Detecting Parkinson's disease from sustained phonation and speech signals.
Ontology highlight
ABSTRACT:
SUBMITTER: Vaiciukynas E
PROVIDER: S-EPMC5628839 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo elucidate the role of phonation frequency (i.e., pitch) and intensity of speech on respiratory aerosol emissions during sustained phonations.MethodsRespiratory aerosol emissions are measured in 40 (24 males and 16 females) healthy, non-trained singers phonating the phoneme /a/ at seven specific frequencies at varying vocal intensity levels.ResultsIncreasing frequency of phonation was positively correlated with particle production (r = 0.28, p < 0.001). Particle production rate was also positively correlated (r = 0.37, p < 0.001) with the vocal intensity of phonation, confirming previously reported findings. The primary mode (particle diameter ~0.6 μm) and width of the particle number size distribution were independent of frequency and vocal intensity. Regression models of the particle production rate using frequency, vocal intensity, and the individual subject as predictor variables only produced goodness of fit of adjusted R2 = 40% (p < 0.001). Finally, it is proposed that superemitters be defined as statistical outliers, which resulted in the identification of one superemitter in the sample of 40 participants.SignificanceThe results suggest there remain unexplored effects (e.g., biomechanical, environmental, behavioral, etc.) that contribute to the high variability in respiratory particle production rates, which ranged from 0.2 particles/s to 142 particles/s across all trials. This is evidenced as well by changes in the distribution of participant particle production that transitions to a more bimodal distribution (second mode at particle diameter ~2 μm) at higher frequencies and vocal intensity levels.
| S-EPMC8963400 | biostudies-literature
Project description:BackgroundAs many as 89% of people with Parkinson's disease (PD) develop speech disorders.ObjectivesThis randomized controlled trial evaluated two speech treatments for PD matched in intensive dosage and high-effort mode of delivery, differing in subsystem target: voice (respiratory-laryngeal) versus articulation (orofacial-articulatory).MethodsPD participants were randomized to 1-month LSVT LOUD (voice), LSVT ARTIC (articulation), or UNTXPD (untreated) groups. Speech clinicians specializing in PD delivered treatment. Primary outcome was sound pressure level (SPL) in reading and spontaneous speech, and secondary outcome was participant-reported Modified Communication Effectiveness Index (CETI-M), evaluated at baseline, 1, and 7 months. Healthy controls were matched by age and sex.ResultsAt baseline, the combined PD group (n = 64) was significantly worse than healthy controls (n = 20) for SPL (P < 0.05) and CETI-M (P = 0.0001). At 1 and 7 months, SPL between-group comparisons showed greater improvements for LSVT LOUD (n = 22) than LSVT ARTIC (n = 20; P < 0.05) and UNTXPD (n = 22; P < 0.05). Sound pressure level differences between LSVT ARTIC and UNTXPD at 1 and 7 months were not significant (P > 0.05). For CETI-M, between-group comparisons showed greater improvements for LSVT LOUD and LSVT ARTIC than UNTXPD at 1 month (P = 0.02; P = 0.02). At 7 months, CETI-M between-group differences were not significant (P = 0.08). Within-group CETI-M improvements for LSVT LOUD were maintained through 7 months (P = 0.0011).ConclusionsLSVT LOUD showed greater improvements than both LSVT ARTIC and UNTXPD for SPL at 1 and 7 months. For CETI-M, both LSVT LOUD and LSVT ARTIC improved at 1 month relative to UNTXPD. Only LSVT LOUD maintained CETI-M improvements at 7 months. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
| S-EPMC6261685 | biostudies-literature
Project description:Here we report a clinical phenomenon that we have observed repeatedly in clinical research settings; namely, a triggering and/or exacerbation of head tremor during or immediately following sustained phonation. To our knowledge, it has not been reported previously nor has it been the subject of study. Here we: 1) report the phenomenon, 2) provide several visual illustrations, 3) estimate its prevalence, and 4) analyze its clinical correlates. Head tremor was assessed qualitatively, and scores were assigned pre-, during, and post-sustained phonation using the Tremor Research Group Essential Tremor Rating Assessment Scale. Seventy (68.6%) of 102 essential tremor (ET) patients exhibited a qualitative increase in head tremor amplitude during and/or immediately after sustained phonation; in 5 (4.9%), head tremor would not have been detected without the voice activation maneuver (i.e., it was not visible at any other point in the videotape aside from the period during/immediately following sustained phonation). Women were more likely than men to exhibit this phenomenon (p = 0.05), whereas age, age at onset, duration of tremor, and total tremor score did not predict responsiveness of head tremor to sustained phonation. Sustained voice activation is a useful examination maneuver that may elicit or amplify head tremor in ET. Head tremor is not reported to occur in patients with enhanced physiological tremor. Thus, this maneuver, by triggering head tremor, may be a useful diagnostic supplement, particularly in research/clinical settings where arm tremor is mild and the diagnosis (mild ET vs. enhanced physiological tremor) would otherwise be ambiguous.
| S-EPMC3946668 | biostudies-literature
Project description:BackgroundAccess to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD.ObjectiveIn this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD.MethodsWe collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output.ResultsWe achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most.ConclusionsOur model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.
| S-EPMC8564663 | biostudies-literature
Project description:ObjectiveTo analyze the frequency of speech disruptions across different speech tasks, comparing the performance of individuals with Parkinson's Disease (PD) and DS.MethodParticipants were 20 people with PD, 20 people with DS and 40 fluent individuals. Speech samples were recorded during monologue speech, choral and solo oral reading. Transcriptions of 200 fluent syllables were performed to identify stuttering-like disfluencies (SLDs) and other disfluencies (ODs).ResultsPeople with PD presented significantly less speech disruptions when compared to people with DS, but significantly more speech disruptions than the control group. Stuttering-like disfluencies ocurred more frequently during monologue speech and solo oral reading for both PD and DS, whereas the control group did not present difference between these tasks.ConclusionThe stuttering pattern presented by people with PD is different from what is usually described as being neurogenic stuttering.
| S-EPMC6005540 | biostudies-literature
Project description:Difficulty producing intelligible speech is a debilitating symptom of Parkinson's disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. Using task-free magnetoencephalography, we examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N = 59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N = 65), we show that articulation impairments in patients with PD are associated with aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.
| S-EPMC10104849 | biostudies-literature
Project description:BackgroundVoice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application.ObjectiveThis study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios.MethodsWe designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system.ResultsThe proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions.ConclusionsThis study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.
| S-EPMC7746501 | biostudies-literature
Project description:BackgroundParkinson's disease (PD) is the most prevalent neurodegenerative movement disorder and a growing health concern in demographically aging societies. The prevalence of PD among individuals over the age of 60 and 80 years has been reported to range between 1% and 4%. A timely diagnosis of PD is desirable, even though it poses challenges to medical systems.ObjectiveThis study aimed to classify PD and healthy controls based on the analysis of voice records at different frequencies using machine learning (ML) algorithms.MethodsThe voices of 252 individuals aged 33 to 87 years were recorded. Based on the voice record data, ML algorithms can distinguish PD patients and healthy controls. One binary decision variable was associated with 756 instances and 754 attributes. Voice records data were analyzed through supervised ML algorithms and pipelines. A 10-fold cross-validation method was used to validate models.ResultsIn the classification of PD patients, ML models were performed with 84.21 accuracy, 93 precision, 89 Sensitivity, 89 F1-scores, and 87 AUC. The pipeline performance improved to accuracy: 85.09, precision: 92, Sensitivity:91, F1-score: 89, and AUC: 90. The Pipeline methods improved the performance of classifying PD from voice record.ConclusionsOur study demonstrated that ML classifiers and pipelines can classify PD patients based on speech biomarkers. It was found that pipelines were more effective at selecting the most relevant features from high-dimensional data and at accurately classifying PD patients and healthy controls. This approach can therefore be used for early diagnosis of initial forms of PD.
| S-EPMC10836572 | biostudies-literature
Project description:BackgroundIt has been suggested that sensory impairments contribute significantly to the motor deficits secondary to impaired sensorimotor integration in Parkinson's disease. Speech and swallowing are likely to become disordered in PD, and there is evidence that impaired upper airway sensation also contributes to these disorders.ObjectivesThe goal of this study was to investigate the relationship between perception of general respiratory sensation, speech, and swallowing in PD.MethodsThirteen people with PD and 14 age-equivalent controls volunteered to participate. Randomized blocks of inspiratory resistive loads were delivered, and participants gauged the magnitude of the loads using a modified Borg scale. The magnitude estimates were then compared to results of speech and swallowing evaluations using multivariate analysis of variance and a stepwise linear regression model.ResultsThere was a significant overall interaction between the participant group (PD versus control) and respiratory load (F [10, 300] = 2.138; P = .022). A significant regression equation containing a predictor speech variable respiratory rating was found (F [1,22] = 6.946), P = .023), with a moderate effect size of R2 = .387.ConclusionsPeople with PD have blunted perception of respiratory resistive loads when compared with age-equivalent healthy adults. Results also suggest that blunted ME of resistive loads could contribute to changes in respiratory drive for speech (i.e., loudness).
| S-EPMC6417761 | biostudies-literature
Project description:Deep brain stimulation (DBS) has been reported to be successful in relieving the core motor symptoms of Parkinson's disease (PD) and motor fluctuations in the more advanced stages of the disease. However, data on the effects of DBS on speech performance are inconsistent. While there are some series of patients documenting that speech function was relatively unaffected by DBS of the nucleus subthalamicus (STN), other investigators reported on improvements of distinct parameters of oral control and voice. Though, these ameliorations of single speech modalities were not always accompanied by an improvement of overall speech intelligibility. On the other hand, there are also indications for an induction of dysarthria as an adverse effect of STN-DBS occurring at least in some patients with PD. Since a deterioration of speech function has more often been observed under high stimulation amplitudes, this phenomenon has been ascribed to a spread of current-to-adjacent pathways which might also be the reason for the sporadic observation of an onset of dysarthria under DBS of other basal ganglia targets (e.g., globus pallidus internus/GPi or thalamus/Vim). The aim of this paper is to review and evaluate reports in the literature on the effects of DBS on speech function in PD.
| S-EPMC3512320 | biostudies-other