On the Use of Voice Signals for Studying Sclerosis Disease
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/6/4/30 |
| _version_ | 1798005222998540288 |
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| author | Patrizia Vizza Giuseppe Tradigo Domenico Mirarchi Roberto Bruno Bossio Pierangelo Veltri |
| author_facet | Patrizia Vizza Giuseppe Tradigo Domenico Mirarchi Roberto Bruno Bossio Pierangelo Veltri |
| author_sort | Patrizia Vizza |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:35:41Z |
| format | Article |
| id | doaj.art-8fc48db7921d4352b0be85e6970ecfde |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-11T12:35:41Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-8fc48db7921d4352b0be85e6970ecfde2022-12-22T04:23:37ZengMDPI AGComputers2073-431X2017-11-01643010.3390/computers6040030computers6040030On the Use of Voice Signals for Studying Sclerosis DiseasePatrizia Vizza0Giuseppe Tradigo1Domenico Mirarchi2Roberto Bruno Bossio3Pierangelo Veltri4Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, ItalyDepartment of Computer Engineering, Modelling, Electronics and Systems (DIMES), University of Calabria, 87036 Rende, ItalyDepartment of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, ItalyNeurological Operative Unit, Center of Multiple Sclerosis, Provincial Health Authority of Cosenza, 87100 Cosenza, ItalyDepartment of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, ItalyMultiple 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.https://www.mdpi.com/2073-431X/6/4/30multiple sclerosisvocal signal analysisvowel metricacoustic analysis |
| spellingShingle | Patrizia Vizza Giuseppe Tradigo Domenico Mirarchi Roberto Bruno Bossio Pierangelo Veltri On the Use of Voice Signals for Studying Sclerosis Disease Computers multiple sclerosis vocal signal analysis vowel metric acoustic analysis |
| title | On the Use of Voice Signals for Studying Sclerosis Disease |
| title_full | On the Use of Voice Signals for Studying Sclerosis Disease |
| title_fullStr | On the Use of Voice Signals for Studying Sclerosis Disease |
| title_full_unstemmed | On the Use of Voice Signals for Studying Sclerosis Disease |
| title_short | On the Use of Voice Signals for Studying Sclerosis Disease |
| title_sort | on the use of voice signals for studying sclerosis disease |
| topic | multiple sclerosis vocal signal analysis vowel metric acoustic analysis |
| url | https://www.mdpi.com/2073-431X/6/4/30 |
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