Acoustic signatures of bulbar ALS: Predictive modeling with sustained vowels and LightGBM
This paper demonstrates the applicability of acoustic analysis and machine learning for early ALS detection. The proposed method provides an affordable, low-cost, and non-invasive way for ALS diagnosis with potential for application in telemedicine and clinical settings. Future research must expand datasets and integrate additional diagnostic modalities to improve the model's robustness and clinical translation.
Authors
- Mahsa Asadi Anar (ORCID: https://orcid.org/0000-0002-5772-2472)
- Niloofar Deravi
- Zahra Farrokhi (ORCID: https://orcid.org/0009-0007-5699-1017)
- Zahra Mashhadi Tafreshi
- Maryam Valifard
- Salar Yousefzadeh
- Seyed Amirali Zakavi
- Arian Sarafraz
- Omid Anbiyaee
- Navid Rostami
Institutions
- Islamic Azad University of Garmsar (IR)
- Iran University of Medical Sciences (IR)
- Namazi Hospital (IR)
- Ardabil University of Medical Sciences (IR)
- Islamic Azad University, Jahrom Branch (IR)
- Shahid Beheshti University of Medical Sciences (IR)
- Qazvin University of Medical Sciences (IR)
Publication Details
- Journal
- eNeurologicalSci
- Published
- 2025-07-30
- DOI
- https://doi.org/10.1016/j.ensci.2025.100579
- Citations
- 1
- Primary Topic
- Voice and Speech Disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.16