Data di Pubblicazione:
2019
Abstract:
Background and objective
Neurodegenerative diseases are disorders that affect neurons in the brain resulting in a debilitating condition and progressive degeneration of nerve cells. These diseases involve different aspects among which speech impairment. Vocal signal analysis is used to evaluate this impairment and to discriminate normal from pathological voices.
Materials and methods
In this paper, two methods of vocal signal analysis have been proposed to evaluate an anomalous condition in human speech, known as dysarthria, useful to compare pathological and healthy voices. Parkinson and Multiple Sclerosis disease have been considered and patients affected by both pathologies have been enrolled. The methods have been tested on 153 voice signals belonging to: 39 healthy subjects (HS), 60 patients with Parkinson's Disease (PD) and 54 patients with Multiple Sclerosis (MS). Acoustic (F0, jitter, shimmer, NHR) and vowel metric (tVSA, qVSA, FCR) features have been extracted.
Results
The results report significant differences in almost all of these features in pathological and healthy voices by performing statistical tests. F0, jitter, shimmer, NHR, tVSA and FCR are statistically significant features thus they can be used as indicators in the diagnosis of dysarthria-related diseases such as in PD and MS. The results suggest that the applied methodologies are efficient and useful in characterizing the different behavior of vocal signal in healthy and pathological subjects. Consequently, they could be a valid support for physicians in disease evaluation and progression monitoring.
Conclusions
The contribution aims to evaluate, support and diagnose the comorbidity in pathological patients verifying the co-occurrence of speech and neurological disorders in the same individual. The proposed solution is studied and implemented to be efficient and low cost following the model of precision medicine to customize clinical practice in disease diagnosis and treatment.
Neurodegenerative diseases are disorders that affect neurons in the brain resulting in a debilitating condition and progressive degeneration of nerve cells. These diseases involve different aspects among which speech impairment. Vocal signal analysis is used to evaluate this impairment and to discriminate normal from pathological voices.
Materials and methods
In this paper, two methods of vocal signal analysis have been proposed to evaluate an anomalous condition in human speech, known as dysarthria, useful to compare pathological and healthy voices. Parkinson and Multiple Sclerosis disease have been considered and patients affected by both pathologies have been enrolled. The methods have been tested on 153 voice signals belonging to: 39 healthy subjects (HS), 60 patients with Parkinson's Disease (PD) and 54 patients with Multiple Sclerosis (MS). Acoustic (F0, jitter, shimmer, NHR) and vowel metric (tVSA, qVSA, FCR) features have been extracted.
Results
The results report significant differences in almost all of these features in pathological and healthy voices by performing statistical tests. F0, jitter, shimmer, NHR, tVSA and FCR are statistically significant features thus they can be used as indicators in the diagnosis of dysarthria-related diseases such as in PD and MS. The results suggest that the applied methodologies are efficient and useful in characterizing the different behavior of vocal signal in healthy and pathological subjects. Consequently, they could be a valid support for physicians in disease evaluation and progression monitoring.
Conclusions
The contribution aims to evaluate, support and diagnose the comorbidity in pathological patients verifying the co-occurrence of speech and neurological disorders in the same individual. The proposed solution is studied and implemented to be efficient and low cost following the model of precision medicine to customize clinical practice in disease diagnosis and treatment.
Tipologia CRIS:
1.1 Articolo in rivista
Elenco autori:
Vizza, P.; Tradigo, G.; Mirarchi, D.; Bossio, R. B.; Lombardo, N.; Arabia, G.; Quattrone, A.; Veltri, P.
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