Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)

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Comparative Analysis of Spiking Neural Networks and SNN-Diffusion Hybrid Models for Voice-Based Heart Failure Detection

Authors
T. Jothilakshmi1, *, K. Sathiyamurthy2
1PhD Scholar in Dept. Of CSE, Puducherry Technological University, Puducherry, 605014, India
2Professor in Dept. Of CSE, Puducherry Technological University, Puducherry, 605014, India
*Corresponding author. Email: jothilakshmi.cse12@gmail.com
Corresponding Author
T. Jothilakshmi
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-616-6_44How to use a DOI?
Keywords
Spiking Neural Network (SNN); Diffusion Model; Voice Biomarkers; Heart Failure Detection; Neuromorphic Computing; Biomedical Signal Processing
Abstract

Heart failure alters vocal characteristics due to cardiopulmonary fatigue, leading to measurable acoustic deviations. This study compares a Spiking Neural Network (SNN) and a hybrid SNN–Diffusion model for early heart failure detection from speech recordings. The SNN captures temporal-spike dynamics via leaky integrate-and-fire neurons, while the diffusion model enhances latent feature denoising and generalization. Using normalized Mel-spectrograms and MFCC encodings, both architectures were evaluated on a curated heart-failure speech dataset. The hybrid SNN–Diffusion achieved higher accuracy (95.2%), precision (94.8%), recall (96.1%), and F1-score (95.4%) than the standalone SNN (89.6%). The results confirm that integrating bio-plausible spiking computation with diffusion-based generative modelling enhances robustness to noise and data scarcity, enabling more reliable low-energy biomedical audio analytics.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 March 2026
ISBN
978-94-6239-616-6
ISSN
1951-6851
DOI
10.2991/978-94-6239-616-6_44How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - T. Jothilakshmi
AU  - K. Sathiyamurthy
PY  - 2026
DA  - 2026/03/31
TI  - Comparative Analysis of Spiking Neural Networks and SNN-Diffusion Hybrid Models for Voice-Based Heart Failure Detection
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 585
EP  - 599
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_44
DO  - 10.2991/978-94-6239-616-6_44
ID  - Jothilakshmi2026
ER  -