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.
Highlights
Neurodegenerative diseases concern the central nervous system and are characterized by chronic and selective neuron cell death
PRAAT was used to perform the acoustic analysis on vocal signals for both healthy subjects (HS) and patients affected by secondary progressive MS (SPMS) and by relapsing–remitting MS (RRMS)
An algorithm has been developed to (i) load F1 and F2 values extracted by PRAAT software; (ii) calculate triangular VSA (tVSA), quadrilateral VSA (qVSA) and formant centralization ratio (FCR) according to Equations (1)–(3) reported in Section 2; and (iii) map
Summary
Neurodegenerative diseases concern the central nervous system and are characterized by chronic and selective neuron cell death. Neuronal deterioration may result in cognitive deficits, dementia, motor changes, and behavioral and psychological disorders. The neurodegenerative diseases are conditions primarily affecting neurons and causing the disruption of information flow within the brain and between the brain and rest of the body [1]. Multiple sclerosis (MS) is one of the most common neurodegenerative disorders. It is a chronic demyelinating disease that affects the central nervous system, interfering with nerve impulses within the brain, the spinal cord and the optic nerves [2]. The evolution in time of MS is different for each patient, four main types of MS can be identified: (i) clinically isolated syndrome (CIS); (ii) primary progressive MS (PPMS);
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