Phonemes based detection of parkinson’s disease for telehealth applications

Pah, Nemuel Daniel; Motin, Mohammod Abdul; Kumar, Dinesh

doi:10.1038/s41598-022-13865-z

Cited by 17 publications

(14 citation statements)

References 43 publications

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“…Vowel: 59 [ 17 , 40 , 42 , 44 , 48 , 50 , 54 – 59 , 61 – 64 , 68 , 69 , 74 – 85 , 87 – 91 , 93 – 97 , 99 , 100 , 102 , 119 – 122 , 124 , 126 – 128 , 130 – 132 , 135 , 170 , 183 , 184 , 192 ]…”

Section: Resultsunclassified

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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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Section: Resultsunclassified

“…BLA a : 26 [ 17 , 40 , 50 , 53 , 56 , 58 - 60 , 64 - 66 , 71 , 73 , 77 , 79 , 82 , 84 , 87 , 88 , 92 , 98 , 118 , 132 , 133 , 184 , 188 ]…”

Section: Resultsunclassified

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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“…For all three selection algorithms, the most prominent features are F1, F2, DDF, BBE and MFCC, which are all from articulation features. F1, F2, DDF1 and DDF2 can represent resonances in the vocal tract ( Pah et al, 2022 ) and the capability of the speaker to hold the tongue in a certain position ( Ladefoged and Harshman, 1979 ). BBE and MFCC are common dynamic signals.…”

Section: Discussionmentioning

confidence: 99%

Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset

Wang

Yan²,

Shao³

et al. 2022

Front. Aging Neurosci.

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Parkinson’s disease (PD) is a neurodegenerative disorder that negatively affects millions of people. Early detection is of vital importance. As recent researches showed dysarthria level provides good indicators to the computer-assisted diagnosis and remote monitoring of patients at the early stages. It is the goal of this study to develop an automatic detection method based on newest collected Chinese dataset. Unlike English, no agreement was reached on the main features indicating language disorders due to vocal organ dysfunction. Thus, one of our approaches is to classify the speech phonation and articulation with a machine learning-based feature selection model. Based on a relatively big sample, three feature selection algorithms (LASSO, mRMR, Relief-F) were tested to select the vocal features extracted from speech signals collected in a controlled setting, followed by four classifiers (Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Stochastic Gradient Descent) to detect the disorder. The proposed approach shows an accuracy of 75.76%, sensitivity of 82.44%, specificity of 73.15% and precision of 76.57%, indicating the feasibility and promising future for an automatic and unobtrusive detection on Chinese PD. The comparison among the three selection algorithms reveals that LASSO selector has the best performance regardless types of vocal features. The best detection accuracy is obtained by SGD classifier, while the best resulting sensitivity is obtained by LR classifier. More interestingly, articulation features are more representative and indicative than phonation features among all the selection and classifying algorithms. The most prominent articulation features are F1, F2, DDF1, DDF2, BBE and MFCC.

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“…Voice features extracted from sustained vowels have been evaluated as potential parameters for both the diagnosis and monitoring of PD in several studies [8]- [15]. These features cover various aspects, including issues related to glottal vibration, the harmonics-to-noise ratio (HNR), the control of glottal pressure through the respiratory mechanism, and vocal tract control [16]. The effectiveness of these voice features has been assessed through a range of statistical analyses, including calculations of mean values, standard deviations, effect sizes, and p-values derived from statistical tests such as t-tests and the analysis of variance (ANOVA).…”

Section: Introductionmentioning

confidence: 99%

“…Many classification models have been developed to diagnose PD [17]. Pah et al [16], [18] and Motin [19] developed support vector machine (SVM) models to identify people with PD and distinguish the effect of levodopa in PD patients. Ali [20] developed an intelligent system that uses linear discriminant analysis (LDA) for dimensionality reduction and a genetic algorithm (GA) to detect people with PD.…”

Section: Introductionmentioning

confidence: 99%

Voice-Based SVM Model Reliability for Identifying Parkinson’s Disease

Pah,

Indrawati,

Kumar

2023

IEEE Access

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One of the possibilities for developing non-invasive computerized diagnostic tools for Parkinson's disease (PD) is to detect changes in the voice, known as Parkinsonian dysarthria. Numerous classification models have been developed to diagnose PD based on voice features. However, the performance of models developed and trained only using voice features extracted from people with PD and healthy people might be affected when tested on individuals with other voice-related pathological conditions. Therefore, we investigated the reliability of voice-based machine-learning models that were developed only using datasets of people with PD and healthy people for accurately identifying people without PD when they have other voice-related pathological conditions (i.e. dysphonia and laryngitis). Three different support vector machines (SVMs) were developed and tested on voice features extracted from healthy people and those with PD, dysphonia, and laryngitis. The results confirmed that a voice-based SVM classifier only trained on the dataset of people with PD and healthy people was equally reliable in classifying other voice-related pathological conditions, such as dysphonia and laryngitis, as non-PD cases.

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Phonemes based detection of parkinson’s disease for telehealth applications

Cited by 17 publications

References 43 publications

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

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

Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset

Voice-Based SVM Model Reliability for Identifying Parkinson’s Disease

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