Voice signal processing for detecting possible early signs of Parkinson’s disease in patients with rapid eye movement sleep behavior disorder

Benba, Achraf; Jilbab, Abdelilah; Sandabad, Sara; Hammouch, Ahmed

doi:10.1007/s10772-018-09588-0

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…These findings suggest that recordings collected from smartphones and professional microphones could be of comparable quality. Using the speech dataset employed by Rusz et al [30], a classification of up to 66% between early PD and RBD was reported by Benba et al [31]. However, these studies on speech-RBD have mainly relied on high-quality recordings, collected in a laboratory under controlled acoustic conditions, using small cohorts (typically fewer than 50 participants).…”

Section: Introductionmentioning

confidence: 99%

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Arora

et al. 2021

IEEE Access

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Speech impairment in Parkinson's Disease (PD) has been extensively studied. Our understanding of speech in people who are at an increased risk of developing PD is, however, rather limited. It is known that isolated Rapid Eye Movement (REM) sleep Behavior Disorder (RBD) is associated with a high risk of developing PD. The aim of this study is to investigate smartphone speech testing to: (1) distinguish participants with RBD from controls and PD, and (2) predict a range of self-or researcheradministered clinical scores that quantify participants' motor symptoms, cognition, daytime sleepiness, depression, and the overall state of health. The rationale of our analyses is to test an initial hypothesis that speech can be used to detect and quantify the symptoms associated with RBD and PD. We analyzed 4242 smartphone voice recordings collected in clinic and at home from 92 Controls, 112 RBD and 335 PD participants. We used acoustic signal analysis and machine learning, employing 337 features that quantify different properties of speech impairment. Using a leave-one-subject-out cross-validation scheme, we were able to distinguish RBD from controls (sensitivity 60.7%, specificity 69.6%) and RBD from PD participants (sensitivity 74.9%, specificity 73.2%), and predict clinical assessments with clinically useful accuracy. These promising findings warrant further investigation in using speech as a digital biomarker for PD and RBD to facilitate intervention in the early and prodromal stages of PD.

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Section: Introductionmentioning

confidence: 99%

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Arora

et al. 2021

IEEE Access

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“…The second process would be POS tagging for each words to identify the corresponding government department for which the respective public opinion is corresponding. Once the parts of speech tagging is been done, we also analyze unigram, bigram and trigram analysis (Benba et al 2019 ). Example : This road is not so good for driving.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In this pandemic situation smart device based voice analysis can be used for covid detection using trained dataset model (Pramono et al 2017 ). In previous researchers, several research has been performed in detecting illness, parkinson’s disease, artery disease, stress using public voice analysis (Erdogdu-Sakar et al 2017 ; Benba et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Public opinion mining using natural language processing technique for improvisation towards smart city

Leelavathy

Nithya

2020

Int J Speech Technol

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“…Early PD patients were efficiently discriminated from controls with a BA of 89% in men and 70% in age-matched women. Recent studies using voice for early PD detection reported BA between 71% and 85% [10,13,28,29], but classification performances are difficult to compare as they did not analyze men and women separately. Previously, one study in English speakers recorded via a smartphone, separated men and women for early PD detection [30].…”

Section: Classificationmentioning

confidence: 99%

Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease

Jeancolas

Mangone

Petrovska‐Delacrétaz

et al. 2022

Parkinsonism & Related Disorders

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Background: Speech disorders are amongst the first symptoms to appear in Parkinson's disease (PD). Objectives: We aimed to characterize PD voice signature from the prodromal stage (isolated rapid eye movement sleep behavior disorder, iRBD) to early PD using an automated acoustic analysis and compare male and female patients. We carried out supervised learning classifications to automatically detect patients using voice only. Methods: Speech samples were acquired in 256 French speakers (117 participants with early PD, 41 with iRBD, and 98 healthy controls), with a professional quality microphone, a computer microphone and their own telephone. High-level features related to prosody, phonation, speech fluency and rhythm abilities were extracted. Group analyses were performed to determine the most discriminant features, as well as the impact of sex, vocal tasks, and microphone type. These speech features were used as inputs of a support vector machine and were combined with classifiers using low-level features. Results: PD related impairments were found in prosody, pause durations and rhythmic abilities, from the prodromal stage. These alterations were more pronounced in men than in women. Early PD detection was achieved with a balanced accuracy of 89% in males and 70% in females. Participants with iRBD were detected with a balanced accuracy of 63% (reaching 70% in the subgroup with mild motor symptoms). Conclusion:This study provides new insight in the characterization of sex-dependent early PD speech impairments, and demonstrates the valuable benefit of including automated voice analysis in future diagnostic procedures of prodromal PD.

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Voice signal processing for detecting possible early signs of Parkinson’s disease in patients with rapid eye movement sleep behavior disorder

Cited by 8 publications

References 19 publications

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Public opinion mining using natural language processing technique for improvisation towards smart city

Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease

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