Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease

AFM signal model for dysarthric speech classification using speech biomarkers

Neurological disorders include various conditions affecting the brain, spinal cord, and nervous system which results in reduced performance in different organs and muscles throughout the human body. Dysarthia is a neurological disorder that significantly impairs an individual's ability to effectively communicate through speech. Individuals with dysarthria are characterized by muscle weakness that results in slow, slurred, and less intelligible speech production. An efficient identification of speech disorders at the beginning stages helps doctors suggest proper medications. The classification of dysarthric speech assumes a pivotal role as a diagnostic tool, enabling accurate differentiation between healthy speech patterns and those affected by dysarthria. Achieving a clear distinction between dysarthric speech and the speech of healthy individuals is made possible through the application of advanced machine learning techniques. In this work, we conducted feature extraction by utilizing the Amplitude and frequency modulated (AFM) signal model, resulting in the generation of a comprehensive array of unique features. A method involving Fourier-Bessel series expansion is employed to separate various components within a complex speech signal into distinct elements. Subsequently, the Discrete Energy Separation Algorithm is utilized to extract essential parameters, namely the Amplitude envelope and Instantaneous frequency, from each component within the speech signal. To ensure the robustness and applicability of our findings, we harnessed data from various sources, including TORGO, UA Speech, and Parkinson datasets. Furthermore, the classifier's performance was evaluated based on multiple measures such as the area under the curve, F1-Score, sensitivity, and accuracy, encompassing KNN, SVM, LDA, NB, and Boosted Tree. Our analyses resulted in classification accuracies ranging from 85 to 97.8% and the F1-score ranging between 0.90 and 0.97.

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS AND CONCLUSION

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

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

BACKGROUND

Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.

OBJECTIVE

This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest.

METHODS

This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group.

RESULTS

In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models.

CONCLUSIONS

This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

Monitoring the Effect of Levodopa Using Sustained Phonemes in Parkinson's Disease Patients

Parkinson's disease (PD) is a neurological disease identified by multiple symptoms, and levodopa is one of the most effective medications for treating the disease. To determine the dosage of levodopa, it is necessary to meet on a regular basis and observe motor function. The early detection and progression of the disease have been proposed using hypokinetic dysarthria. However, previous studies have not examined the effects of levodopa on speech rigorously and have provided inconsistent results. In this study, three sustained phonemes of PD patients were investigated for the effect of medication. A set of features characterizing vocal fold dynamics as well as the vocal tract coordinators were extracted from the sustained phonemes /of 28 PD patients during levodopa medication off and on states. All the features were statistically investigated and classified using a linear discriminant analysis (LDA) classifier. LDA classifier identified medication on from medication off based on the combined features from phoneme /a/, /o/ and /m/ with the accuracy=82.75% and F1-score=82.18%. Voice recording of PD patients during sustained phonemes /a/, /o/ and /m/ has the potential for identifying whether the patients are in On state or Off state of medication.Clinical Relevance- The outcomes of this study have the potential to monitor the effect and progress of levodopa on PD patients.

Voices from the past: The pioneering use of the phonograph in neurology

Since its discovery by the American inventor and industrialist Thomas Alva Edison (1847-1931) in 1877, the phonograph attracted much interest in the field of medicine. This article describes the earliest pioneering examples of the use of the phonograph in neurology. In France, the use of the phonograph for obtaining audio recordings of delusions and speech or language disturbances was first proposed by Victor Maurice Dupont (1857-1910) in 1889 and in Italy by the physician Gaetano Rummo (1853-1917), who had studied at La Salpêtrière under Jean-Martin Charcot (1825-1893). The applicability of the phonograph to the record of speech disturbances was illustrated in England by John Hughlings Jackson (1835-1911) and William Halse Rivers (1864-1922), and by William Hale White (1857-1949) and Cuthbert Hilton Golding-Bird (1848-1939) in 1891. Since then, audio recordings have been used rarely in neurology, a branch of medicine where the visual aspects dominate, to the extent that inspection can be enough to reach a definite clinical diagnosis. In the mid-20th century, the advent of audio and video recordings supplanted audio recordings alone, relegating them to a very marginal role.

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.

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.

Phonemes based detection of parkinson's disease for telehealth applications

Dysarthria is an early symptom of Parkinson’s disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan’s dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for 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.

Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease

This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.

Detecting Effect of Levodopa in Parkinson's Disease Patients Using Sustained Phonemes

BACKGROUND

Parkinson's disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture.

METHOD

This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM).

RESULTS

The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD-off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD-off versus PD-on achieved an AUC of 0.81.

CONCLUSION

Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.

Hybrid Model for Parkinson’s Disease Prediction

Parkinson’s is a chronic, progressive neurological disease with no known cause that affects the central nervous system of older people and compromises their movement. This disorder can impair daily aspects of people and therefore identify their existence early, helps in choosing treatments that can reduce the impact of the disease on the patient’s routine. This work aims to identify Parkinson’s traces through a voice recording replications database applied to a fuzzy neural network to identify their patterns and enable the extraction of knowledge about situations present in the data collected in patients. The results obtained by the hybrid model were superior to state of the art for the theme, proving that it is possible to perform hybrid models in the extraction of knowledge and the classification of behavioral patterns of high accuracy Parkinson’s.