Voice changes in Parkinson's disease: What are they telling us?

Voice as a Biomarker in Health-Tech: Mapping the Evolving Landscape of Voice Biomarkers in the Start-Up World

OBJECTIVE

The vocal biomarkers market was worth $1.9B in 2021 and is projected to exceed $5.1B by 2028, for a compound annual growth rate of 15.15%. The investment growth demonstrates a blossoming interest in voice and artificial intelligence (AI) as it relates to human health. The objective of this study was to map the current landscape of start-ups utilizing voice as a biomarker in health-tech.

DATA SOURCES

A comprehensive search for start-ups was conducted using Google, LinkedIn, Twitter, and Facebook. A review of the research was performed using company website, PubMed, and Google Scholar.

REVIEW METHODS

A 3-pronged approach was taken to thoroughly map the landscape. First, an internet search was conducted to identify current start-ups focusing on products relating to voice as a biomarker of health. Second, Crunchbase was utilized to collect financial and organizational information. Third, a review of the literature was conducted to analyze publications associated with the identified start-ups.

RESULTS

A total of 27 start-up start-ups with a focus in the utilization of AI for developing biomarkers of health from the human voice were identified. Twenty-four of these start-ups garnered $178,808,039 in investments. The 27 start-ups published 194 publications combined, 128 (66%) of which were peer reviewed.

CONCLUSION

There is growing enthusiasm surrounding voice as a biomarker in health-tech. Academic drive may complement commercialization to best achieve progress in this arena. More research is needed to accurately capture the entirety of the field, including larger industry players, academic institutions, and non-English content.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Perceptual consequences of online group speech treatment for individuals with Parkinson's disease: A pilot study case series

PURPOSE

We examined perceptual changes in the domains of ease of understanding, naturalness, and speech severity, as well as changes in self-perceptions of voice disability, following an online group speech treatment program for people with Parkinson's disease (PD) conducted during the COVID-19 pandemic.

METHOD

Seven speakers with hypokinetic dysarthria associated with PD participated in a university and community-based online group speech program for 10 weeks. Speech recordings occurred remotely 1 week before and 1 week after the online program. Thirty naïve listeners rated ease of understanding, naturalness, and speech severity based on the speech recordings. Speakers' self-perceptions of voice disability were also obtained at both time points.

RESULT

Individual analysis of the speech data showed that for most speakers with dysarthria, ease of understanding and perceptions of severity were rated the same or better pre- to post-treatment. Naturalness, however, was only perceived to be the same or better post-treatment in three out of seven speakers. Over half of the speakers reported improvements in their self-perception of voice disability.

CONCLUSION

This pilot study highlighted the individual variability among speakers with dysarthria and the potential of online group speech treatment to maintain and/or improve speech function in this population.

Exploring the impact of type II diabetes mellitus on voice quality

PURPOSE

This cross-sectional study aimed to investigate the potential of voice analysis as a prescreening tool for type II diabetes mellitus (T2DM) by examining the differences in voice recordings between non-diabetic and T2DM participants.

METHODS

60 participants diagnosed as non-diabetic (n = 30) or T2DM (n = 30) were recruited on the basis of specific inclusion and exclusion criteria in Iran between February 2020 and September 2023. Participants were matched according to their year of birth and then placed into six age categories. Using the WhatsApp application, participants recorded the translated versions of speech elicitation tasks. Seven acoustic features [fundamental frequency, jitter, shimmer, harmonic-to-noise ratio (HNR), cepstral peak prominence (CPP), voice onset time (VOT), and formant (F1-F2)] were extracted from each recording and analyzed using Praat software. Data was analyzed with Kolmogorov-Smirnov, two-way ANOVA, post hoc Tukey, binary logistic regression, and student t tests.

RESULTS

The comparison between groups showed significant differences in fundamental frequency, jitter, shimmer, CPP, and HNR (p < 0.05), while there were no significant differences in formant and VOT (p > 0.05). Binary logistic regression showed that shimmer was the most significant predictor of the disease group. There was also a significant difference between diabetes status and age, in the case of CPP.

CONCLUSIONS

Participants with type II diabetes exhibited significant vocal variations compared to non-diabetic controls.

Exploring the Role of Opera Voice Quality Exercise in the Voice Therapy

OBJECTIVE

There are very diverse approaches for voice therapy, and the application of voice quality used in vocal arts in voice therapy can also be seen. However, there is little research on the application of opera voice quality in voice therapy. This study explored the applications of our Opera Voice Quality Exercise in the field of voice therapy and investigated the impacts of this exercise on pitch, intensity, voice quality, and vocal ability.

METHODS

Sixty-two healthy subjects, defined as those with no discomfort in their voice and no appearance of organic lesions on the larynx via stroboscopic laryngoscopy were included in the study. The subjects were randomly divided into an experimental group of 31 subjects and a control group of 31 subjects. The experimental group received a voice health education and weekly coaching sessions of Opera Voice Quality Exercise, whereas the subjects in the control group only had the former. The acoustic and aerodynamic parameters were evaluated before and after the experimental interventions.

RESULTS

When producing [a] at comfortable speech pitch and intensity, the experimental group compared to the control group showed statistically significant improvement (P < 0.05) in the irregularity component (IC) parameter for males. When producing [a] at loudest intensity at a higher pitch in the normal speech pitch range, the experimental group compared to the control group showed statistically significant increase (P < 0.01) in sound pressure level (SPL) as well as improvements (P < 0.05) in shimmer and IC parameters for males. There was a statistically significant increase (P < 0.05) in SPL for females. During continuous speech, the experimental group compared to the control group showed statistically significant increase (P < 0.01) in SPLmax (maximum sound pressure level) for both males and females. There was a statistically significant increase in highest pitch (P < 0.01) and lowest pitch (P < 0.05) for males.

CONCLUSION

Regardless of gender, there is the greatest impact of Opera Voice Quality Exercise on phonation intensity. Furthermore, for males, this exercise causes the voice quality to be improved and the speech pitch to raise. Therefore, there may be applications of Opera Voice Quality Exercise in voice problems with weak voice such as nonorganic hypofunctional dysphonia, vocal fold paresis and paralysis, and voice problems related to Parkinson and age.

Oral-diadochokinesis between Parkinson's disease and neurotypical elderly among Malaysian-Malay speakers

BACKGROUND

Parkinson's disease (PD) has an impact on speech production, manifesting in various ways including alterations in voice quality, challenges in articulating sounds and a decrease in speech rate. Numerous investigations have been conducted to ascertain the oral-diadochokinesis (O-DDK) rate in individuals with PD. However, the existing literature lacks exploration of such O-DDK rates in Malaysia and does not provide consistent evidence regarding the advantage of real-word repetition.

AIMS

To explore the effect of gender, stimuli type and PD status and their interactions on the O-DDK rates among Malaysian-Malay speakers.

METHODS & PROCEDURES

O-DDK performance of 62 participants (29 individuals with PD and 33 healthy elderly) using a non-word ('pataka'), a Malay real-word ('patahkan') and an English real-word ('buttercake') was audio recorded. The number of syllables produced in 8 s was counted. A hierarchical linear modelling was performed to investigate the effects of stimuli type (non-word, Malay real-word, English real-word), PD status (yes, no), gender (male, female) and their interactions on the O-DDK rate. The model accounted for participants' age as well as the nesting of repeated measurements within participants, thereby providing unbiased estimates of the effects.

OUTCOMES & RESULTS

The stimuli effect was significant (p < 0.0001). Malay real-word showed the lowest O-DDK rate (5.03 ± 0.11 syllables/s), followed by English real-word (5.25 ± 0.11 syllables/s) and non-word (5.42 ± 0.11 syllables/s). Individuals with PD showed a significantly lower O-DDK rate compared to healthy elderly (4.73 ± 0.15 syllables/s vs. 5.74 ± 0.14 syllables/s, adjusted p < 0.001). A subsequent analysis indicated that the O-DDK rate declined in a quadratic pattern. However, neither gender nor age effects were observed. Additionally, no significant two-way interactions were found between stimuli type, PD status and gender (all p > 0.05). Therefore, the choice of stimuli type has no or only limited effect considering the use of O-DDK tests in clinical practice for diagnostic purposes.

CONCLUSIONS & IMPLICATIONS

The observed slowness in O-DDK among individuals with PD can be attributed to the impact of the movement disorder, specifically bradykinesia, on the physiological aspects of speech production. Speech-language pathologists can gain insights into the impact of PD on speech production and tailor appropriate intervention strategies to address the specific needs of individuals with PD according to disease stages.

WHAT THIS PAPER ADDS

What is already known on this subject The observed slowness in O-DDK rates among individuals with PD may stem from the movement disorder's effects on the physiological aspects of speech production, particularly bradykinesia. However, there is a lack of consistent evidence regarding the influence of real-word repetition and how O-DDK rates vary across different PD stages. What this study adds to existing knowledge The O-DDK rates decline in a quadratic pattern as the PD progresses. The research provides insights into the advantage of real-word repetition in assessing O-DDK rates, with Malay real-word showing the lowest O-DDK rate, followed by English real-word and non-word. What are the potential or actual clinical implications of this work? Speech-language pathologists can better understand the evolving nature of speech motor impairments as PD progresses. This insight enables them to design targeted intervention strategies that are sensitive to the specific needs and challenges associated with each PD stage. This finding can guide clinicians in selecting appropriate assessment tools for evaluating speech motor function in PD patients.

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

INTRODUCTION

Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.

OBJECTIVE

The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.

METHODS

A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.

RESULTS

Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).

CONCLUSION

To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.

LEVEL OF EVIDENCE

5 Laryngoscope, 134:1333-1339, 2024.

Early ultrasonic vocalization deficits and related thyroarytenoid muscle pathology in the transgenic TgF344-AD rat model of Alzheimer's disease

Background

Alzheimer's disease (AD) is a progressive neurologic disease and the most common cause of dementia. Classic pathology in AD is characterized by inflammation, abnormal presence of tau protein, and aggregation of β-amyloid that disrupt normal neuronal function and lead to cell death. Deficits in communication also occur during disease progression and significantly reduce health, well-being, and quality of life. Because clinical diagnosis occurs in the mid-stage of the disease, characterizing the prodrome and early stages in humans is currently challenging. To overcome these challenges, we use the validated TgF344-AD (F344-Tg(Prp-APP, Prp-PS1)19/Rrrc) transgenic rat model that manifests cognitive, behavioral, and neuropathological dysfunction akin to AD in humans.

Objectives

The overarching goal of our work is to test the central hypothesis that pathology and related behavioral deficits such as communication dysfunction in part manifest in the peripheral nervous system and corresponding target tissues already in the early stages. The primary aims of this study are to test the hypotheses that: (1) changes in ultrasonic vocalizations (USV) occur in the prodromal stage at 6 months of age and worsen at 9 months of age, (2) inflammation as well as AD-related pathology can be found in the thyroarytenoid muscle (TA) at 12 months of age (experimental endpoint tissue harvest), and to (3) demonstrate that the TgF344-AD rat model is an appropriate model for preclinical investigations of early AD-related vocal deficits.

Methods

USVs were collected from male TgF344-AD (N = 19) and wildtype (WT) Fischer-344 rats (N = 19) at 6 months (N = 38; WT: n = 19; TgF344-AD: n = 19) and 9 months of age (N = 18; WT: n = 10; TgF344-AD: n = 8) and acoustically analyzed for duration, mean power, principal frequency, low frequency, high frequency, peak frequency, and call type. RT-qPCR was used to assay peripheral inflammation and AD-related pathology via gene expressions in the TA muscle of male TgF344-AD rats (n = 6) and WT rats (n = 6) at 12 months of age.

Results

This study revealed a significant reduction in mean power of ultrasonic calls from 6 to 9 months of age and increased peak frequency levels over time in TgF344-AD rats compared to WT controls. Additionally, significant downregulation of AD-related genes Uqcrc2, Bace2, Serpina3n, and Igf2, as well as downregulation of pro-inflammatory gene Myd88 was found in the TA muscle of TgF344-AD rats at 12 months of age.

Discussion

Our findings demonstrate early and progressive vocal deficits in the TgF344-AD rat model. We further provide evidence of dysregulation of AD-pathology-related genes as well as inflammatory genes in the TA muscles of TgF344-AD rats in the early stage of the disease, confirming this rat model for early-stage investigations of voice deficits and related pathology.

Sex Differences in the Speech of Persons With and Without Parkinson's Disease

BACKGROUND

Sex differences are apparent in the prevalence and the clinical presentation of Parkinson's disease (PD), but their effects on speech have been less studied.

METHOD

Speech acoustics of persons with (34 females and 34 males) and without (age- and sex-matched) PD were examined, assessing the effects of PD diagnosis and sex on ratings of dysarthria severity and acoustic measures of phonation (fundamental frequency standard deviation, smoothed cepstral peak prominence), speech rate (net syllables per second, percent pause ratio), and articulation (articulatory-acoustic vowel space, release burst precision).

RESULTS

Most measures were affected by PD (dysarthria severity, fundamental frequency standard deviation) and sex (smoothed cepstral peak prominence, net syllables per second, percent pause ratio, articulatory-acoustic vowel space), but without interactions between them. Release burst precision was differentially affected by sex in PD. Relative to those without PD, persons with PD produced fewer plosives with a single burst: females more frequently produced multiple bursts, whereas males more frequently produced no burst at all.

CONCLUSIONS

Most metrics did not indicate that speech production is differentially affected by sex in PD. Sex was, however, associated with disparate effects on release burst precision in PD, which deserves further study.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24388666.

Screening of Voice Pathologies: Identifying the Predictive Value of Voice Acoustic Parameters for Common Voice Pathologies

BACKGROUND

Voice acoustic analysis is important for objectively assessing voice production and diagnosing voice disorders.

AIM

This study aimed to investigate the sensitivity of various voice acoustic parameters in differentiating common voice pathology types.

METHODS

Data from the publicly available Perceptual Voice Qualities Database were analyzed; the database includes recordings of participants with and without voice disorders. A wide range of acoustic parameters was estimated from the recordings, such as alpha ratio, harmonics-to-noise ratio (HNR), cepstral peak prominence smoothed (CPPS), pitch period entropy (PPE), fundamental frequency, jitter, shimmer, and sound pressure levels. The predictive capabilities of the parameters were evaluated using receiver operating characteristic curves. Linear regression analysis determined the associations between parameters and voice disorders. Principal component analysis was conducted to identify important parameters for distinguishing voice disorders.

RESULTS AND CONCLUSION

This study has identified significant differences in acoustic parameters between those with and without voice disorders. Notably, the combination of five parameters-namely, PPE, shimmer, jitter, CPPS, and HNR-was identified as a strong predictor in voice disorder screening. These findings contribute substantially to the field of voice disorders, offering valuable insights for screening and diagnosis.

Towards interpretable speech biomarkers: exploring MFCCs

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

How older adults relate to their own voices: a qualitative study of subjective experiences of the aging voice

AIM

The aim of this study was to investigate how otherwise healthy older adults with self-assessed voice problems relate to their voice and voice changes.

METHOD

Focus groups were conducted at an activity center to identify how older adults reflect on their own voice and the aging voice in general. The interviews were audio recorded and transcribed. The analysis was done using thematic content analysis.

RESULTS

The analysis resulted in three main themes: "communicational aspects of the aging voice," "consequences of deteriorating vocal and communicative capacity," and "attitudes, strategies, and ideas". The participants considered voice to be an important communication tool and presented what could be interpreted as awareness regarding their voice. Voice changes were considered a natural part of aging. This attitude was also an important reason why the participants had not sought medical care for their voice problems. The participants discussed ideas concerning extended voice use to maintain a functioning voice when aging. Simultaneously, voice changes due to aging were considered to have a negative effect on communication and social participation.

CONCLUSIONS

The voice is important for older adults, and an insufficient voice can affect communication and social participation. Information about aging voice and voice exercises, for example from speech language pathologists, could be of interest among older adults. Further studies on the voice of older adults are needed regarding how they experience their voice and the general aspects of a healthy aging voice.

The meaning and impact on well-being of bespoke dancing sessions for those living with Parkinson's

PURPOSE

This paper presents qualitative research findings from the evaluation of a Parkinson's Dance well-being venture in the UK.

METHODS

Qualitative data was gathered to see how bespoke dancing sessions helped people with Parkinson's (PwP) to manage their conditions and improve their lives and prospects. Principles of a participatory approach were incorporated and methods included semi-structured interviewing, researchers participant observation and an elicitation-based activity. Nineteen PwP, six carers, four dance artists and seven helpers participated in the study.

RESULTS

Participating in Parkinson's Dance sessions meant that PwP could experience the possibilities to dance, develop a "can do" attitude, experience fun, enjoyment, social connection, exercise, movement to music, improvement and/or maintenance of their balance, suppleness, coordination and confidence with movement, symptoms being pushed back and ability to learn new things.

CONCLUSIONS

Our findings add to the evidence-base about the benefits of dance for people experiencing Parkinson's and through novel application of the Life-world based well-being framework of K. T. Galvin and Todres (2011) we propose a theoretical basis for Parkinson's Dance as a resource for well-being. There is scope to consider application of the well-being framework to other arts activities and as the basis of an arts and well-being evaluation tool.

Severity Classification Using Dynamic Time Warping-Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study

Background

In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores.

Objective

This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level.

Methods

We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy.

Results

Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/.

Conclusions

The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.

A review of emergent intelligent systems for the detection of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.

Premotor cortex is hypoactive during sustained vowel production in individuals with Parkinson's disease and hypophonia

Introduction

Hypophonia is a common feature of Parkinson's disease (PD); however, the contribution of motor cortical activity to reduced phonatory scaling in PD is still not clear.

Methods

In this study, we employed a sustained vowel production task during functional magnetic resonance imaging to compare brain activity between individuals with PD and hypophonia and an older healthy control (OHC) group.

Results

When comparing vowel production versus rest, the PD group showed fewer regions with significant BOLD activity compared to OHCs. Within the motor cortices, both OHC and PD groups showed bilateral activation of the laryngeal/phonatory area (LPA) of the primary motor cortex as well as activation of the supplementary motor area. The OHC group also recruited additional activity in the bilateral trunk motor area and right dorsal premotor cortex (PMd). A voxel-wise comparison of PD and HC groups showed that activity in right PMd was significantly lower in the PD group compared to OHC (p < 0.001, uncorrected). Right PMd activity was positively correlated with maximum phonation time in the PD group and negatively correlated with perceptual severity ratings of loudness and pitch.

Discussion

Our findings suggest that hypoactivation of PMd may be associated with abnormal phonatory control in PD.

Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis

Introduction

Deep brain stimulation of the subthalamic nucleus (STN-DBS) can exert relevant effects on the voice of patients with Parkinson's disease (PD). In this study, we used artificial intelligence to objectively analyze the voices of PD patients with STN-DBS.

Materials and methods

In a cross-sectional study, we enrolled 108 controls and 101 patients with PD. The cohort of PD was divided into two groups: the first group included 50 patients with STN-DBS, and the second group included 51 patients receiving the best medical treatment. The voices were clinically evaluated using the Unified Parkinson's Disease Rating Scale part-III subitem for voice (UPDRS-III-v). We recorded and then analyzed voices using specific machine-learning algorithms. The likelihood ratio (LR) was also calculated as an objective measure for clinical-instrumental correlations.

Results

Clinically, voice impairment was greater in STN-DBS patients than in those who received oral treatment. Using machine learning, we objectively and accurately distinguished between the voices of STN-DBS patients and those under oral treatments. We also found significant clinical-instrumental correlations since the greater the LRs, the higher the UPDRS-III-v scores.

Discussion

STN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.

Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels

Recent research has achieved a great classification rate for separating healthy people from those with Parkinson's disease (PD) using speech and the voice. However, these studies have primarily treated early and advanced stages of PD as equal entities, neglecting the distinctive speech impairments and other symptoms that vary across the different stages of the disease. To address this limitation, and improve diagnostic precision, this study assesses the selected acoustic features of dysphonia, as they relate to PD and the Hoehn and Yahr stages, by combining various preprocessing techniques and multiple classification algorithms, to create a comprehensive and robust solution for classification tasks. The dysphonia features extracted from the three sustained Korean vowels /아/(a), /이/(i), and /우/(u) exhibit diversity and strong correlations. To address this issue, the analysis of variance F-Value feature selection classifier from scikit-learn was employed, to identify the topmost relevant features. Additionally, to overcome the class imbalance problem, the synthetic minority over-sampling technique was utilized. To ensure fair comparisons, and mitigate the influence of individual classifiers, four commonly used machine learning classifiers, namely random forest (RF), support vector machine (SVM), k-nearest neighbor (kNN), and multi-layer perceptron (MLP), were employed. This approach enables a comprehensive evaluation of the feature extraction methods, and minimizes the variance in the final classification models. The proposed hybrid machine learning pipeline using the acoustic features of sustained vowels efficiently detects the early and mid-advanced stages of PD with a detection accuracy of 95.48%, and with a detection accuracy of 86.62% for the 4-stage, and a detection accuracy of 89.48% for the 3-stage classification of PD. This study successfully demonstrates the significance of utilizing the diverse acoustic features of dysphonia in the classification of PD and its stages.

Laryngeal symptoms related to motor phenotypes in Parkinson's disease: A systematic review

Objective

This study aimed to systematically review the associations between motor clinical phenotypes in Parkinson's disease (PD) and laryngeal disease symptoms. Laryngeal dysfunctions such as dysphonia and dysphagia are ubiquitous in people with Parkinson's disease (PwPD). Similar to other disease symptoms, they manifest variably across PwPD. Some of the variability within PD has been explained by clinical phenotypes. However, it is unclear how laryngeal symptoms of PD express themselves across these phenotypes.

Methods

Five databases were searched (MEDLINE, CINAHL, Web of Science, Embase, Scopus) in May 2022. After the removal of duplicates, all retrieved records were screened. Cohort, case-control, and cross-sectional studies in English discussing laryngeal symptoms and clinical PD phenotypes were included. Data were extracted, tabulated, and assessed using Moola et al.'s (2021) appraisal tool for systematic reviews of risk and etiology.

Results

The search retrieved 2370 records, representing 540 PwPD. After the removal of duplicates and screening, eight articles were included for review. The most common phenotype categories were tremor-dominant and postural-instability gait disordered (PIGD). Five studies addressed vocal characteristics, while four considered swallowing. Differences and lack of rigor in methodology across studies complicated conclusions, but a tendency for tremor-dominant phenotypes to present with less severe laryngeal symptoms was found.

Conclusion

Some minor differences in laryngeal function were found between tremor-dominant and PIGD phenotypes in PD. However, there is a need for more standardized and high-quality studies when comparing motor phenotypes for laryngeal function.

A study on the correlations between acoustic speech variables and bradykinesia in advanced Parkinson's disease

Background

Very few studies have assessed the presence of a possible correlation between speech variables and limb bradykinesia in patients with Parkinson's disease (PD). The objective of this study was to find correlations between different speech variables and upper extremity bradykinesia under different medication conditions in advanced PD patients.

Methods

Retrospective data were collected from a cohort of advanced PD patients before and after an acute levodopa challenge. Each patient was assessed with a perceptual-acoustic analysis of speech, which included several quantitative parameters [i.e., maximum phonation time (MPT) and intensity (dB)]; the Unified Parkinson's Disease Rating Scale (UPDRS) (total scores, subscores, and items); and a timed test (a tapping test for 20 s) to quantify upper extremity bradykinesia. Pearson's correlation coefficient was applied to find correlations between the different speech variables and the tapping rate.

Results

A total of 53 PD patients [men: 34; disease duration: 10.66 (SD 4.37) years; age at PD onset: 49.81 years (SD 6.12)] were included. Levodopa intake increased the MPT of sustained phonation (p < 0.01), but it reduced the speech rate (p = 0.05). In the defined-OFF condition, MPT of sustained phonation positively correlated with both bilateral mean (p = 0.044, r-value:0.299) and left (p = 0.033, r-value:0.314) tapping. In the defined-ON condition, the MPT correlated positively with bilateral mean tapping (p = 0.003), left tapping (p = 0.003), and right tapping (p = 0.008).

Conclusion

This study confirms the presence of correlations between speech acoustic variables and upper extremity bradykinesia in advanced PD patients. These findings suggest common pathophysiological mechanisms.

Machine learning approaches to identify Parkinson's disease using voice signal features

Parkinson's Disease (PD) is the second most common age-related neurological disorder that leads to a range of motor and cognitive symptoms. A PD diagnosis is difficult since its symptoms are quite similar to those of other disorders, such as normal aging and essential tremor. When people reach 50, visible symptoms such as difficulties walking and communicating begin to emerge. Even though there is no cure for PD, certain medications can relieve some of the symptoms. Patients can maintain their lifestyles by controlling the complications caused by the disease. At this point, it is essential to detect this disease and prevent it from progressing. The diagnosis of the disease has been the subject of much research. In our project, we aim to detect PD using different types of Machine Learning (ML), and Deep Learning (DL) models such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) to differentiate between healthy and PD patients by voice signal features. The dataset taken from the University of California at Irvine (UCI) machine learning repository consisted of 195 voice recordings of examinations carried out on 31 patients. Moreover, our models were trained using different techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and hyperparameter tuning (GridSearchCV) to enhance their performance. At the end, we found that MLP and SVM with a ratio of 70:30 train/test split using GridSearchCV with SMOTE gave the best results for our project. MLP performed with an overall accuracy of 98.31%, an overall recall of 98%, an overall precision of 100%, and f1-score of 99%. In addition, SVM performed with an overall accuracy of 95%, an overall recall of 96%, an overall precision of 98%, and f1-score of 97%. The experimental results of this research imply that the proposed method can be used to reliably predict PD and can be easily incorporated into healthcare for diagnosis purposes.

Perceptual and qualitative voice alterations detected by GIRBAS in patients with Parkinson's disease: is there a relation with lung function and oxygenation?

BACKGROUND

Impairments in respiration, voice and speech are common in people with Parkinson's disease (PD).

AIMS

To evaluate the prevalence of dysphonia, assessed by a specific acoustic evaluation and description of the voice by the speech therapist (GIRBAS), and its relation with lung function and oxygenation, in particular cough ability and during the night or exercise desaturation.

METHODS

This is a posthoc analysis of a prospective cross-sectional observational study on PD patients collecting anthropometric and clinical data, comorbidities, PD severity, motor function and balance, respiratory function at rest, during exercise and at night, voice function with acoustic analysis and presence of speech disorders, in addition to the GIRBAS scale. Based on GIRBAS Global dysphonia ('G') score, we divided patients into dysphonic (moderate-to-severe deviance from the euphonic condition) vs. no/mild dysphonic and analyzed the relations with respiratory impairments.

RESULTS

We analyzed 55 patients and found significant impairments in both respiratory and voice/speech functions. Most patients (85.5%) presented mild-to-severe deviance from the euphonic condition in at least one GIRBAS perceptual element (80% of cases for Global dysphonia) and only 14.5% did not show deviance in all elements simultaneously. At Odds Ratio analysis, the risk of presenting nocturnal desaturation and reduced peak cough expiratory flow was approximately 24 and 8 times higher, respectively, in dysphonic patients vs. those with no/mild dysphonia.

CONCLUSION

Perceptual and qualitative evaluation of the voice with GIRBAS showed that mild-to-severe dysphonia was highly prevalent in PD patients, and associated with nocturnal oxygen desaturation and poor cough ability.

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Parkinson's Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Parkinson's disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

Nyquist Plot Parametrization for Quantitative Analysis of Vibration of the Vocal Folds

OBJECTIVES

The Nyquist plot provides a graphical representation of the glottal cycles as elliptical trajectories in a 2D plane. This study proposes a methodology to parameterize the Nyquist plot with application to support the quantitative analysis of voice disorders.

METHODS

We considered high-speed videoendoscopy recordings of 33 functional dysphonia (FD) patients and 33 normophonic controls (NC). Quantitative analysis was performed by computing four shape-based parameters from the Nyquist plot: Variability, Size (Perimeter and Area), and Consistency. Additionally, we performed automatic classification using a linear support vector machine and feature importance analysis by combining the proposed features with state-of-the-art glottal area waveform (GAW) parameters.

RESULTS

We found that the inter-cycle variability was significantly higher in FD patients compared to NC. We achieved a classification accuracy of 83% when the top 30 most important features were used. Furthermore, the proposed Nyquist plot features were ranked in the top 12 most important features.

CONCLUSIONS

The Nyquist plot provides complementary information for subjective and objective assessment of voice disorders. On the one hand, with visual inspection it is possible to observe intra- and inter-glottal cycle irregularities during sustained phonation. On the other hand, shaped-based parameters allow quantifying such irregularities and provide complementary information to state-of-the-art GAW parameters.

Investigating the Impact of Voice Impairment on Quality of Life in Stroke Patients: The Voice Handicap Index (VHI) Questionnaire Study

The Voice Handicap Index (VHI) is a patient-centered evaluation tool specifically designed for assessing voice-related quality of life. Although the VHI has been extensively used in patients with voice disorders, its applicability in stroke patients has not been fully established. This prospective cross-sectional study aimed to investigate the feasibility of using the VHI questionnaire in identifying stroke patients with voice problems. The study included a cohort of acute to subacute first-ever stroke patients (n = 48), with or without voice problems, as well as other non-stroke patients (n = 31) who agreed to complete the VHI questionnaire. Stroke patients with self-reported voice problems demonstrated significantly higher VHI scores and poorer life quality scores compared to the control groups. These patients also had lower Mini-Mental State Examination (MMSE), Modified Barthel Index (MBI), and Euro-QoL-5D-5L (EQ-5D-5L) scores. Spearman correlation analysis revealed an inverse association between VHI scores and EQ-5D-5L (rho = -0.77, p < 0.001), Korean Mann Assessment of Swallowing Ability (rho = -0.51, p < 0.001), and other functional parameters, including the National Institutes of Health Stroke Scale, MMSE, and MBI scores. Multiple regression analysis indicated that the VHI score was the biggest contributing factor to EQ scores. This is the first study to demonstrate that stroke patients with voice problems may experience reduced quality of life, even after controlling for other confounding factors such as dysphagia or neurological deficits. Future studies are needed whether addressing these issues by implementing the VHI may facilitate the improvement of patients' quality of life.

Automated measurement of inter-arytenoid distance on 4D laryngeal CT: A validation study

Changes to the voice are prevalent and occur early in Parkinson's disease. Correlates of these voice changes on four-dimensional laryngeal computed-tomography imaging, such as the inter-arytenoid distance, are promising biomarkers of the disease's presence and severity. However, manual measurement of the inter-arytenoid distance is a laborious process, limiting its feasibility in large-scale research and clinical settings. Automated methods of measurement provide a solution. Here, we present a machine-learning module which determines the inter-arytenoid distance in an automated manner. We obtained automated inter-arytenoid distance readings on imaging from participants with Parkinson's disease as well as healthy controls, and then validated these against manually derived estimates. On a modified Bland-Altman analysis, we found a mean bias of 1.52 mm (95% limits of agreement -1.7 to 4.7 mm) between the automated and manual techniques, which improves to a mean bias of 0.52 mm (95% limits of agreement -1.9 to 2.9 mm) when variability due to differences in slice selection between the automated and manual methods are removed. Our results demonstrate that estimates of the inter-arytenoid distance with our automated machine-learning module are accurate, and represents a promising tool to be utilized in future work studying the laryngeal changes in Parkinson's disease.

Computerized analysis of speech and voice for Parkinson's disease: A systematic review

BACKGROUND AND OBJECTIVE

Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity.

METHODS

A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed.

RESULTS

Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians.

CONCLUSIONS

The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.

Speech and language characteristics differentiate Alzheimer's disease and dementia with Lewy bodies

Introduction

Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important, but it remains challenging. Different profiles of speech and language impairments between AD and DLB have been suggested, but direct comparisons have not been investigated.

Methods

We collected speech responses from 121 older adults comprising AD, DLB, and cognitively normal (CN) groups and investigated their acoustic, prosodic, and linguistic features.

Results

The AD group showed larger differences from the CN group than the DLB group in linguistic features, while the DLB group showed larger differences in prosodic and acoustic features. Machine-learning classifiers using these speech features achieved 87.0% accuracy for AD versus CN, 93.2% for DLB versus CN, and 87.4% for AD versus DLB.

Discussion

Our findings indicate the discriminative differences in speech features in AD and DLB and the feasibility of using these features in combination as a screening tool for identifying/differentiating AD and DLB.

Voice Alterations, Dysarthria, and Respiratory Derangements in Patients With Parkinson's Disease

PURPOSE

Almost 90% of people with Parkinson's disease (PD) develop voice and speech disorders during the course of the disease. Ventilatory dysfunction is one of the main causes. We aimed to evaluate relationships between respiratory impairments and speech/voice changes in PD.

METHOD

At Day 15 from admission, in consecutive clinically stable PD patients in a neurorehabilitation unit, we collected clinical data as follows: comorbidities, PD severity, motor function and balance, respiratory function at rest (including muscle strength and cough ability), during exercise-induced desaturation and at night, voice function (Voice Handicap Index [VHI] and acoustic analysis [Praat]), speech disorders (Robertson Dysarthria Profile [RDP]), and postural abnormalities. Based on an arbitrary RDP cutoff, two groups with different dysarthria degree were identified-moderate-severe versus no-mild dysarthria-and compared.

RESULTS

Of 55 patients analyzed (median value Unified Parkinson's Disease Rating Scale Part II 9 and Part III 17), we found significant impairments in inspiratory and expiratory muscle pressure (&gt; 90%, both), exercise tolerance at 6-min walking distance (96%), nocturnal (12.7%) and exercise-induced (21.8%) desaturation, VHI (34%), and Praat Shimmer% (89%). Patients with moderate-severe dysarthria (16% of total sample) had more comorbidities/disabilities and worse respiratory pattern and postural abnormalities (camptocormia) than those with no-mild dysarthria. Moreover, the risk of presenting nocturnal desaturation, reduced peak expiratory flow, and cough ability was about 11, 13, and 8 times higher in the moderate-severe group.

CONCLUSIONS

Dysarthria and respiratory dysfunction are closely associated in PD patients, particularly nocturnal desaturation and reduced cough ability. In addition, postural condition could be at the base of both respiratory and voice impairments.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.21210944.

Quantification of Vocal Fold Atrophy in Age‐Related and Parkinson's Disease‐Related Vocal Atrophy

OBJECTIVE

Vocal fold atrophy (VFA) is associated with aging and Parkinson's disease (PD). Clinical diagnosis of VFA depends on several visual-perceptual laryngostroboscopy findings that are inherently subjective. The purpose of this study was to use quantitative measurements to; (1) examine the relationships between VFA and dysphonia severity and (2) evaluate differences in VFA in patients with age-related VFA versus PD.

METHODS

Thirty-six patients >60 years of age with VFA were included in this retrospective cohort study. Demographic information, medical history, Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V), Voice Handicap Index-10 (VHI-10), and still images from the stroboscopic exam were obtained. Image J™ was used to measure VFA, including bowing index (BI), normalized glottal gap area, and normalized mucosal wave amplitude. Pearson's correlation was used to evaluate the relationship between VFA, CAPE-V, and VHI-10. t-Tests and multivariate linear regression were used to compare VFA measures by dysphonia severity (CAPE-V <30 vs. >30) and diagnosis (age-related vocal atrophy [ARVA] and PD).

RESULTS

BI was positively correlated with CAPE-V. Patients with CAPE-V >30 had a significantly larger BI compared to those with CAPE-V <30. Patients with PD had significantly larger BI than those with ARVA. Diagnosis of PD also predicted a larger BI after controlling for age and CAPE-V.

CONCLUSION

Quantitative measures supported an association between bowing severity and dysphonia severity in patients with PD and ARVA. A PD diagnosis significantly predicted more severe BI. These findings demonstrate the potential utility of BI. Quantitative VFA measures might also provide insight into the mechanisms of ARVA and dysphonia.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:1462-1469, 2023.

Classification of Dysphonic Voices in Parkinson's Disease with Semi-Supervised Competitive Learning Algorithm

This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson’s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann−Whitney−Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.

Effects of Resonance Tube Voice Therapy on Parkinson's Disease: Clinical Trial

PURPOSE

To verify the effect of resonance tube voice therapy on the vocal aspects of patients with Parkinson's Disease (PD).

METHOD

Intra-subject comparative controlled clinical trial with a single group assignment. Fourteen individuals with PD (10 men, mean age 66.1 years; four women, mean age 73.75 years) received eight 45-minute sessions of voice therapy, twice a week for 4 weeks. The therapy consisted of semi-occluded vocal tract exercises - phonation method in a resonance tube (glass, 27 cm x 9 mm) in water. Tube depth in water ranged from 2 cm to 9 cm, as the difficulty in carrying out the exercises increased (usual pitch, high pitch, low pitch, ascending/descending glissandos), followed by sentence production. The assessments were made three times: at baseline (Time0), after 30 days without intervention (Time1), and 1 day after eight intervention sessions (Time2). The following aspects were assessed: vocal intensity; acoustic parameters (Smoothed Cepstral Peak Prominence - CPPs, alpha ratio, and L1-L0 difference); auditory-perceptual analysis of the overall degree of vocal quality deviation; voice symptoms (Voice Symptom Scale protocol - VoiSS) and voice-related quality of life (Voice-Related Quality of Life Protocol - V-RQOL). The results were compared at the three times of assessment Time0/Time1/Time2 using one-way repeated measures ANOVA test and Tukey test (5% significance).

RESULTS

intervention significantly increased: vocal intensity, L1-L0 value of vowel /a/ and counting, CPP value in counting, and decreased: the overall degree of vocal quality deviation in 78% of participants, the total score of VoiSS protocol, the limitation, and emotional subscales. In addition, the intervention increased the score of all the domains of V-RQOL protocol - physical, socio-emotional, and total.

CONCLUSION

Resonance tube phonation in voice therapy was effective in increasing vocal intensity and long-term acoustic parameters, the improved overall degree of vocal quality, reducing voice symptoms, and increasing voice-related quality of life in individuals with PD.

Characterization of Mild and Moderate Dysarthria in Parkinson’s Disease: Behavioral Measures and Neural Correlates

Purpose

Alterations in speech and voice are among the most common symptoms in Parkinson’s disease (PD), often resulting in motor speech disorders such as hypokinetic dysarthria. We investigated dysarthria, verbal fluency, executive functions, and global cognitive function in relation to structural and resting-state brain changes in people with PD.

Methods

Participants with mild-moderate PD (n = 83) were recruited within a randomized controlled trial and divided into groups with varying degrees of dysarthria: no dysarthria (noDPD), mild dysarthria (mildDPD), moderate dysarthria (modDPD), and also combined mildDPD and modDPD into one group (totDPD). Voice sound level and dysphonia, verbal fluency, motor symptoms, executive functions, disease severity, global cognition, and neuroimaging were compared between groups. Gray matter volume and intensity of spontaneous brain activity were analyzed. Additionally, regressions between behavioral and neuroimaging data were performed.

Results

The groups differed significantly in mean voice sound level, dysphonia, and motor symptom severity. Comparing different severity levels of dysarthria to noDPD, groups differed focally in resting-state activity, but not in brain structure. In totDPD, lower scores on semantic verbal fluency, a composite score of executive functions, and global cognition correlated with lower superior temporal gyrus volume.

Conclusion

This study shows that severity of dysarthria may be related to underlying structural and resting-state brain alterations in PD as well as behavioral changes. Further, the superior temporal gyrus may play an important role in executive functions, language, and global cognition in people with PD and dysarthria.

Voice in Parkinson's Disease: A Machine Learning Study

Introduction

Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy.

Methods

We investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations.

Results

Voice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning.

Conclusion

Voice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.

Scoping review of the relationship between diabetes and voice quality

AIMS

The objective of this scoping review is to synthesize all of the known information about the relationship between diabetes mellitus and voice quality and to explore its potential applications for new technology.

METHODS

We conducted a scoping literature review of articles published between March 2000 and September 2021 using the following databases: PubMed, Web of Science, Scopus, and Embase. Additionally, we did a manual search of Google Scholar. The search strategy abides by the PRISMA-ScR guidelines. Studies pertaining to the relationship between diabetes and the voice were categorized separately for further evaluation.

RESULTS

Out of the 2732 originally identified articles, nine were ultimately included in this scoping review. The chosen articles address both diabetes and its impact on a variety of vocal parameters.

CONCLUSIONS

There is currently very little research investigating the relationship between diabetes, neuropathy, and phonatory symptoms. Additionally, existing publications contain some contradictory findings. Further research that incorporates imaging technology is needed to clarify the physiological explanations for the differences observed between healthy individuals and those with diabetes mellitus. Such information can be used to develop noninvasive technology for diabetes diagnosis and monitoring.

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

The Role of the Diaphragm in Postural Stability and Visceral Function in Parkinson's Disease

Background: In normal subjects, the diaphragm plays a key functional role in postural stability, articulation, respiration, defecation, and urination.

Objectives: The aim of this study was to investigate the role of the diaphragm in postural stability and visceral function in patients with Parkinson’s disease (PD) and to compare the diaphragm function by gender, Hoehn and Yahr (H&Y) staging, and motor subtypes.

Methods: In total, 79 patients were enrolled in this cross-sectional study. The severity of the disease was assessed by the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale III and by H&Y staging. Postural stability was quantitatively recorded, and respiratory function was evaluated by spirometry. Several scales were used to evaluate visceral function in patients with PD. In addition, diaphragm ultrasound was used to measure the excursion, contraction velocity, and thickness of the diaphragm during quiet breathing, deep breathing, and the sniff test. Significant features were selected by the least absolute shrinkage and selection operator (LASSO) regression and fitted in the multivariate linear regression and Pearson’s correlation analysis.

Results: Diaphragm thickness and excursion during quiet breathing were significantly different between men and women and between H&Y stage 1–2 and stage 2.5–3, whereas the diaphragm function was not influenced by motor subtypes. It was shown that the diaphragmatic function was significantly correlated with postural stability, voice function, respiratory function, constipation, and urological function to varying degrees in patients with PD.

Conclusion: The diaphragmatic function is associated with dysfunction in PD although it remains unclear as to whether the observed changes in the diaphragm are primary or secondary.

Parkinson's disease patients with freezing of gait have more severe voice impairment than non-freezers during "ON state"

BACKGROUND

Speech disorders and freezing of gait (FOG) in Parkinson's disease (PD) may have some common pathological mechanisms. The purpose of this study was to compare the acoustic parameters of PD patients with dopamine-responsive FOG (PD-FOG) and without FOG (PD-nFOG) during "ON state" and explore the ability of "ON state" voice features in distinguishing PD-FOG from PD-nFOG.

METHODS

A total of 120 subjects, including 40 PD patients with dopamine-responsive FOG, 40 PD-nFOG, and 40 healthy controls (HCs) were recruited. All subjects underwent neuropsychological tests. Speech samples were recorded through the sustained vowel pronunciation tasks during the "ON state" and then analyzed by the Praat software. A set of 27 voice features was extracted from each sample for comparison. Support vector machine (SVM) was used to build mathematical models to classify PD-FOG and PD-nFOG.

RESULTS

Compared with PD-nFOG, the jitter, the standard deviation of fundamental frequency (F0SD), the standard deviation of pulse period (pulse period SD) and the noise-homophonic-ratio (NHR) were increased, and the maximum phonation time (MPT) was decreased in PD-FOG. The above voice features were correlated with the freezing of gait questionnaire (FOGQ). The average accuracy, specificity, and sensitivity of SVM models based on 27 voice features for classifying PD-FOG and PD-nFOG were 73.57%, 75.71%, and 71.43%, respectively.

CONCLUSIONS

PD-FOG have more severe voice impairment than PD-nFOG during "ON state".

Partner perception of affective, behavioral, and cognitive reactions to voice use in people with Parkinson’s disease

•

Behavior Assessment Battery – Voice in people with Parkinson’s disease and proxies.

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Proxies and people with Parkinson’s Disease rate psychosocial impact similarly.

•

Proxies can be part of vocal assessment and treatment in Parkinson’s Disease.

Introduction

People with Parkinson’s disease (PWPD) experience negative feelings, thoughts, and coping behaviors due to the experienced communication challenges. This study aimed to compare the perceptions of PWPD with those of proxies for the affective, behavioral, and cognitive reactions specific to voice production during communicative interactions.

Methods

The Behavior Assessment Battery – Voice (BAB-Voice) was administered to 31 PWPD and their close communication partner/proxy. The BAB-Voice contained four subtests: Speech Situation Checklist – Emotional Reaction (SSC-ER), Speech Situation Checklist – Speech Disruption (SSC-SD), Behavior Checklist (BCL), and Communication Attitude Test for Adults (BigCAT). The scores for each of these subtests were calculated and statistically analyzed.

Results

A repeated measures MANOVA did not find statistically significant differences between the subscores of PWPD and proxies (Pillai’s trace = 0.25, F[4] = 2.22, p =.094, η_p² = 0.25). Fair to excellent agreement between the PWPD and proxies was found. The highest agreement was found on the BigCAT (ICC = 0.80). The SSC-SD (ICC = 0.77) and SSC-ER (ICC = 0.71) still showed excellent agreement, while only fair agreement was found for the BCL (ICC = 0.57).

Conclusion

Proxies were able to identify the affective, behavioral, and cognitive reactions to voice use in PWPD. Communication partners close to the PWPD could, therefore, provide valuable information regarding the assessment and treatment of hypophonia in PD.

Deficient Interhemispheric Connectivity Underlies Movement Irregularities in Parkinson's Disease

BACKGROUND

Movement execution is impaired in patients with Parkinson's disease. Evolving neurodegeneration leads to altered connectivity between distinct regions of the brain and altered activity at interconnected areas. How connectivity alterations influence complex movements like drawing spirals in Parkinson's disease patients remains largely unexplored.

OBJECTIVE

We investigated whether deteriorations in interregional connectivity relate to impaired execution of drawing.

METHODS

Twenty-nine patients and 31 age-matched healthy control participants drew spirals with both hands on a digital graphics tablet, and the regularity of drawing execution was evaluated by sample entropy. We recorded resting-state fMRI and task-related EEG, and calculated the time-resolved partial directed coherence to estimate effective connectivity for both imaging modalities to determine the extent and directionality of interregional interactions.

RESULTS

Movement performance in Parkinson's disease patients was characterized by increased sample entropy, corresponding to enhanced irregularities in task execution. Effective connectivity between the motor cortices of both hemispheres, derived from resting-state fMRI, was significantly reduced in Parkinson's disease patients in comparison to controls. The connectivity strength in the nondominant to dominant hemisphere direction in both modalities was inversely correlated with irregularities during drawing, but not with the clinical state.

CONCLUSION

Our findings suggest that interhemispheric connections are affected both at rest and during drawing movements by Parkinson's disease. This provides novel evidence that disruptions of interhemispheric information exchange play a pivotal role for impairments of complex movement execution in Parkinson's disease patients.

Speech Characteristics of Patients with Parkinson's Disease-Does Dopaminergic Medications Have a Role?

Objective  The present study aimed to investigate the effects of dopaminergic medication on voice, speech motor functions, and motor impairment in patients with Parkinson's disease (PD).

Materials and Methods  Twenty-five individuals (16 males and 9 females) with PD underwent comprehensive assessment of voice, speech, and motor functions in levodopa medication ON and medication OFF conditions. Age- and gender-matched healthy controls were recruited to compare speech and acoustic parameters. Multi-Dimensional Voice Program (MDVP) from Computerized Speech Laboratory (Model: 4500) was utilized for acoustic analysis of voice and the Voice Handicap Index (VHI) for the self-assessment of vocal function. Frenchay Dysarthria Assessment (FDA-2) and Unified Parkinson's Disease Rating Scale-III (UPDRS III) were used to evaluate speech motor and motor functions, respectively.

Statistical Analysis  The mean and standard deviation were used as descriptive statistics measures. Raw scores were obtained for FDA-2, DRS, VHI, MDVP parameters, and UPDRS-III in either medication condition. The Wilcoxon signed-rank test was performed to determine the statistical significance of the above measures in both genders across the medication conditions. Spearman's rank correlation coefficient was used to determine the relationship between motor speech function and motor impairment and between VHI and MDVP parameters across both medication conditions. The interrater reliability rating was established using Cohen's kappa.

Results  An improvement in lip and laryngeal functioning was found in the medication ON over medication OFF state in both males and females with PD. A few frequency and amplitude-related measures improved in the medication-ON state over the medication-OFF state. UPDRS-III scores reduced from the OFF state to the ON state, and no change in dysarthria severity or VHI was found in either gender or medication condition. No correlation was found between speech motor function and motor function or between VHI and acoustic parameters of voice in either medication condition.

Conclusions  Improvement in motor symptoms with levodopa was predominantly observed when compared with the minor improvements in a few aspects of speech motor function and vocal parameters. The results of this study suggest the need for speech therapy as a nonpharmacological treatment method for speech impairments in PD.

Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice

Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.

Perceptual Characterization of Voice Quality in Nonadvanced Stages of Parkinson's Disease

INTRODUCTION

Parkinson's disease (PD) is a neurodegenerative disorder that impacts motor and nonmotor systems, and consequently influences voice. In later stages of the disease, people with PD develop salient hypokinetic dysarthria. However, it is unclear how extensive the voice impairment is in the nonadvanced stages of PD. Therefore, the aim of the current research was to investigate the auditory-perceptual characteristics of voice in people with Parkinson's disease (PWPD) in nonadvanced stages.

METHODS

29 PWPD and 32 healthy older controls were recruited. For each participant, a recording of the sentence "We were away a year ago" was acquired. These recordings were evaluated by 2 licensed and experienced speech-language pathologists, who provided perceptual ratings of overall dysphonia severity, breathiness, roughness, and perceived age.

RESULTS

MANCOVA analysis showed that, when controlling for age and intensity, there was a significant effect of group (P = 0.001) on perceptual voice quality. PWPD were perceived to be significantly older, more breathy and more severely dysphonic than the older healthy controls. No differences were found for the perceived roughness.

CONCLUSIONS

The results suggest that perceptual features of hypokinetic dysarthria in voice, specifically breathiness, are present in nonadvanced stages of PWPD and may contribute to listener perceptions of speaker age. Moreover, the perceptual voice profiles in PWPD showed great variability, possibly reflecting the heterogeneity of disease impact on individuals. The results of this study may inform how research targets rehabilitation and maintenance of voice and laryngeal function in PWPD at nonadvanced stages.