Abstract
Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive technique which is known to produce modifications in cortical brain activity. This paper is devoted to describe potential beneficial effects of rTMS on the phonation stability of Parkinson’s Disease Patients (PDPs). To this end, several measurements derived from phonation have been studied. The stability of phonation is evaluated on sustained emisions of certain open vowels, as [a:]. Using vocal tract inversion, a correlate of the glottal source (pressure on the supraglottal rim of the vocal folds) is estimated from vowel emissions. The glottal source power spectral density is used to indirectly estimate the biomechanical tension of the vocal folds. The neuromotor instabilities experienced by PDPs, affecting the vocal fold tension are used as perturbation features related to tremor bands. A longitudinal analysis of the features from an active rTMS case can be compared in different time laps after rTMS, and tested against those from a similar study on a sham rTMS case. Relevant improvements on phonation stability may be appreciated on the active rTMS case compared to the sham one, which are reflected on several features as biomechanical tremor bands. These results open a new non-invasive, costless and remote methodology for PD functional neuromotor evaluation.
This research received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 734718 (CoBeN) and from a grant from the Czech Ministry of Health, 16-30805A, and from grants TEC2016-77791-C4-4-R (Ministry of Economic Affairs and Competitiveness of Spain), and Teca-Park-MonParLoc FGCSIC-CENIE 0348-CIE-6-E (InterReg Programme).
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Gómez-Rodellar, A., Mekyska, J., Gómez-Vilda, P., Brabenec, L., Simko, P., Rektorova, I. (2022). Evaluation of TMS Effects on the Phonation of Parkinson’s Disease Patients. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_20
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