Exploiting speech tremors: machine learning for early diagnosis of amyotrophic lateral sclerosis

Abstract Neurodegenerative diseases pose significant challenges in healthcare, with Amyotrophic Lateral Sclerosis (ALS) being one such rare yet debilitating condition affecting motor neurons. Machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in healthcare, offering insights and solutions for various medical conditions. This study investigates the application of ML to enhance early ALS diagnosis through the analysis of tremors in sustained speech. By focusing on tremor detection as a diagnostic marker, the research employs ML algorithms to develop predictive models capable of distinguishing ALS patients from healthy controls. The dataset comprises 54 patients from the Republican Research and Clinical Centre of Neurology and Neurosurgery in Belarus, Minsk. The study adopts a two-faceted approach: (1) Exploratory voice analysis to identify tremors associated with ALS in speech samples. (2) Development of ML algorithms to construct predictive models for early ALS diagnosis based on the identified tremors. The ML models exhibit promising results in distinguishing ALS patients from healthy controls based on speech analysis. Tremor detection in sustained speech proves to be an effective marker for early ALS diagnosis. While initial findings are encouraging, larger-scale studies are required to validate the clinical applicability of this approach. The successful application of ML and AI in early ALS diagnosis by leveraging innovative approaches, such as tremor detection in sustained speech, we can enhance early diagnosis and improve patient outcomes in neurodegenerative diseases like ALS on a broader scale.