ALS is a fatal neurodegenerative disease with no cure. Experts typically measure disease progression via the ALSFRS-R score, which includes measurements of various abilities known to decline. We propose instead the use of speech analysis as a proxy for ALS progression. This technique enables 1) frequent non-invasive, inexpensive, longitudinal analysis, 2) analysis of data recorded in the wild, and 3) creation of an extensive ALS databank for future analysis. Patients and trained medical professionals need not be co-located, enabling more frequent monitoring of more patients from the convenience of their own homes. The goals of this study are the identification of acoustic speech features in naturalistic contexts which characterize disease progression and development of machine models which can recognize the presence and severity of the disease. We evaluated subjects from the Prize4Life Israel dataset, using a variety of frequency, spectral, and voice quality features. The dataset was generated using the ALS Mobile Analyzer, a cellphone app that collects data regarding disease progress using a self-reported ALSFRS-R questionnaire and several active tasks that measure speech and motor skills. Classification via leavefive-subjects-out cross-validation resulted in an accuracy rate of 79% (61% chance) for males and 83% (52% chance) for females.

Background: Globally, the annual incidence and prevalence of amyotrophic lateral sclerosis (ALS) are estimated at 1.9 and 4.5 per 100,000 population, respectively. This study is aimed at describing the epidemiology of ALS in Israel in a real-world setting. Methods: A retrospective study was performed using the databases of Maccabi Healthcare Services (MHS), a 2-million-member health maintenance organization in Israel. The study included all MHS adults diagnosed with ALS between 1997 and 2013. In 2013, characteristics of ALS patients were compared to those of age-sex-matched patients without ALS. Survival after ALS diagnosis was assessed until death and until tracheostomy or death (follow-up through 2014). Results: In 2013 (n = 158), the prevalence of ALS was 8.1 per 100,000 population in MHS. In 1997-2013, a total of 375 ALS patients were diagnosed, corresponding to an average annual incidence of 1.8 per 100,000 population in MHS. The median survival from diagnosis to death was 3.5 years (95% CI 2.9-4.1), with approximately 28% surviving at least 10 years. Median tracheostomy-free survival was 2.5 years (95% CI 2.1-2.9). Conclusions: Results suggest that there is a relatively high prevalence of ALS in Israel. Further research is needed to investigate factors that may contribute to the survival of patients with ALS in Israel.

ALS is a fatal neurodegenerative disease with no cure. Experts typically measure disease progression via the ALSFRS-R score, which includes measurements of various abilities known to decline. We propose instead the use of speech analysis as a proxy for ALS progression. This technique enables 1) frequent non-invasive, inexpensive, longitudinal analysis, 2) analysis of data recorded in the wild, and 3) creation of an extensive ALS databank for future analysis. Patients and trained medical professionals need not be co-located, enabling more frequent monitoring of more patients from the convenience of their own homes. The goals of this study are the identification of acoustic speech features in naturalistic contexts which characterize disease progression and development of machine models which can recognize the presence and severity of the disease. We evaluated subjects from the Prize4Life Israel dataset, using a variety of frequency, spectral, and voice quality features. The dataset was generated using the ALS Mobile Analyzer, a cellphone app that collects data regarding disease progress using a self-reported ALSFRS-R questionnaire and several active tasks that measure speech and motor skills. Classification via leavefive-subjects-out cross-validation resulted in an accuracy rate of 79% (61% chance) for males and 83% (52% chance) for females.