Tidal modulation of temperature oscillations monitored in borehole Yaxcopoil-1 (Yucatán, Mexico)

Čermák, Vladimír; Bodri, L.; Šаfanda, Jan

doi:10.1016/j.epsl.2009.03.009

Cited by 3 publications

(1 citation statement)

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“…Zbilut and Weber [17] and later Marwan et al [18] have consolidated the method as a tool in nonlinear data analysis with the application of quantification measures to analyze the RPs. Recurrence quantification analysis (RQA) can be applied to almost every kind of data such as: biological systems including electromiography (EMG) [19], intracranial electroencephalogram recordings (EEG) [20], cardiac sound evaluation [21], Earth sciences [22], Finances [23] and computer networks [24]. Recently, but still little explored, these have been applied to speech signals analysis [25], [26], [13].…”

Section: Introdutionmentioning

confidence: 99%

Pathological Voice Classification Based on Recurrence Quantification Measures

Costa¹,

Assis²,

Neto³

et al. 2012

Anais De XXX Simpósio Brasileiro De Telecomunicações

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This paper presents an analysis of speech signals based on quantification measures of recurrence plots. A comparison between healthy voices and voices affected by laryngeal pathologies (Reinke's edema, nodule and vocal fold paralysis) is made. In order to classify these signals as pathological or healthy, seven recurrence quantification measures are used: Determinism, maximum length of the diagonal structures, Shannon entropy of line distribution, slope of line of best fit, laminarity, length of longest vertical line segment and mean vertical line length or trapping time. Discriminant analysis methods (linear and quadratic) are applied to each feature individually, and to the vectors formed by feature combination with cross-validation classification rates up to 95.71±4.94% (95% confidence interval). Results show that the employed measures present a significant discriminative potential to distinguish healthy voices from pathological ones.

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