Early identification of bulbar motor dysfunction in ALS: An approach using AFM signal decomposition

Abstract

Amyotrophic lateral sclerosis (ALS) is an aggressive neurodegenerative disorder that impacts the nerve cells in the brain and spinal cord that control muscle movements. Early ALS symptoms include speech and swallowing difficulties, and sadly, the disease is incurable and fatal in some instances. This study aims to construct a predictive model for identifying speech dysarthria and bulbar motor dysfunction in ALS patients, using speech signals as a non-invasive biomarker. Utilizing an amplitude and frequency modulated (AFM) signal decomposition model, the study identifies distinctive characteristics crucial for monitoring and diagnosing ALS. The study focuses on classifying ALS patients and healthy controls (HC) through a machine-learning approach, employing the TORGO database for analysis. Recognizing speech signals as potential biomarkers for ALS detection, the study aims to achieve early identification without invasive measures. An ensemble learning classifier attains a remarkable 97% accuracy in distinguishing between ALS and HC based on features extracted using the AFM signal model.