Vocal signal analysis in patients affected by Multiple Sclerosis

Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient’s quality of life.