Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson's disease

A Systematic Review and Bayesian Meta-Analysis of Acoustic Measures of Prosody in Parkinson's Disease

PURPOSE

Linguistic prosody is affected in Parkinson's disease (PD), which implicates the basal ganglia's role in the production of prosody. However, there is no recent systematic synthesis of the available acoustic evidence of prosodic impairment in PD. This study aimed to identify the acoustic features of linguistic prosody that are consistently affected in PD.

METHOD

The authors systematically reviewed articles that reported acoustic features of prosodic production in PD. Articles focused on fundamental frequency (F0) and its variability, intensity and its variability, speech and articulation rate, and pause duration and ratio. From a total of 648 records identified, 36 met criteria for inclusion and exclusion. For each acoustic measurement and task, data from people with PD (PwPD) were compared with those from controls to extract effect sizes. Pooled effect sizes were estimated using robust Bayesian hierarchical regression models.

RESULTS

PD was associated with decreased F0 variability and increased pause duration. There was limited evidence of reduced intensity variability and speech rate in PwPD. No evidence was found to suggest that PD affects articulation rate or pause ratio.

CONCLUSIONS

The primary acoustic parameters of prosody affected by PD are F0 variability and pause duration. The identification of these acoustic parameters has important clinical implications for the selection of PD management strategies. The association of F0 variability and pause duration with PD suggests that the neural circuits controlling these parameters are at least partly shared and might include the basal ganglia. While the current study focused on the phonetic realization of prosodic cues, future studies should examine whether and how PD affects prosody at higher levels of processing.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25892923.

Analysis of Voice Changes in Early-Stage Parkinson's Disease with AVQI and ABI: A Follow-up Study

OBJECTIVES

The purpose of this pilot study was to examine voice quality changes in individuals with early-stage Parkinson's disease (PD) utilizing the Acoustic Voice Quality Index (AVQI) and Acoustic Breathiness Index (ABI) over approximately a 1-year period.

STUDY DESIGN

Follow-up study.

METHODS

Baseline and follow-up data were gathered from the PDSTUlong speech corpus. The data for both time points included: speaker background information, sustained vowels, reading samples, and measures of PD severity (Hoehn and Yahr scores and Unified Parkinson's Disease Rating Scale III scores [UPDRS-III]). All speakers (N = 12) were native Finnish speakers. AVQIv03.01 and ABI analysis were completed in VOXplot v2.0.1. Changes in AVQI and ABI scores between baseline and follow-up were examined via causal analysis. Further, AVQI and ABI were analyzed in relation to measures of PD severity.

RESULTS

Baseline mean AVQI score was 1.79 (range 0.14-4.83, SD=1.60), whereas follow-up mean AVQI score was 2.25 (range 0.55-4.53, SD=1.36). Baseline mean ABI score, in turn, was 2.92 (range 1-27 - 5.31, SD=1.57), whereas follow-up mean ABI score was 3.42 (range 1.40-5.40, SD=1.38). A significant difference was found between baseline and follow-up measures for both AVQI (Z = -2.002, P = 0.045) and ABI (Z = -2.197, P = 0.028). A significant difference in smoothed cepstral peak prominence (Z = -2.118, P = 0.034) and harmonics-to-noise ratio (Z = -1.961, P = 0.050) was also found between the two measurement periods. Change in AVQI and ABI were not correlated with the change in measures of PD severity.

CONCLUSION

Over approximately 1-year, a statistical change was observed in AVQI and ABI scores, even in such a small dataset. The specific qualities of breathiness and hoarseness showed the most significant progression. Changes in voice quality were more prominent in ABI analysis.

Intra-subject enveloped multilayer fuzzy sample compression for speech diagnosis of Parkinson's disease

Machine learning-based Parkinson's disease (PD) speech diagnosis is a current research hotspot. However, existing methods use each corpus sample as the base unit for modeling. Since different corpus samples within the same subject have different sensitive speech features, it is difficult to obtain unified and stable sensitive speech features (diagnostic markers) that reflect the pathology of the whole subject. Therefore, this study aims at compressing the corpus samples within the subject to facilitate the search for diagnostic markers with high diagnostic accuracy. A two-step sample compression module (TSCM) can solve the problem above. It includes two major parts: sample pruning module (SPM) and sample fuzzy clustering mechanism (SFCMD). Based on stacking multiple TSCMs, a multilayer sample compression module (MSCM) is formed to obtain multilayer compression samples. After that, simultaneous sample/feature selection mechanism (SS/FSM) is designed for feature selection. Based on the multilayer compression samples processed by MSCM and SS/FSM, a novel ensemble learning algorithm (EMSFE) is designed with sparse fusion ensemble learning mechanism (SFELM). The proposed EMSFE is validated by visualization of extracted features and performance comparison with related algorithms. The experimental results show that the proposed algorithm can effectively extract the stable diagnostic markers by compressing the corpus samples within the subject. Furthermore, based on LOSO cross validation, the proposed algorithm with extreme learning machine (ELM) classifier can achieve the accuracy of 92.5%, 93.75% and 91.67% on three datasets, respectively. The proposed EMSFE can extract unified and stable sensitive features that accurately reflect the overall pathology of the subject, which can better meet the requirements of clinical applications.The code and datasets can be found in: https://github.com/wywwwww/EMSFE-supplementary-material.git.

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.

CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments

Parkinson's disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson's disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.

Reading fluency in Spanish patients with Parkinson's disease: A reading prosody examination

BACKGROUND

The expressiveness during reading is essential for a fluent reading. Reading prosody has been scarcely studied in an experimental manner, owing to the difficulties in taking objective and direct measures of this reading skill. However, new technologies development has made it possible to analyse reading prosody in an experimental way. Prosodic patterns may vary, not being the same at the beginning of the reading learning process as in adulthood. They may also be altered in disorders such as dyslexia, but little is known about the prosodic characteristics and reading fluency of people with neurodegenerative diseases that cause language impairment, such as Parkinson's disease (PD).

AIMS

The aim of this work was to study reading fluency in PD considering the prosodic characteristics of its reading.

METHODS & PROCEDURES

The participants were 31 Spanish adults with PD and 31 healthy controls, aged 59-88 years. Two experimental texts were designed that included declarative, interrogative, and exclamatory sentences and experimental verbs and nouns. The manipulability level of the nouns and the motor content of the verbs were considered. The reading of the participants was recorded and analysed with Praat software.

OUTCOMES & RESULTS

A longer reading duration and a greater number of pauses, especially in verbs, were found in the PD group, which also showed less pitch variation than the control group in the experimental sentences. The control group showed a big initial rise in declarative and interrogative sentences, as well as a stronger final declination in declarative and exclamatory ones, when compared to the PD group.

CONCLUSIONS & IMPLICATIONS

The use of experimental methodologies for the analysis of reading fluency allows learning more about the prosodic characteristics of people with different pathologies, such as PD. Scarce pitch variability found in the analysis, together with the great number of pauses and the longer reading duration, leads to poorly expressive reading, which compromises fluency in PD. The exhaustive evaluation of the reading fluency of PD patients will make it possible to design more complete assessment methods that will favour the diagnosis and early detection of this pathology.

WHAT THIS STUDY ADDS

What is already known on this subject • The speech of people with Parkinson's disease (PD) is often impaired by the appearance of hypokinetic dysarthria. The language of people with PD is usually affected with the progression of the disease, with lexico-semantic impairment which mainly affects verbs. Previous literature on reading fluency in PD usually considers reading speed and accuracy, neglecting prosody. Other neurodegenerative diseases with language impairment, such as Alzheimer's disease, commonly cause reading fluency problems. What this paper adds to existing knowledge • This study provides direct and objective measures of the reading fluency (speed, accuracy and prosody) in patients with PD, by the design of experimental texts. Reading fluency characteristics were found to be altered in these patients, especially in pitch variations and reading duration. The reading of Parkinson's patients showed a more flattened pitch. In addition, a greater number of pauses and longer reading durations were also found in the reading of verbs compared to the control group. What are the potential or actual clinical implications of this work? • The use of experimentally created texts makes it possible to analyse the influence of different psycholinguistic variables (frequency, length, motor content, manipulability) on reading fluency, and how the processing of these stimuli could be affected in PD. The objective analysis of the reading fluency characteristics in PD allows the design of more specific evaluation and diagnostic tasks. More complete assessment methods may allow the early detection of the disease. In the same way, it may favour a differential diagnosis with other neurodegenerative diseases.

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.

An integrated biometric voice and facial features for early detection of Parkinson's disease

Hypomimia and voice changes are soft signs preceding classical motor disability in patients with Parkinson's disease (PD). We aim to investigate whether an analysis of acoustic and facial expressions with machine-learning algorithms assist early identification of patients with PD. We recruited 371 participants, including a training cohort (112 PD patients during "on" phase, 111 controls) and a validation cohort (74 PD patients during "off" phase, 74 controls). All participants underwent a smartphone-based, simultaneous recording of voice and facial expressions, while reading an article. Nine different machine learning classifiers were applied. We observed that integrated facial and voice features could discriminate early-stage PD patients from controls with an area under the receiver operating characteristic (AUROC) diagnostic value of 0.85. In the validation cohort, the optimal diagnostic value (0.90) maintained. We concluded that integrated biometric features of voice and facial expressions could assist the identification of early-stage PD patients from aged controls.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

Random Forest Algorithm Based on Speech for Early Identification of Parkinson's Disease

To investigate the effectiveness of identifying patients with Parkinson's disease (PD) from speech signals, various acoustic parameters including prosodic and segmental features are extracted from speech and then the random forest classification (RF) algorithm based on these acoustic parameters is applied to diagnose early-stage PD patients. To validate the proposed method of RF algorithm in early-stage PD identification, this study compares the accuracy rate of RF with that of neurologists' judgments based on auditory test outcomes, and the results clearly show the superiority of the proposed method over its rival. Random forest algorithm based on speech can improve the accuracy of patients' identification, which provides an efficient auxiliary method in the early diagnosis of PD patients.

Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson's Disease Speech Data

As a neurodegenerative disease, Parkinson's disease (PD) is hard to identify at the early stage, while using speech data to build a machine learning diagnosis model has proved effective in its early diagnosis. However, speech data show high degrees of redundancy, repetition, and unnecessary noise, which influence the accuracy of diagnosis results. Although feature reduction (FR) could alleviate this issue, the traditional FR is one-sided (traditional feature extraction could construct high-quality features without feature preference, while traditional feature selection could achieve feature preference but could not construct high-quality features). To address this issue, the Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model (HBD-SFREM) is proposed in this paper. The major contributions of HBD-SFREM are as follows: (1) The instance space of the deep hierarchy is built by an iterative deep extraction mechanism. (2) The manifold features extraction method embeds the nearest neighbor feature preference method to form the dual-stage feature reduction pair. (3) The dual-stage feature reduction pair is iteratively performed by the AdaBoost mechanism to obtain instances features with higher quality, thus achieving a substantial improvement in model recognition accuracy. (4) The deep hierarchy instance space is integrated into the original instance space to improve the generalization of the algorithm. Three PD speech datasets and a self-collected dataset are used to test HBD-SFREM in this paper. Compared with other FR algorithms and deep learning algorithms, the accuracy of HBD-SFREM in PD speech recognition is improved significantly and would not be affected by a small sample dataset. Thus, HBD-SFREM could give a reference for other related studies.

Parkinson's Disease Detection Based on Running Speech Data From Phone Calls

OBJECTIVE

Parkinson's Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living offers the potential for transforming the disease assessment and accelerating PD diagnosis.

METHODS

A privacy-aware method for classifying PD patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed here. Voice features from running speech signals were extracted from recordings passively captured over voice phone calls. Features are fed in a language-aware training of multiple- and single-instance learning classifiers, along with demographic variables, exploiting a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients) to classify PD.

RESULTS

By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of-sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. Comparative analysis with other approaches for language-aware PD detection justified the efficiency of the proposed one, considering the ecological validity of the acquired voice data.

CONCLUSIONS

The present work demonstrates increased robustness in PD detection using voice data captured in-the-wild.

SIGNIFICANCE

A high-frequency, privacy-aware and unobtrusive PD screening tool is introduced for the first time, based on analysis of voice samples captured during routine phone calls.

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.

Reading Fluency in Spanish Patients with Alzheimer's Disease

BACKGROUND

Reading fluency is essential for our functioning in the literate society in which we live. Reading expressiveness or prosody, along with speed and accuracy, are considered key aspects of fluent reading. Prosodic patterns may vary, not being the same in children learning to read as in adulthood. But little is known about the prosodic characteristics and reading fluency of people with neurodegenerative diseases that causes language impairment and reading difficulties, such as Alzheimer's disease (AD).

OBJECTIVE

The aim of this work was to study reading fluency in AD, considering reading speed, accuracy and reading prosody.

METHODS

The participants were 20 healthy elderly Spanish adults, and 20 AD patients, aged 64-88 years. An experimental text was designed, that included declarative, exclamatory, and interrogative sentences, words with different stresses and low-frequency words. The reading of the participants was recorded and analyzed using Praat software.

RESULTS

The AD group showed significantly longer reading duration, both at the syllable level and at the word and sentence level. These patients also committed more pauses between words, which were also longer, and more reading errors. The control group showed a variation of the syllabic F0 in the three types of sentences, while these variations only appeared in declarative ones in the AD group.

CONCLUSION

The pauses, along with the slight pitch variations and the longer reading times and errors committed, compromise the reading fluency of people with AD. Assessment of this reading feature could be interesting as a possible diagnostic marker for the disease.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease

Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples of speech-based PD is relatively small, and there exist discrepancies in the distribution between subjects. In order to solve the two problems, a novel unsupervised two-step sparse transfer learning is proposed in this paper to tackle with PD speech diagnosis. In the first step, convolution sparse coding with the coordinate selection of samples and features is designed to learn speech structure from the source domain to replenish sample information of the target domain. In the second step, joint local structure distribution alignment is designed to maintain the neighbor relationship between the respective samples of the training set and test set, and reduce the distribution difference between the two domains at the same time. Two representative public PD speech datasets and one real-world PD speech dataset were exploited to verify the proposed method on PD speech diagnosis. Experimental results demonstrate that each step of the proposed method has a positive effect on the PD speech classification results, and it also delivers superior performance over the existing relative methods.

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.

An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement

BACKGROUND AND OBJECTIVES

Diagnosis of Parkinson's with higher accuracy is always desirable to slow down the progression of the disease and improved quality of life. There are evidences of inherent neurological differences between male and females as well as between elderly and adults. However, the potential of such gender and age infomration have not been exploited yet for Parkinson's identification.

METHODS

In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's (n = 37; m/f-19/18;age-69.3 ± 10.9yrs) and healthy controls (n = 38; m/f-20/18;age-62.4 ± 11.3yrs). A support vector machine ranking method is used to present the features specific to their dominance in sex and age group for Parkinson's diagnosis.

RESULTS

The sex-specific and age-dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD = 1.63) with the female-specific classifier, and 79.55% (SD = 1.58) with the old-age dependent classifier was observed in comparison to 75.76% (SD = 1.17) accuracy with the generalized classifier.

CONCLUSIONS

Combining the age and sex information proved to be encouraging in classification. A distinct set of features were observed to be dominating for higher classification accuracy in a different category of classification.

Classifying Parkinson's Disease Based on Acoustic Measures Using Artificial Neural Networks

In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson's Disease

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson's disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG-Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG-Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG-Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples

Background

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined.

Methods

In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation.

Results

Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms.

Conclusions

This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.