Assessment of voice and speech symptoms in early Parkinson’s disease by the Robertson dysarthria profile

Effect of subthalamic and nigral deep brain stimulation on speech and voice in Parkinson's patients

Deep brain stimulation can influence the speech and voice quality in Parkinson´s disease (PD). This controlled, randomized, double-blind, cross-over clinical trial was conducted in 15 PD patients with bilateral subthalamic deep brain stimulation (DBS) to compare the effects of STN-DBS with combined subthalamic and nigral stimulation (STN + SNr-DBS) and DBS OFF on speech and voice parameters in PD patients. Speech and voice were analyzed subjectively using questionnaires (voice/pronunciation quality VAS, VHI, SHI) and objectively using audio analysis (maximum phonation time, AVQI, mean F0, intonation, syllable rate, reading time). Both stimulation conditions, STN + SNr-DBS and STN-DBS, revealed heterogeneous effects on speech and voice production with a slight beneficial effect on the voice quality of individual patients compared to DBS OFF, but not in the whole group. Small, but not significant effects were seen only in subjective voice quality on the VAS and intonation (both stimulation conditions compared to DBS OFF). No significant changes of the objective speech parameters during the audio analysis could be observed (both stimulation conditions compared to DBS OFF). There were no significant differences between STN + SNr-DBS and STN-DBS in any speech and voice domain. The beneficial effects on speech and voice production are minor in most patients compared to the motor improvements by DBS. Both STN-DBS and STN + SNr-DBS were safe, with comparable effects between both DBS modes, and represent no contraindications from the perspective of the voice specialist.

Voice self-assessment in individuals with Parkinson's Disease as compared to general voice disorders

BACKGROUND

Individuals with Parkinson's Disease (IwPD) often fail to adjust their voice in different situations, without awareness of this limitation. Clinicians use self-report questionnaires that are typically designed for individuals with General Voice Disorders (GVD) in the vocal assessment of IwPD. However, these instruments may not consider that IwPD have a reduced self-perception of their vocal deficits. This study aimed to compare self-reported vocal symptoms and voice loudness between IwPD and GVD.

METHODS

28 IwPD and 26 with GVD completed the Voice Symptom Scale (VoiSS) questionnaire to evaluate their voice self-perception. Vocal loudness (dB) was also assessed. Univariate and multivariate analyses were used to compare the outcomes from these measures between the two groups. Principal Component Analysis and Hierarchical Clustering Analysis were applied to explore data patterns related to voice symptoms.

RESULTS

IwPD reported significantly fewer vocal symptoms than those with GVD in all VoiSS questionnaire domains. Multivariate principal component analysis found no significant correlations between VoiSS scores and participant similarities in voice measures. Despite experiencing hypophonia, IwPD scored lower in all VoiSS domains but still fell in the healthy voice range. Hierarchical Clustering Analysis grouped participants into three distinct categories, primarily based on age, vocal loudness, and VoiSS domain scores, distinguishing between PD and GVD individuals.

CONCLUSIONS

IwPD reported fewer vocal symptoms than GVD. The voice self-assessment seems to be unreliable to assess vocal symptoms in IwPD, at least regarding loudness. New self-report instruments tailored to PD individuals are needed due to their particular voice characteristics.

Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset

Introduction

Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5.

Method

We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.

Results

Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.

Conclusion

The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

Intelligibility of speech in Parkinson's disease relies on anatomically segregated subthalamic beta oscillations

BACKGROUND

Speech impairment is commonly reported in Parkinson's disease and is not consistently improved by available therapies - including deep brain stimulation of the subthalamic nucleus (STN-DBS), which can worsen communication performance in some patients. Improving the outcome of STN-DBS on speech is difficult due to our incomplete understanding of the contribution of the STN to fluent speaking.

OBJECTIVE

To assess the relationship between subthalamic neural activity and speech production and intelligibility.

METHODS

We investigated bilateral STN local field potentials (LFPs) in nine parkinsonian patients chronically implanted with DBS during overt reading. LFP spectral features were correlated with clinical scores and measures of speech intelligibility.

RESULTS

Overt reading was associated with increased beta-low ([1220) Hz) power in the left STN, whereas speech intelligibility correlated positively with beta-high ([2030) Hz) power in the right STN.

CONCLUSION

We identified separate contributions from frequency and brain lateralization of the STN in the execution of an overt reading motor task and its intelligibility. This subcortical organization could be exploited for new adaptive stimulation strategies capable of identifying the occurrence of speaking behavior and facilitating its functional execution.

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.

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.

Feasibility of a standardized protocol for respiratory training with intermitted positive pressure breathing ventilator application in dysphonia and dysarthria

BACKGROUND

Brain damage can affect several functions related to speech production leading to dysphonia and dysarthria. Most rehabilitation treatments focus on articulation training rather than on pneumophonic coordination and respiratory muscle strength. Respiratory training using an intermitted positive pressure breathing (IPPB) ventilator can be used for this last purpose; no agreement on a standard protocol has been reached to date.

AIM

To evaluate the feasibility and the effectiveness of a standardized incremental protocol of respiratory training using IPPB to treat dysphonia and dysarthria.

DESIGN

Case series study.

SETTING

Neuropsychological Rehabilitation Unit in an Italian Neurorehabilitation Division.

POPULATION

Thirty-two subjects with dysphonia and dysarthria resulting from neurological lesion.

METHODS

Participants were assessed using clinical evaluation scales (GIRBAS scale of dysphonia, Robertson dysarthria profile), respiratory function test, and arterial blood gas analysis in air. The evaluations were performed at baseline and after 20 sessions of respiratory training with IPPB. The protocol provided a default increment of ventilator parameters. All subjects also underwent a standard speech and language therapy treatment. A satisfaction survey to assess acceptability and the Goal Attainment Scale were applied.

RESULTS

All participants fulfilled the protocol. No complications or discomfort were reported. Subjects' satisfaction at survey was 97.7%. After respiratory training, all respiratory function parameters increased, but only maximal voluntary ventilation (MVV), maximum inspiratory pressure (MIP), and maximum expiratory pressure (MEP) were statistically significant (P<0.05). Clinical evaluation scales significantly improved (P<0.05). Correlation between respiratory function parameters and clinical evaluation scales showed a moderate correlation between MVV, MEP, MIP, and Robertson dysarthria profile (P<0.01). A weak correlation was found between MIP, MVV, and GIRBAS scale (P<0.05).

CONCLUSIONS

Our protocol showed to be practical and well-tolerated. After respiratory training, MVV, MIP and MEP improved in significantly. Clinical scale scores improved in all participants.

CLINICAL REHABILITATION IMPACT

Respiratory training using IPPB ventilator can be useful in implementing speech and language treatments in subjects with dysphonia and dysarthria linked to brain injury.

Voice handicap Index in Parkinson's patients: Subthalamic versus globus pallidus deep brain stimulation

PURPOSE

Subthalamic nucleus (STN) and globus pallidus interna (GPI) are the two most common sites for deep brain stimulation (DBS) in people with Parkinson's disease (PWP). Voice impairments are a common symptom of Parkinson's disease and information about voice outcomes with DBS is limited. Most studies in speech-language pathology have focused on STN-DBS and few have examined the effects of GPI-DBS. This was an initial effort to examine the impact of DBS location on Vocal Handicap Index (VHI) scores, which assess the impact of a voice disorder on an individual.

METHOD

Twenty-four gender-matched PWP (12 STN-DBS and 12 GPI-DBS) completed the VHI post-DBS implantation. Two-tailed independent samples t-tests were used to compare each VHI scale score (physical, functional, emotional, total) and patient factors between the two groups.

RESULTS

No significant differences in total or subscale VHI scores were identified between the two DBS groups. A trend toward greater impairment in PWP with GPI-DBS was noted. An association between higher VHI scores and DBS settings was found.

CONCLUSIONS

Studies directly comparing speech outcomes for different DBS targets are lacking. The current findings provide new insights concerning voice outcomes following DBS by adding to the limited literature directly comparing speech outcomes in multiple DBS targets. Limitations and directions for future research are discussed.

An algorithm for Parkinson's disease speech classification based on isolated words analysis

INTRODUCTION

Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effective follow-up of PD clinical condition, possibly performed in the home environment.

METHODS

In this work, we performed a multi-level analysis, progressively combining features extracted from the entire signal, the voiced segments, and the on-set/off-set regions, leading to a total number of 126 features. Furthermore, we compared the performance of early and late feature fusion schemes, aiming to identify the best model configuration and taking advantage of having 25 isolated words pronounced by each subject. We employed data from the PC-GITA database (50 healthy controls and 50 PD patients) for validation and testing.

RESULTS

We implemented an optimized k-Nearest Neighbours model for the binary classification of PD patients versus healthy controls. We achieved an accuracy of 99.4% in 10-fold cross-validation and 94.3% in testing on the PC-GITA database (average value of male and female subjects).

CONCLUSION

The promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application.

Distinct patterns of speech disorder in early-onset and late-onset de-novo Parkinson's disease

Substantial variability and severity of dysarthric patterns across Parkinson's disease (PD) patients may reflect distinct phenotypic differences. We aimed to compare patterns of speech disorder in early-onset PD (EOPD) and late-onset PD (LOPD) in drug-naive patients at early stages of disease. Speech samples were acquired from a total of 96 participants, including two subgroups of 24 de-novo PD patients and two subgroups of 24 age- and sex-matched young and old healthy controls. The EOPD group included patients with age at onset below 51 (mean 42.6, standard deviation 6.1) years and LOPD group patients with age at onset above 69 (mean 73.9, standard deviation 3.0) years. Quantitative acoustic vocal assessment of 10 unique speech dimensions related to respiration, phonation, articulation, prosody, and speech timing was performed. Despite similar perceptual dysarthria severity in both PD subgroups, EOPD showed weaker inspirations (p = 0.03), while LOPD was characterized by decreased voice quality (p = 0.02) and imprecise consonant articulation (p = 0.03). In addition, age-independent occurrence of monopitch (p < 0.001), monoloudness (p = 0.008), and articulatory decay (p = 0.04) was observed in both PD subgroups. The worsening of consonant articulation was correlated with the severity of axial gait symptoms (r = 0.38, p = 0.008). Speech abnormalities in EOPD and LOPD share common features but also show phenotype-specific characteristics, likely reflecting the influence of aging on the process of neurodegeneration. The distinct pattern of imprecise consonant articulation can be interpreted as an axial motor symptom of PD.

Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach

BACKGROUND

Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject-level and task-related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods.

OBJECTIVE

We aimed to identify which speech dimensions best identify patients with PD in cognitively heterogeneous, cognitively preserved, and cognitively impaired groups through tasks with low (reading) and high (retelling) processing demands.

METHODS

We used support vector machines to analyze prosodic, articulatory, and phonemic identifiability features. Patient groups were compared with healthy control subjects and against each other in both tasks, using each measure separately and in combination.

RESULTS

Relative to control subjects, patients in cognitively heterogeneous and cognitively preserved groups were best discriminated by combined dysarthric signs during reading (accuracy = 84% and 80.2%). Conversely, patients with cognitive impairment were maximally discriminated from control subjects when considering phonemic identifiability during retelling (accuracy = 86.9%). This same pattern maximally distinguished between cognitively spared and impaired patients (accuracy = 72.1%). Also, cognitive (executive) symptom severity was predicted by prosody in cognitively preserved patients and by phonemic identifiability in cognitively heterogeneous and impaired groups. No measure predicted overall motor dysfunction in any group.

CONCLUSIONS

Predominant dysarthric symptoms appear to be best captured through undemanding tasks in cognitively heterogeneous and preserved cohorts and through cognitively loaded tasks in patients with cognitive impairment. Further applications of this framework could enhance dysarthria assessments in PD. © 2021 International Parkinson and Movement Disorder Society.

Non-pharmacological Treatment Challenges in Early Parkinson's Disease for Axial and Cognitive Symptoms: A Mini Review

Background: Parkinson's disease (PD) is now known to be a multisystemic heterogeneous neurodegenerative disease, including a wide spectrum of both motor and non-motor symptoms. PD patients' management must encompass a multidisciplinary approach to effectively address its complex nature. There are still challenges in terms of treating axial (gait, balance, posture, speech, and swallowing) and cognitive symptoms that typically arise with disease progression becoming poorly responsive to dopaminergic or surgical treatments. Objective: The objectives of the study are to further establish the presentation of axial and cognitive symptoms in early PD [Hoehn and Yahr (H&Y) scale ≤ 2] and to discuss the evidence for non-pharmacological approaches in early PD. Results: Mild and subtle changes in the investigated domains can be present even in early PD. Over the last 15 years, a few randomized clinical trials have been focused on these areas. Due to the low number of studies and the heterogeneity of the results, no definitive recommendations are possible. However, positive results have been obtained, with effective treatments being high-intensity treadmill and cueing for gait disturbances, high-intensity voice treatment, video-assisted swallowing therapy for dysphagia, and warm-up exercises and Wii FitTM training for cognition. Conclusions: Considering the association of motor, speech, and cognitive function, future trials should focus on multidisciplinary approaches to combined non-pharmacological management. We highlight the need for a more unified approach in managing these "orphan" symptoms, from the very beginning of the disease. The concept "the sooner the better" should be applied to multidisciplinary non-pharmacological management in PD.

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.

The physical significance of acoustic parameters and its clinical significance of dysarthria in Parkinson's disease

Dysarthria is universal in Parkinson's disease (PD) during disease progression; however, the quality of vocalization changes is often ignored. Furthermore, the role of changes in the acoustic parameters of phonation in PD patients remains unclear. We recruited 35 PD patients and 26 healthy controls to perform single, double, and multiple syllable tests. A logistic regression was performed to differentiate between protective and risk factors among the acoustic parameters. The results indicated that the mean f0, max f0, min f0, jitter, duration of speech and median intensity of speaking for the PD patients were significantly different from those of the healthy controls. These results reveal some promising indicators of dysarthric symptoms consisting of acoustic parameters, and they strengthen our understanding about the significance of changes in phonation by PD patients, which may accelerate the discovery of novel PD biomarkers.

Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson's disease

Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.

Laryngeal muscle biology in the Pink1-/- rat model of Parkinson disease

Neuromuscular pathology is found in the larynx and pharynx in humans with Parkinson disease (PD); however, it is unknown when this pathology emerges. We hypothesized that pathology occurs in early (premanifest) stages. To address this, we used the Pink1-/- rat model of PD, which shows age-dependent dopaminergic neuron loss, locomotor deficits, and deficits related to laryngeal function. We report findings in the thyroarytenoid muscle (TA) in Pink1-/- rats compared with wild-type (WT) control rats at 4 and 6 mo of age. TAs were analyzed for force production, myosin heavy chain isoform (MyHC), centrally nucleated myofibers, neural cell adhesion molecule, myofiber size, and muscle section size. Compared with WT, Pink1-/- TA had reductions in force levels at 1-Hz stimulation and 20-Hz stimulation, increases in relative levels of MyHC 2L, increases in incidence of centrally nucleated myofibers in the external division of the TA, and reductions in myofiber size of the vocalis division of the TA at 6 mo of age. Alterations of laryngeal muscle biology occur in a rat model of premanifest PD. Although these alterations are statistically significant, their functional significance remains to be determined. NEW & NOTEWORTHY Pathology of peripheral nerves and muscle has been reported in the larynx and pharynx of humans diagnosed with Parkinson disease (PD); however, it is unknown whether differences of laryngeal muscle occur at premanifest stages. This study examined the thyroarytenoid muscles of the Pink1-/- rat model of PD for differences of muscle biology compared with control rats. Thyroarytenoid muscles of Pink1-/- rats at premanifest stages show differences in multiple measures of muscle biology.

Towards the identification of Idiopathic Parkinson's Disease from the speech. New articulatory kinetic biomarkers

Although a large amount of acoustic indicators have already been proposed in the literature to evaluate the hypokinetic dysarthria of people with Parkinson's Disease, the goal of this work is to identify and interpret new reliable and complementary articulatory biomarkers that could be applied to predict/evaluate Parkinson's Disease from a diadochokinetic test, contributing to the possibility of a further multidimensional analysis of the speech of parkinsonian patients. The new biomarkers proposed are based on the kinetic behaviour of the envelope trace, which is directly linked with the articulatory dysfunctions introduced by the disease since the early stages. The interest of these new articulatory indicators stands on their easiness of identification and interpretation, and their potential to be translated into computer based automatic methods to screen the disease from the speech. Throughout this paper, the accuracy provided by these acoustic kinetic biomarkers is compared with the one obtained with a baseline system based on speaker identification techniques. Results show accuracies around 85% that are in line with those obtained with the complex state of the art speaker recognition techniques, but with an easier physical interpretation, which open the possibility to be transferred to a clinical setting.

Speech, Voice, and Communication

Communication changes are an important feature of Parkinson's and include both motor and nonmotor features. This chapter will cover briefly the motor features affecting speech production and voice function before focusing on the nonmotor aspects. A description of the difficulties experienced by people with Parkinson's when trying to communicate effectively is presented along with some of the assessment tools and therapists' treatment options. The idea of clinical heterogeneity of PD and subtyping patients with different communication problems is explored and suggestions are made on how this may influence clinicians' treatment methods and choices so as to provide personalized therapy programmes. The importance of encouraging and supporting people to maintain social networks, employment, and leisure activities is stated as the key to achieving sustainability. Finally looking into the future, the emergence of new technologies is seen as providing further possibilities to support therapists in the goal of helping people with Parkinson's to maintain good communication skills throughout the course of the disease.