings of the IEEE/CVF International Conference on computer vision, pages 12239–12248.

De Chazal, P., O’Dwyer, M., and Reilly, R. B. (2004). Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 51(7):1196–1206.

Freitas, C., Silva, P., Moreira, G., and Luz, E. (2022). Rede neural convolucional e lstm para biometria baseada em eeg no modo de identificaçao. In Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 256–267. SBC.

Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet. Circulation, 101(23):e215–e220.

Habi, H. V. and Rafalovich, G. (2019). Genetic network architecture search. arXiv preprint arXiv:1907.02871.

Li, C., Zhang, Z., Zhang, X., Huang, G., Liu, Y., and Chen, X. (2022). Eeg-based emotion recognition via transformer neural architecture search. IEEE transactions on industrial informatics, 19(4):6016–6025.

Liang, Z. and Sun, Y. (2024). Evolutionary neural architecture search for multivariate time series forecasting. In Asian Conference on Machine Learning, pages 771–786. PMLR.

Llamedo, M. and Martínez, J. P. (2011). Heartbeat classification using feature selection driven by database generalization criteria. IEEE Transactions on Biomedical Engineering, 58(3):616–625.

Lu, Z., Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., and Banzhaf, W. (2019). Nsga-net: neural architecture search using multi-objective genetic algorithm. In Proceedings of the genetic and evolutionary computation conference, pages 419–427.

Moody, G. B. and Mark, R. G. (2001). The impact of the mit-bih arrhythmia database. IEEE engineering in medicine and biology magazine, 20(3):45–50.

Pak, M. and Kim, S. (2017). A review of deep learning in image recognition. In 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), pages 1–3.

Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J. (2018). Efficient neural architecture search via parameters sharing. In International conference on machine learning, pages 4095–4104. PMLR.

Rakhshani, H., Fawaz, H. I., Idoumghar, L., Forestier, G., Lepagnot, J., Weber, J., Brévilliers, M., and Muller, P.-A. (2020). Neural architecture search for time series classification. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.

Rala Cordeiro, J., Raimundo, A., Postolache, O., and Sebastião, P. (2021). Neural architecture search for 1D CNNs—different approaches tests and measurements. Sensors, 21(23):7990.

Real, E., Aggarwal, A., Huang, Y., and Le, Q. V. (2019). Regularized evolution for image classifier architecture search. In Proceedings of the aaai conference on artificial intelligence, volume 33, pages 4780–4789.

Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Chen, X., and Wang, X. (2021). A comprehensive survey of neural architecture search: Challenges and solutions. ACM Computing Surveys (CSUR), 54(4):1–34.

Shin, R., Packer, C., and Song, D. (2018). Differentiable neural network architecture search.

Singh, S. P., Kumar, A., Darbari, H., Singh, L., Rastogi, A., and Jain, S. (2017). Machine translation using deep learning: An overview. In 2017 International Conference on Computer, Communications and Electronics (Comptelix), pages 162–167.

Thanh, T. H., Doan, L., Luong, N. H., and Huynh Thi Thanh, B. (2024). Thnas-ga: A genetic algorithm for training-free hardware-aware neural architecture search. In Proceedings Of The Genetic And Evolutionary Computation Conference, pages 1128–1136.

Tian, S., Qu, L., Wang, L., Hu, K., Li, N., and Xu, W. (2021). A neural architecture search based framework for liquid state machine design. Neurocomputing, 443:174–182.

Wen, L., Gao, L., Li, X., and Li, H. (2022). A new genetic algorithm based evolutionary neural architecture search for image classification. Swarm and Evolutionary Computation, 75:101191.

Wistuba, M., Rawat, A., and Pedapati, T. (2019). A survey on neural architecture search. arXiv preprint arXiv:1905.01392.

Zhang, H. and Vargas, D. V. (2023). A survey on reservoir computing and its interdisciplinary applications beyond traditional machine learning. IEEE Access, 11:81033–81070.

Zhou, Y., Jin, Y., Sun, Y., and Ding, J. (2023). Surrogate-assisted cooperative co-evolutionary reservoir architecture search for liquid state machines. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(5):1484–1498.

Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8697–8710.