Analysis of phonation in patients with Parkinson's disease using empirical mode decomposition

Smékal, Zdeněk; Mekyska, Jiří; Galáž, Zoltán; Mzourek, Zdenek; Rektorová, Irena; Faundez-Zanuy, Marcos

doi:10.1109/isscs.2015.7203931

Cited by 15 publications

(7 citation statements)

References 23 publications

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“…This result hits another dogma and that is the analysis of sustained vowels pronounced with normal intensity. In our recent paper we have proved that sustained vowels pronounced with minimum intensity (not whispering) accent vocal tremor and they are more complicated for precise vocal fold vibration (more than in the case of normal sustained vowels where a speaker does not have to concentrate too much on precise voicing -he couldn't whisper) [48].…”

Section: Resultsmentioning

confidence: 98%

Assessing progress of Parkinson's disease using acoustic analysis of phonation

Mekyska

Galáž

Mzourek

et al. 2015

2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)

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This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinson's disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %). Finally, we proposed a binary classification based on random forests that is able to identify Parkinson's disease with sensitivity SEN = 92.86 % (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.

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Section: Resultsmentioning

confidence: 98%

Assessing progress of Parkinson's disease using acoustic analysis of phonation

Mekyska

Galáž

Mzourek

et al. 2015

2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)

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“…Most modern machine learning-based PD detection techniques employ feature selection techniques at the pre-processing stage to select suitable features. Smekal et al (2015) suggested a Parkinson's finding approach utilizing Empirical Mode Decomposition (EMD) and Sequential Forward Feature Selection (SFFS) along with the Random Forest (RF) decision tree. EMD and SFFS have been used as feature selectors, which ultimately ascertained the optimum vowel features.…”

Section: Related Workmentioning

confidence: 99%

“…It is worth watching the models' performance because the Naranjo et al (2016) dataset is a balanced dataset having an equal number of Parkinson's (Polat and Nour 2020) 82.08 79.17 85.00 OAA + LR (Polat and Nour 2020) 77.50 80.00 75.00 RBF (Upadhya et al 2019) 83.80 97.50 70.00 SAE (LDA + all features) (Xiong and Lu 2020) 76.00 84.00 89.00 SAE (LDA + CFS) (Xiong and Lu 2020) 89.00 92.00 93.00 SAE (RF + RFE) (Xiong and Lu 2020) 81.00 84.00 86.00 SAE (LDA + MRMR) (Xiong and Lu 2020) 91.00 92.00 94.00 Support vector machine (Bourouhou Et Al. 2016) 80.00 77.00 83.00 Random forest (Smekal et al 2015) 71.43 72.60 69.40 Classification and regression trees (Mekyska et al 2015) 75 and controls subjects. The result of the proposed models with other peer models has been presented in Table 14.…”

Section: Comparing the Proposed Detection Model With Existing Recent ...mentioning

confidence: 99%

The ForEx++ based decision tree ensemble approach for robust detection of Parkinson’s disease

Pramanik

Pradhan²,

Nandy

et al. 2022

J Ambient Intell Human Comput

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The progressive reduction of dopaminergic neurons in the human brain, especially at the substantia nigra is one of the principal causes of Parkinson’s Disease (PD). Voice alteration is one of the earliest symptoms found in PD patients. Therefore, the impaired PD subjects’ acoustic voice signal plays a crucial role in detecting the presence of Parkinson's. This manuscript presents four distinct decision tree ensemble methods of PD detection on a trailblazing ForEx++ rule-based framework. The Systematically Developed Forest (SysFor) and a Penalizing Attributes Decision Forest (ForestPA) ensemble approaches has been used for PD detection. The proposed detection schemes efficiently identify positive subjects using primary voice signal features, viz., baseline, vocal fold, and time–frequency. A novel feature selection scheme termed Feature Ranking to Feature Selection (FRFS) has also been proposed to combine filter and wrapper strategies. The proposed FRFS scheme encompasses Gel’s normality test to rank and selects outstanding features from baseline, time–frequency, and vocal fold feature groups. The SysFor and ForestPA decision forests underneath the ForEx++ rule-based framework on both FRFS feature ranking and subset selection represents Parkinson’s detection approaches, which expedite a better overall impact on segregating PD from control subjects. It has been observed that the ForestPA decision forest in the ForEx++ framework on FRFS ranked features proved to be a robust Parkinson’s detection scheme. The proposed models deliver the highest accuracy of 94.12% and a lowest mean absolute error of 0.25, resulting in an Area Under Curve (AUC) value of 0.97.

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“…has been employed. Additionally, HD has been analysed using conventional, clinically interpretable speech processing techniques based on the description of: a) articulation (reduced mobility of the articulatory organs) [15], [21]- [23], b) speech prosody (reduced variability of pitch and loudness) [10]- [12], [17], c) speech fluency (speech rate and pausing abnormalities) [10], [12], [17], [22], d) speech quality (increased level of voice tremor) [13], [14], [23], [24]. Further information can be found in our recent article [5].…”

Section: Introductionmentioning

confidence: 99%

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Mucha¹,

Galáž²,

Mekyska³

et al. 2022

Preprint

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Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63 % (imprecise articulation), 61.62 % (dysprosody), 71.72 % (speech dysfluency) and 59.60 % (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42 % using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

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Analysis of phonation in patients with Parkinson's disease using empirical mode decomposition

Cited by 15 publications

References 23 publications

Assessing progress of Parkinson's disease using acoustic analysis of phonation

Assessing progress of Parkinson's disease using acoustic analysis of phonation

The ForEx++ based decision tree ensemble approach for robust detection of Parkinson’s disease

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

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