Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

Abstract

In this paper, we have investigated the differences in the voices of Parkinson’s disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD–CO, PD–PD and CO–CO. Four features reported in the literature—normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)—were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO–PD and PD–PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD–CO and PD–PD is higher compared with CO–CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach

“…For subgroup analyses, as in previous research, 46 16 patients with functional independence and MoCA scores below the region‐specific cutoff 75 were classified as PD‐MCI, while 24 with preserved functional skills and normal MoCA scores were grouped as PD‐nMCI (Supporting Information Section 3). These sample sizes are similar to or larger than those of previous ML studies 13,22,57,59 . Each PD subgroup was matched to its own HC sample 44‐46 .…”

“…We recruited 80 Spanish‐speaking participants (40 early‐stage PD patients with heterogeneous cognitive profiles and 40 HCs) (Table 1). This sample size meets the field's standards 13,22,23,57‐59 and equals that of dysarthria research guidelines 60 . Diagnoses were established by an expert neurologist following UK PD Society Brain Bank criteria 61 .…”

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

“…The recording was performed in a noise-restricted room. The de-identified data is available on RMIT website and has been reported earlier [25] .…”

“…Low HNR is an indicator of the existence of dysarthria. Studies have reported the use of other features such as the fractal dimension (FD) [25] linear predictive model (LPM) [13] , multivariate deep features [21] , and entropy [25] . Many of these studies have shown these features are very effective in differentiating between the voices of PD and healthy people.…”

Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease

“…The analysis, including 5-s recordings of the vowel "a" and the syllable "pa", as well as text with different emotional shades in patients with Parkinson's disease and healthy people, has been described in [19]. Studies of the correlation between individual sound parameters of the voice and the severity of the condition of PD patients have also been carried out [20][21][22].…”