Objective vowel sound characteristics and their relationship with motor dysfunction in Asian Parkinson's disease patients

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.

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.

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.

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.

Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features

Parkinson’s disease (PD), a slow-progressing neurological disease, affects a large percentage of the world’s elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.

Automated speech analysis in early untreated Parkinson's disease: Relation to gender and dopaminergic transporter imaging

BACKGROUND

The mechanisms underlying speech abnormalities in Parkinson's disease (PD) remain poorly understood, with most of the available evidence based on male patients. This study aimed to estimate the occurrence and characteristics of speech disorder in early, drug-naive PD patients with relation to gender and dopamine transporter imaging.

METHODS

Speech samples from 60 male and 40 female de novo PD patients as well as 60 male and 40 female age-matched healthy controls were analyzed. Quantitative acoustic vocal assessment of 10 distinct speech dimensions related to phonation, articulation, prosody, and speech timing was performed. All patients were evaluated using [123]I-2b-carbomethoxy-3b-(4-iodophenyl)-N-(3-fluoropropyl) nortropane single-photon emission computed tomography and Montreal Cognitive Assessment.

RESULTS

The prevalence of speech abnormalities in the de novo PD cohort was 56% for male and 65% for female patients, mainly manifested with monopitch, monoloudness, and articulatory decay. Automated speech analysis enabled discrimination between PD and controls with an area under the curve of 0.86 in men and 0.93 in women. No gender-specific speech dysfunction in de novo PD was found. Regardless of disease status, females generally showed better performance in voice quality, consonant articulation, and pauses production than males, who were better only in loudness variability. The extent of monopitch was correlated to nigro-putaminal dopaminergic loss in men (r = 0.39, p = 0.003) and the severity of imprecise consonants was related to cognitive deficits in women (r = -0.44, p = 0.005).

CONCLUSIONS

Speech abnormalities represent a frequent and early marker of motor abnormalities in PD. Despite some gender differences, our findings demonstrate that speech difficulties are associated with nigro-putaminal dopaminergic deficits.

Assessment of Acoustic Features and Machine Learning for Parkinson's Detection

This article presents a machine learning approach for Parkinson's disease detection. Potential multiple acoustic signal features of Parkinson's and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson's magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.