ALS Detection Using Frequency Analysis and Machine Learning
DOI:
https://doi.org/10.32870/recibe.v14i3.451Keywords:
Amyotrophic Lateral Sclerosis, Machine Learning, Frequency AnalysisAbstract
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative condition that deteriorates motor neurons, leading to muscle weakness and impairments in voluntary control of movement, speech, and facial expressions. Current diagnostic methods, based on clinical evaluations and specialized tests present significant delays, affecting patient survival and quality of life. This study proposes a non-invasive method to detect ALS by characterizing facial markers in the frequency domain through machine learning algorithms.References
- Richards, D., Morren, J. A., & Pioro, E. P. (2020). Time to diagnosis and factors affecting diagnostic delay in amyotrophic lateral sclerosis.
- Bandini, A., Green, J. R., Taati, B., Orlandi, S., Zinman, L., & Yunusova, Y. (2018). Automatic Detection of Amyotrophic Lateral Sclerosis (ALS) from Video-Based Analysis of Facial Movements: Speech and Non-Speech Tasks. 2018
- Gomes, N., Yoshida, A., Roder, M., Camargo De Oliveira, G., & Papa, J. (2024). Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
- Bandini, A., Rezaei, S., Guarin, D. L., Kulkarni, M., Lim, D., Boulos, M. I., Zinman, L., Yunusova, Y., & Taati, B. (2021). A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders
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- 2026-06-21 (2)
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Copyright (c) 2026 ReCIBE, electronic journal of Computing, Informatics, Biomedical and Electronics
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