Performance of Articulation Kinetic Distributions Vs MFCCs in Parkinson’s Detection from Vowel Utterances

Gómez-Rodellar, Andrés; Álvarez-Marquina, Agustín; Mekyska, Jiří; Palacios-Alonso, Daniel; Meghraoui, Djamila; Gómez-Vilda, Pedro

doi:10.1007/978-981-13-8950-4_38

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The novel methods that have emerged in recent years include audio analysis [16], video analysis [17], wearable sensor analysis [18]. There also some works that are focused on multimodal analysis [19].…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Skibińska

Bürget

2020

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

View full text Add to dashboard Cite

Parkinson's disease (PD) is the neurodegenerative disease which affects 2-3 % of the population beyond 65 years of age in EU. When PD treatment is administered early, it is significantly more effective. Unfortunately, it is quite challenging to detect this disease at its early stage and when the symptoms can be recognized it is usually quite late. For this reason there is big motivation for development more accessible and accurate solutions for the detection of PD. One of the early symptoms is so-called hypomimia. This paper introduces an automatic method, which can objectively detect PD. The method is based on analysis of emotion changes during pronunciation defined speech exercises. We achieved balanced accuracy 69 % using XGBoost algorithm. As the exercise we proposed to use a Czech tongue twister-the difficult to pronounce sentence. The features can be explained and thus it can be used in clinical practice. We identified that the most valuable emotion for PD detection in this case is fear. 124 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

show abstract

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Skibińska

Bürget

2020

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

View full text Add to dashboard Cite

show abstract

Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Parkinson’s Disease Detection

Goyal

Rani

Singh

2022

2022 3rd International Conference on Computing, Analytics and Networks (ICAN)

View full text Add to dashboard Cite

Machine Learning Methods with Decision Forests for Parkinson’s Detection

et al. 2021

View full text Add to dashboard Cite

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson’s detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson’s Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson’s and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%.

show abstract

Performance of Articulation Kinetic Distributions Vs MFCCs in Parkinson’s Detection from Vowel Utterances

Cited by 3 publications

References 14 publications

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Parkinson’s Disease Detection

Machine Learning Methods with Decision Forests for Parkinson’s Detection

Contact Info

Product

Resources

About