Automated gender‐Parkinson's disease detection at the same time via a hybrid deep model using human voice

Kaya, Doǧan

doi:10.1002/cpe.7289

Cited by 5 publications

(3 citation statements)

References 46 publications

(65 reference statements)

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“…For instance, 42,45 achieved 88% accuracy using the SVM and PCA-SVM methods, respectively. More recent studies, such as Ouhmida et al ( 2021) with a hybrid SVM-CNN model, achieved 98.26% accuracy, and 38 with a hybrid structure (CNN, k-NN) reported 98.9% accuracy. Although our single CNN topology does not surpass the highest reported accuracy of 99.57% by 40 using Fractional Attribute Topology (FrAT), it offers a simpler and more streamlined approach with substantial accuracy, reinforcing the viability of using a single CNN architecture for effective PD detection.…”

Section: Numerical Results and Discussionmentioning

confidence: 96%

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Figueredo,

Monteiro,

Silva

et al. 2024

Preprint

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Parkinson’s disease (PD) is a neurodegenerative disorder caused by a lack of dopamine secretion, resulting in motor dysfunction that affects speech production. The literature indicates that PD can be identified by observing changes in speech signals over time. Early detection of PD is essential to slow its progression and enable patients to access disease-modifying therapies. In this context, an adapted Convolutional Neural Network (CNN) for this task appears promising. In this paper, we propose a CNN classifier to efficiently detect PD with an accuracy above 95\%. We employed advanced machine learning techniques to identify PD early through voice analysis using the Parkinson’s Disease Voice Recording Data Set, which includes the vocal characteristics of 147 PD patients and 48 healthy individuals. The primary objective is to enhance the diagnostic accuracy of PD using a CNN tailored for this task. The preprocessing steps involved cleaning and normalizing the data, followed by the application of Bayesian hyperparameter optimization to improve model performance. The CNN model achieved an average accuracy of 96.8451\% in PD detection, demonstrating high precision, sensitivity, and a balanced F1 score. This result underscores the model’s effectiveness in identifying individuals with PD and highlights the potential of using a single CNN topology, as opposed to more complex hybrid models, to achieve satisfactory diagnostic rates.To evaluate the performance of the proposed approach, we used simulated data generated from the original dataset. The results indicate that the proposed approach is suitable for the automatic identification of PD in practical scenarios.

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Section: Numerical Results and Discussionmentioning

confidence: 96%

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Figueredo,

Monteiro,

Silva

et al. 2024

Preprint

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“…Two groups of studies [ 48 , 73 ] from UCI data sets were found to be suitable for meta-analysis due to the homogeneity between studies. The first group consisted of 15 studies using a data set containing voice recordings from 188 participants with PD and 64 HC participants [ 40 , 42 , 44 , 48 , 55 , 61 , 64 , 68 , 99 , 119 , 121 , 122 , 124 , 126 , 127 ]. The second group consisted of 5 studies [ 53 , 57 , 60 , 71 , 100 ] using voice recordings from 20 participants with PD and 20 HC participants ( Table 4 ).…”

Section: Resultsmentioning

confidence: 99%

“…A total of 70 studies collected data for a specific study and 75 studies gathered data from an available data set. Sakar et al (2013) [73] and Sakar et al (2019) [48] are 2 different data sets donated to the UCI (University of California, Irvine), which have been used in 15 different included studies in this SLR; 5 studies [53,57,60,71,100] used the UCI data set containing 20 participants with PD and 20 HC participants, and 15 studies [40,42,44,48,55,61,64,68,99,119,121,122,124,126,127] used the UCI data set having 188 participants with PD and 64 HC participants from the same source. UCI and Coswara provide data sets that can be accessed and downloaded without any additional application [73,189].…”

Section: Data Characteristicsmentioning

confidence: 99%

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

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Background Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

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Temperature interpretation method for temperature indicating paint based on spectrogram

Wang

Gui

et al. 2023

Measurement

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Automated gender‐Parkinson's disease detection at the same time via a hybrid deep model using human voice

Cited by 5 publications

References 46 publications

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Temperature interpretation method for temperature indicating paint based on spectrogram

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