Methodologies of speech analysis for neurodegenerative diseases evaluation. (February 2019)

• Record Type: Journal Article
• Authors: Vizza, Patrizia; Tradigo, Giuseppe; Mirarchi, Domenico; Bossio, Roberto Bruno; Lombardo, Nicola; Arabia, Gennarina; Quattrone, Aldo; Veltri, Pierangelo
• Is Part Of: International journal of medical informatics. Volume 122(2019)
• Page Start: 45
• Page End: 54
• Publication Date: 2019-02
• Subjects: Vocal signal analysis -- Neurodegenerative disease -- Vowel metric -- Statistical analysis; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.11.008
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Identify signal voice anomalies in dysarthria condition. Acoustic analysis of vocal signal is useful to extract relevant parameters aiming to evaluate voice and neurological disorders. The vowels metric is a relevant method for measuring vowel articulation impairment in patients with dysarthria associated to neurological diseases. The integration of these two methodologies allows to characterize speech behavior in healthy and pathological subjects (MS e PD patients). 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 ( F 0, 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. F 0, 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.