Detecting a History of Repetitive Head Impacts from a Short Voice Recording

Repetitive head impacts (RHI) are associated with an increased risk of developing various neurodegenerative disorders, such as Alzheimers disease (AD), Parkinsons disease (PD), and most notably, chronic traumatic encephalopathy (CTE). While the clinical presentation of AD and PD is well established, CTE can only be diagnosed post-mortem. Therefore, a distinction can be made between the pathologically defined CTE and RHI-related functional or structural brain changes (RHI-BC) which may result in CTE. Unfortunately, there are currently no accepted biomarkers of CTE nor RHI-BC, a major hurdle to achieving clinical diagnoses. Interestingly, speech has shown promise as a potential biomarker of both AD and PD, being used to accurately classify individuals with AD and PD from those without. Given the overlapping symptoms between CTE, RHI-BC, PD and AD, we aimed to determine if speech could be used to identify individuals with a history of RHI from those without. We therefore created the Verus dataset, consisting of 13 second voice recordings from 605 professional fighters (RHI group) and 605 professional athletes in non-contact sports (control group) for a total of 1210 recordings. Using a deep learning approach, we achieved 85% accuracy in detecting individuals with a history of RHI from those without. We then used our model trained on the Verus dataset to fine-tune on publicly available AD and PD speech datasets and achieved new state-of-the-art accuracies of 84.99% on the AD dataset and 89% on the PD dataset. Finding a biomarker of CTE and RHI-BC that presents early in disease progression is critical to improve risk management and patient outcome. Our study is the first we are aware of to investigate speech as such a candidate biomarker of RHI-BC.