Automatic Voice Analysis for Dysphagia Screening in Neurological Patients

Condition(s) studied

Study summary

The proposed study suggests using automatic voice analysis and machine learning algorithms to develop a dysphagia screening tool for neurological patients. The research involves patients with Parkinson's disease, stroke, and amyotrophic lateral sclerosis, both with and without dysphagia, along with healthy individuals. Participants perform various vocal tasks during a single recording session. Voice signals are analysed and used as input for machine learning classification algorithms. The significance of this study is that oropharyngeal dysphagia, a condition involving swallowing difficulties in the transit of food or liquids from the mouth to the esophagus, generates malnutrition, dehydration, and pneumonia, significantly contributing to management costs and hospitalization durations. Currently, there is a lack of rapid and effective dysphagia screening methods for healthcare personnel, with only expensive invasive tests and clinical scales in use.

Eligibility

Primary outcome measure(s)

• A classification algorithm to screen swallowing disorders in neurological patients — Baseline
  Development of a classification algorithm for dysphagia screening in neurological patients using voice analysis

Trial sites (2)

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