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

Speech Amplification Device Usage for the Management of Hypophonia: A Survey of Speech-Language Pathologists

PURPOSE

The purpose of this study was to survey speech-language pathologists (SLPs) who assess and treat people with Parkinson's disease (PD) to gather insights into their decision making regarding their use or potential use of speech amplification technology for the management of hypophonia.

METHOD

A total of 111 SLPs who were currently practicing in the United States or Canada and had experience working with clients with PD for at least 2 years completed an anonymous Qualtrics survey. Questions were designed to probe the following areas: (a) degree of familiarity with amplification devices as a form of treatment for PD, (b) attitudes and perceptions of the implementation of these devices for PD, and (c) factors that influence the clinical decision to prescribe such devices.

RESULTS

Most participants (75; 71%) reported they had considered prescribing a device to at least one client with PD. When asked at which stages of speech or voice impairment they would consider the use of an amplification device for clients with PD, the most common response was for clients with moderate or severe hypophonia who were not stimulable for louder speech. However, 36 (32%) respondents indicated they would also consider an amplification device for clients who were stimulable for louder speech with severe hypophonia. When asked to rank the most important factors they would weigh when considering the prescription of an amplification device, they ranked the client's preference and comfort level as the most important consideration.

CONCLUSION

This study provides valuable clinical insights regarding how SLPs can approach utilizing speech amplification devices in the therapy environment.

Pausing and fluency in speech of patients with relapsing-remitting multiple sclerosis

Multiple Sclerosis (MS) causes a variety of symptoms in speech production, such as more frequent pauses and an increase in the duration of pauses in the speech. However, there is almost no data on whether the disease affects speech fluency in other ways, such as changes in the frequency of disfluencies in speech. The main question of this study is the following: if we examine speech fluency in speech tasks requiring different cognitive load, will there be a difference between patients and controls? Twenty people with relapsing-remitting MS (3 men and 17 women) and 20 age- and education-matched control speakers (4 men and 16 women) participated in the study. Speech samples were recorded with each participant in three speech tasks: 1) spontaneous narratives about their own lives, 2) narratives about their previous day, and 3) narrative recalls based on a heard text. In the speech samples, pauses and disfluencies were annotated and the duration of pauses was measured. Then, the frequency of pauses and disfluencies were calculated and the types of disfluencies were examined. The results show that there are differences in the frequency and duration of pauses between people with MS and controls. However, there were no significant differences in the frequency of disfluencies between the groups. The same types of disfluencies occurred in the same frequency in both groups. The results help to better understand the speech production processes in MS.

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.

Levodopa may modulate specific speech impairment in Parkinson's disease: an fMRI study

Hypokinetic dysarthria (HD) is a difficult-to-treat symptom affecting quality of life in patients with Parkinson's disease (PD). Levodopa may partially alleviate some symptoms of HD in PD, but the neural correlates of these effects are not fully understood. The aim of our study was to identify neural mechanisms by which levodopa affects articulation and prosody in patients with PD. Altogether 20 PD patients participated in a task fMRI study (overt sentence reading). Using a single dose of levodopa after an overnight withdrawal of dopaminergic medication, levodopa-induced BOLD signal changes within the articulatory pathway (in regions of interest; ROIs) were studied. We also correlated levodopa-induced BOLD signal changes with the changes in acoustic parameters of speech. We observed no significant changes in acoustic parameters due to acute levodopa administration. After levodopa administration as compared to the OFF dopaminergic condition, patients showed task-induced BOLD signal decreases in the left ventral thalamus (p = 0.0033). The changes in thalamic activation were associated with changes in pitch variation (R = 0.67, p = 0.006), while the changes in caudate nucleus activation were related to changes in the second formant variability which evaluates precise articulation (R = 0.70, p = 0.003). The results are in line with the notion that levodopa does not have a major impact on HD in PD, but it may induce neural changes within the basal ganglia circuitries that are related to changes in speech prosody and articulation.

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.

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Background and Objective

An aging society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson's disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients' quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerized analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems.

Methods

We enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrized in the fields of phonation, articulation and prosody. Video recordings of a face were analyzed in terms of facial landmarks movement. Both modalities were consequently modeled by the XGBoost algorithm.

Results

The acoustic analysis enabled diagnosis of PD with 77% balanced accuracy, while in the case of the facial analysis, we observed 81% balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83% (88% sensitivity and 78% specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia.

Conclusions

The introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83%. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Additionally, the clinical interpretation of the created models is illustrated. The presented computer-supported methodology could serve as an extra tool for neurologists in PD detection and the proposed potential solution of mHealth will facilitate the patient's and doctor's life.

On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voice

BACKGROUND AND OBJECTIVE

Parkinson's disease is the second-most-common neurodegenerative disorder that affects motor skills, cognitive processes, mood, and everyday tasks such as speaking and walking. The voices of people with Parkinson's disease may become weak, breathy, or hoarse and may sound emotionless, with slurred words and mumbling. Algorithms for computerized voice analysis have been proposed and have shown highly accurate results. However, these algorithms were developed on single, limited datasets, with participants possessing similar demographics. Such models are prone to overfitting and are unsuitable for generalization, which is essential in real-world applications.

METHODS

We evaluated the computerized Parkinson's disease diagnosis performance of various machine learning models and showed that these models degraded rapidly when used on different datasets. We evaluated two mainstream state-of-the-art approaches, one based on deep convolutional neural networks and another based on voice feature extraction followed by a shallow classifier (i.e., extreme gradient boosting (XGBoost)).

RESULTS

An investigation with four datasets (CzechPD, PC-GITA, ITA, and RMIT-PD) proved that even if the algorithms yielded excellent performance on a single dataset, the results obtained on new data or even a mix of datasets were very unsatisfactory.

CONCLUSIONS

More work needs to be done to make computerized voice analysis methods for Parkinson's disease diagnosis suitable for real-world applications.

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.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Early detection of Parkinson disease using stacking ensemble method

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures.

Has machine learning over-promised in healthcare? A critical analysis and a proposal for improved evaluation, with evidence from Parkinson’s disease

Adoption of artificial intelligence (AI) by the medical community has long been anticipated, endorsed by a stream of machine learning literature showcasing AI systems that yield extraordinary performance. However, many of these systems are likely over-promising and will under-deliver in practice. One key reason is the community's failure to acknowledge and address the presence of inflationary effects in the data. These simultaneously inflate evaluation performance and prevent a model from learning the underlying task, thus severely misrepresenting how that model would perform in the real world. This paper investigated the impact of these inflationary effects on healthcare tasks, as well as how these effects can be addressed. Specifically, we defined three inflationary effects that occur in medical data sets and allow models to easily reach small training losses and prevent skillful learning. We investigated two data sets of sustained vowel phonation from participants with and without Parkinson's disease, and revealed that published models which have achieved high classification performances on these were artificially enhanced due to the inflationary effects. Our experiments showed that removing each inflationary effect corresponded with a decrease in classification accuracy, and that removing all inflationary effects reduced the evaluated performance by up to 30%. Additionally, the performance on a more realistic test set increased, suggesting that the removal of these inflationary effects enabled the model to better learn the underlying task and generalize. Source code is available at https://github.com/Wenbo-G/pd-phonation-analysis under the MIT license.

Sex Differences in Motor and Non-Motor Symptoms among Spanish Patients with Parkinson's Disease

BACKGROUND AND OBJECTIVE

Sex plays a role in Parkinson's disease (PD) mechanisms. We analyzed sex difference manifestations among Spanish patients with PD.

PATIENTS AND METHODS

PD patients who were recruited from the Spanish cohort COPPADIS from January 2016 to November 2017 were included. A cross-sectional and a two-year follow-up analysis were conducted. Univariate analyses and general linear model repeated measure were used.

RESULTS

At baseline, data from 681 PD patients (mean age 62.54 ± 8.93) fit the criteria for analysis. Of them, 410 (60.2%) were males and 271 (39.8%) females. There were no differences between the groups in mean age (62.36 ± 8.73 vs. 62.8 ± 9.24; p = 0.297) or in the time from symptoms onset (5.66 ± 4.65 vs. 5.21 ± 4.11; p = 0.259). Symptoms such as depression (p < 0.0001), fatigue (p < 0.0001), and pain (p < 0.00001) were more frequent and/or severe in females, whereas other symptoms such as hypomimia (p < 0.0001), speech problems (p < 0.0001), rigidity (p < 0.0001), and hypersexuality (p < 0.0001) were more noted in males. Women received a lower levodopa equivalent daily dose (p = 0.002). Perception of quality of life was generally worse in females (PDQ-39, p = 0.002; EUROHIS-QOL8, p = 0.009). After the two-year follow-up, the NMS burden (Non-Motor Symptoms Scale total score) increased more significantly in males (p = 0.012) but the functional capacity (Schwab and England Activities of Daily Living Scale) was more impaired in females (p = 0.001).

CONCLUSION

The present study demonstrates that there are important sex differences in PD. Long-term prospective comparative studies are needed.

The efficacy of acoustic-based articulatory phenotyping for characterizing and classifying four divergent neurodegenerative diseases using sequential motion rates

Despite the impacts of neurodegeneration on speech function, little is known about how to comprehensively characterize the resulting speech abnormalities using a set of objective measures. Quantitative phenotyping of speech motor impairments may have important implications for identifying clinical syndromes and their underlying etiologies, monitoring disease progression over time, and improving treatment efficacy. The goal of this research was to investigate the validity and classification accuracy of comprehensive acoustic-based articulatory phenotypes in speakers with distinct neurodegenerative diseases. Articulatory phenotypes were characterized based on acoustic features that were selected to represent five components of motor performance: Coordination, Consistency, Speed, Precision, and Rate. The phenotypes were first used to characterize the articulatory abnormalities across four progressive neurologic diseases known to have divergent speech motor deficits: amyotrophic lateral sclerosis (ALS), progressive ataxia (PA), Parkinson's disease (PD), and the nonfluent variant of primary progressive aphasia and progressive apraxia of speech (nfPPA + PAOS). We then examined the efficacy of articulatory phenotyping for disease classification. Acoustic analyses were conducted on audio recordings of 217 participants (i.e., 46 ALS, 52 PA, 60 PD, 20 nfPPA + PAOS, and 39 controls) during a sequential speech task. Results revealed evidence of distinct articulatory phenotypes for the four clinical groups and that the phenotypes demonstrated strong classification accuracy for all groups except ALS. Our results highlight the phenotypic variability present across neurodegenerative diseases, which, in turn, may inform (1) the differential diagnosis of neurological diseases and (2) the development of sensitive outcome measures for monitoring disease progression or assessing treatment efficacy.

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.

Articulatory undershoot of vowels in isolated REM sleep behavior disorder and early Parkinson's disease

Imprecise vowels represent a common deficit associated with hypokinetic dysarthria resulting from a reduced articulatory range of motion in Parkinson's disease (PD). It is not yet unknown whether the vowel articulation impairment is already evident in the prodromal stages of synucleinopathy. We aimed to assess whether vowel articulation abnormalities are present in isolated rapid eye movement sleep behaviour disorder (iRBD) and early-stage PD. A total of 180 male participants, including 60 iRBD, 60 de-novo PD and 60 age-matched healthy controls performed reading of a standardized passage. The first and second formant frequencies of the corner vowels /a/, /i/, and /u/ extracted from predefined words, were utilized to construct articulatory-acoustic measures of Vowel Space Area (VSA) and Vowel Articulation Index (VAI). Compared to controls, VSA was smaller in both iRBD (p = 0.01) and PD (p = 0.001) while VAI was lower only in PD (p = 0.002). iRBD subgroup with abnormal olfactory function had smaller VSA compared to iRBD subgroup with preserved olfactory function (p = 0.02). In PD patients, the extent of bradykinesia and rigidity correlated with VSA (r = -0.33, p = 0.01), while no correlation between axial gait symptoms or tremor and vowel articulation was detected. Vowel articulation impairment represents an early prodromal symptom in the disease process of synucleinopathy. Acoustic assessment of vowel articulation may provide a surrogate marker of synucleinopathy in scenarios where a single robust feature to monitor the dysarthria progression is needed.

Sex Differences in Parkinson’s Disease: From Bench to Bedside

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease and gender differences have been described on several aspects of PD. In the present commentary, we aimed to collect and discuss the currently available evidence on gender differences in PD regarding biomarkers, genetic factors, motor and non-motor symptoms, therapeutic management (including pharmacological and surgical treatment) as well as preclinical studies. Methods: A systematic literature review was performed by searching the Pubmed and Scopus databases with the search strings “biomarkers”, “deep brain stimulation”, “female”, “gender”, “genetic”, “levodopa”, “men”, “male”, “motor symptoms”, “non-motor symptoms”, “Parkinson disease”, “sex”, “surgery”, and “women”. Results: The present review confirms the existence of differences between men and women in Parkinson Disease, pointing out new information regarding evidence from animal models, genetic factors, biomarkers, clinical features and pharmacological and surgical treatment. Conclusions: The overall goal is to acquire new informations about sex and gender differences in Parkinson Disease, in order to develop tailored intervetions.

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.

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.

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.

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".