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Computers 2017, 6(4), 30; doi:10.3390/computers6040030

On the Use of Voice Signals for Studying Sclerosis Disease

1
Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
2
Department of Computer Engineering, Modelling, Electronics and Systems (DIMES), University of Calabria, 87036 Rende, Italy
3
Neurological Operative Unit, Center of Multiple Sclerosis, Provincial Health Authority of Cosenza, 87100 Cosenza, Italy
*
Authors to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 13 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient’s life quality. View Full-Text
Keywords: multiple sclerosis; vocal signal analysis; vowel metric; acoustic analysis multiple sclerosis; vocal signal analysis; vowel metric; acoustic analysis
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Vizza, P.; Tradigo, G.; Mirarchi, D.; Bossio, R.B.; Veltri, P. On the Use of Voice Signals for Studying Sclerosis Disease. Computers 2017, 6, 30.

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