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Mračková M, Mareček R, Mekyska J, Košťálová M, Rektorová I. Levodopa may modulate specific speech impairment in Parkinson's disease: an fMRI study. J Neural Transm (Vienna) 2024; 131:181-187. [PMID: 37943390 DOI: 10.1007/s00702-023-02715-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/22/2023] [Indexed: 11/10/2023]
Abstract
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.
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Affiliation(s)
- Martina Mračková
- First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's University Hospital Brno, Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University Brno, Brno, Czech Republic
| | - Radek Mareček
- Multimodal and Functional Neuroimaging Research Group, Central European Institute of Technology, CEITEC, Masaryk University Brno, Brno, Czech Republic
| | - Jiří Mekyska
- Department of Telecommunications, Brno University of Technology, Brno, Czech Republic
| | - Milena Košťálová
- Department of Neurology, Faculty of Medicine, Masaryk University and Faculty Hospital Brno, Brno, Czech Republic
| | - Irena Rektorová
- First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's University Hospital Brno, Brno, Czech Republic.
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University Brno, Brno, Czech Republic.
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Skibińska J, Hosek J. Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease. Heliyon 2023; 9:e21175. [PMID: 37908703 PMCID: PMC10613914 DOI: 10.1016/j.heliyon.2023.e21175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/07/2023] [Accepted: 10/17/2023] [Indexed: 11/02/2023] Open
Abstract
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.
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Affiliation(s)
- Justyna Skibińska
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno, 61600, Czechia
- Unit of Electrical Engineering, Tampere University, Kalevantie 4, Tampere, 33100, Finland
| | - Jiri Hosek
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno, 61600, Czechia
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Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. J Med Internet Res 2023; 25:e46105. [PMID: 37467031 PMCID: PMC10398366 DOI: 10.2196/46105] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 07/20/2023] Open
Abstract
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.
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Affiliation(s)
- Alper Idrisoglu
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
- School of Health Sciences, University of Skövde, Skövde, Sweden
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Faragó P, Ștefănigă SA, Cordoș CG, Mihăilă LI, Hintea S, Peștean AS, Beyer M, Perju-Dumbravă L, Ileșan RR. CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments. Bioengineering (Basel) 2023; 10:bioengineering10050531. [PMID: 37237601 DOI: 10.3390/bioengineering10050531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
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.
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Affiliation(s)
- Paul Faragó
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Sebastian-Aurelian Ștefănigă
- Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania
| | - Claudia-Georgiana Cordoș
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Laura-Ioana Mihăilă
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Sorin Hintea
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Ana-Sorina Peștean
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Iuliu Hatieganu" Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Michel Beyer
- Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland
- Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, CH-4123 Allschwil, Switzerland
| | - Lăcrămioara Perju-Dumbravă
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Iuliu Hatieganu" Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Robert Radu Ileșan
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Iuliu Hatieganu" Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland
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Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques. Sci Rep 2022; 12:22547. [PMID: 36581646 PMCID: PMC9800369 DOI: 10.1038/s41598-022-26644-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.
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6
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Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques. Diagnostics (Basel) 2022; 12:diagnostics12051033. [PMID: 35626189 PMCID: PMC9139946 DOI: 10.3390/diagnostics12051033] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/08/2022] [Accepted: 04/18/2022] [Indexed: 02/04/2023] Open
Abstract
Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy.
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Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi ME, Marín Valero M, Corvol JC, Eskofier B, Van Gyseghem JM, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Front Neurol 2022; 13:788427. [PMID: 35295840 PMCID: PMC8918525 DOI: 10.3389/fneur.2022.788427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/31/2022] [Indexed: 12/18/2022] Open
Abstract
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
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Affiliation(s)
- Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany
| | - Noémi Bontridder
- Centre de Recherches Information, Droit et Societe, University of Namur, Namur, Belgium
| | | | - Enrico Glaab
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | | | - Bjoern Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | - Jürgen Winkler
- Department of Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
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Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease. Parkinsonism Relat Disord 2022; 95:86-91. [DOI: 10.1016/j.parkreldis.2022.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/13/2021] [Accepted: 01/06/2022] [Indexed: 11/23/2022]
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10
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Tomer S, Khanna K, Gambhir S, Gambhir M. Comparison Analysis of GLCM and PCA on Parkinson's Disease Using Structural MRI. INTERNATIONAL JOURNAL OF INFORMATION RETRIEVAL RESEARCH 2022. [DOI: 10.4018/ijirr.289577] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Parkinson disease (PD) is a neurological disorder where the dopaminergic neurons experience deterioration. It is caused from the death of the dopamine neurons present in the substantia nigra i.e., the mid part of the brain. The symptoms of this disease emerge slowly, the onset of the earlier stages shows some non-motor symptoms and with time motor symptoms can also be gauged. Parkinson is incurable but can be treated to improve the condition of the sufferer. No definite method for diagnosing PD has been concluded yet. However, researchers have suggested their own framework out of which MRI gave better results and is also a non-invasive method. In this study, the MRI images are used for extracting the features. For performing the feature extraction techniques Gray Level Co-occurrence Matrix and Principal Component Analysis are performed and are analysed. Feature extraction reduces the dimensionality of data. It aims to reduce the feature of data by generating new features from the original one.
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Affiliation(s)
- Sanjana Tomer
- J. C. Bose University of Science and Technology, YMCA, India
| | - Ketna Khanna
- J. C. Bose University of Science and Technology, YMCA, India
| | - Sapna Gambhir
- J. C. Bose University of Science and Technology, YMCA, India
| | - Mohit Gambhir
- Innovation Cell, Ministry of Education, Faridabad, India
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Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC, Soar J, Palmer EE, Acharya UR. Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021). SENSORS 2021; 21:s21217034. [PMID: 34770340 PMCID: PMC8587636 DOI: 10.3390/s21217034] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Wanrong Hong
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Elizabeth E Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, NSW 2031, Australia
- School of Women's and Children's Health, University of New South Wales, Randwick, NSW 2031, Australia
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan
- Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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Meghraoui D, Boudraa B, Merazi-Meksen T, Gómez Vilda P. A novel pre-processing technique in pathologic voice detection: Application to Parkinson’s disease phonation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102604] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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13
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Hidalgo-De la Guía I, Garayzábal-Heinze E, Gómez-Vilda P, Martínez-Olalla R, Palacios-Alonso D. Acoustic Analysis of Phonation in Children With Smith-Magenis Syndrome. Front Hum Neurosci 2021; 15:661392. [PMID: 34149380 PMCID: PMC8209519 DOI: 10.3389/fnhum.2021.661392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Complex simultaneous neuropsychophysiological mechanisms are responsible for the processing of the information to be transmitted and for the neuromotor planning of the articulatory organs involved in speech. The nature of this set of mechanisms is closely linked to the clinical state of the subject. Thus, for example, in populations with neurodevelopmental deficits, these underlying neuropsychophysiological procedures are deficient and determine their phonation. Most of these cases with neurodevelopmental deficits are due to a genetic abnormality, as is the case in the population with Smith–Magenis syndrome (SMS). SMS is associated with neurodevelopmental deficits, intellectual disability, and a cohort of characteristic phenotypic features, including voice quality, which does not seem to be in line with the gender, age, and complexion of the diagnosed subject. The phonatory profile and speech features in this syndrome are dysphonia, high f0, excess vocal muscle stiffness, fluency alterations, numerous syllabic simplifications, phoneme omissions, and unintelligibility of speech. This exploratory study investigates whether the neuromotor deficits in children with SMS adversely affect phonation as compared to typically developing children without neuromotor deficits, which has not been previously determined. The authors compare the phonatory performance of a group of children with SMS (N = 12) with a healthy control group of children (N = 12) matched in age, gender, and grouped into two age ranges. The first group ranges from 5 to 7 years old, and the second group goes from 8 to 12 years old. Group differences were determined for two forms of acoustic analysis performed on repeated recordings of the sustained vowel /a/ F1 and F2 extraction and cepstral peak prominence (CPP). It is expected that the results will enlighten the question of the underlying neuromotor aspects of phonation in SMS population. These findings could provide evidence of the susceptibility of phonation of speech to neuromotor disturbances, regardless of their origin.
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Affiliation(s)
| | | | - Pedro Gómez-Vilda
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Daniel Palacios-Alonso
- Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Madrid, Spain
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An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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15
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Gómez A, Gómez P, Palacios D, Rodellar V, Nieto V, Álvarez A, Tsanas A. A Neuromotor to Acoustical Jaw-Tongue Projection Model With Application in Parkinson's Disease Hypokinetic Dysarthria. Front Hum Neurosci 2021; 15:622825. [PMID: 33790751 PMCID: PMC8005556 DOI: 10.3389/fnhum.2021.622825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
Aim The present work proposes the study of the neuromotor activity of the masseter-jaw-tongue articulation during diadochokinetic exercising to establish functional statistical relationships between surface Electromyography (sEMG), 3D Accelerometry (3DAcc), and acoustic features extracted from the speech signal, with the aim of characterizing Hypokinetic Dysarthria (HD). A database of multi-trait signals of recordings from an age-matched control and PD participants are used in the experimental study. Hypothesis: The main assumption is that information between sEMG and 3D acceleration, and acoustic features may be quantified using linear regression methods. Methods Recordings from a cohort of eight age-matched control participants (4 males, 4 females) and eight PD participants (4 males, 4 females) were collected during the utterance of a diadochokinetic exercise (the fast repetition of diphthong [aI]). The dynamic and acoustic absolute kinematic velocities produced during the exercises were estimated by acoustic filter inversion and numerical integration and differentiation of the speech signal. The amplitude distributions of the absolute kinematic and acoustic velocities (AKV and AFV) are estimated to allow comparisons in terms of Mutual Information. Results The regression results show the relationships between sEMG and dynamic and acoustic estimates. The projection methodology may help in understanding the basic neuromotor muscle activity regarding neurodegenerative speech in remote monitoring neuromotor and neurocognitive diseases using speech as the vehicular tool, and in the study of other speech-related disorders. The study also showed strong and significant cross-correlations between articulation kinematics, both for the control and the PD cohorts. The absolute kinematic variables presents an observable difference for the PD participants compared to the control group. Conclusion Kinematic distributions derived from acoustic analysis may be useful biomarkers toward characterizing HD in neuromotor disorders providing new insights into PD.
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Affiliation(s)
- Andrés Gómez
- Old Medical School, Medical School, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.,NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Pedro Gómez
- NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Daniel Palacios
- NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Escuela Técnica Superior de Ingeniería Informática-Universidad Rey Juan Carlos, Móstoles, Spain
| | - Victoria Rodellar
- NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Víctor Nieto
- NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Agustín Álvarez
- NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Athanasios Tsanas
- Old Medical School, Medical School, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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16
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Jeancolas L, Petrovska-Delacrétaz D, Mangone G, Benkelfat BE, Corvol JC, Vidailhet M, Lehéricy S, Benali H. X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech. Front Neuroinform 2021; 15:578369. [PMID: 33679361 PMCID: PMC7935511 DOI: 10.3389/fninf.2021.578369] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/18/2021] [Indexed: 01/18/2023] Open
Abstract
Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).
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Affiliation(s)
- Laetitia Jeancolas
- Paris Brain Institute-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Laboratoire SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France
| | | | - Graziella Mangone
- Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris, France
| | - Badr-Eddine Benkelfat
- Laboratoire SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France
| | - Jean-Christophe Corvol
- Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris, France
| | - Marie Vidailhet
- Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neuroradiology, Paris, France
| | - Habib Benali
- Department of Electrical & Computer Engineering, PERFORM Center, Concordia University, Montreal, QC, Canada
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17
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Detection of Parkinson’s disease from handwriting using deep learning: a comparative study. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00470-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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Hilbert spectrum analysis for automatic detection and evaluation of Parkinson’s speech. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102050] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Karan B, Sahu SS, Mahto K. Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2019.05.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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20
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Tracy JM, Özkanca Y, Atkins DC, Hosseini Ghomi R. Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease. J Biomed Inform 2019; 104:103362. [PMID: 31866434 DOI: 10.1016/j.jbi.2019.103362] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 12/10/2019] [Accepted: 12/17/2019] [Indexed: 10/25/2022]
Abstract
Voice technology has grown tremendously in recent years and using voice as a biomarker has also been gaining evidence. We demonstrate the potential of voice in serving as a deep phenotype for Parkinson's Disease (PD), the second most common neurodegenerative disorder worldwide, by presenting methodology for voice signal processing for clinical analysis. Detection of PD symptoms typically requires an exam by a movement disorder specialist and can be hard to access and inconsistent in findings. A vocal digital biomarker could supplement the cumbersome existing manual exam by detecting and quantifying symptoms to guide treatment. Specifically, vocal biomarkers of PD are a potentially effective method of assessing symptoms and severity in daily life, which is the focus of the current research. We analyzed a database of PD patient and non-PD subjects containing voice recordings that were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. The results are presented here and the limitations are discussed given the nature of the recordings. We note that our methodology only advances biomarker research and is not cleared for clinical use. Specifically, we demonstrate that conventional machine learning models applied to voice signals can be used to differentiate participants with PD who exhibit little to no symptoms from healthy controls. This work highlights the potential of voice to be used for early detection of PD and indicates that voice may serve as a deep phenotype for PD, enabling precision medicine by improving the speed, accuracy, accessibility, and cost of PD management.
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Affiliation(s)
- John M Tracy
- Member of DigiPsych Lab, University of Washington, Seattle, WA, USA
| | - Yasin Özkanca
- Electrical & Electronics Engineering, Ozyegin University, Istanbul, Turkey
| | - David C Atkins
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
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21
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Thies T, Mücke D, Lowit A, Kalbe E, Steffen J, Barbe MT. Prominence marking in parkinsonian speech and its correlation with motor performance and cognitive abilities. Neuropsychologia 2019; 137:107306. [PMID: 31857118 DOI: 10.1016/j.neuropsychologia.2019.107306] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 11/14/2019] [Accepted: 12/12/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Research suggests that people with Parkinson's disease (PwPD) do not only suffer from motor but also non-motor impairment. This interdisciplinary study investigated how prominence marking is influenced by problems on the motoric and cognitive level. MATERIALS AND METHODS We collected speech production data from 38 native German speakers: 19 PwPD (under medication) with a mild to moderate motor impairment, 13 males and 6 females (mean 66.2 years old, SD = 7.7), and 19 healthy age- and gender-matched control participants (mean 65.4 years old, SD = 9.3). Target words were produced in an accented and unaccented condition within a speech production task. The data were analyzed for intensity, syllable duration, F0 and vowel production. Furthermore, we assessed motor impairment and cognitive functions, i.e. working memory, task-switching, attention control and speed of information processing. RESULTS Both groups were able to mark prominence by increasing pitch, syllable duration and intensity and by adjusting their vowel production. Comparisons between PwPD and control participants revealed that the vowel space was smaller in PwPD even in mildly impaired speakers. Further, task-switching as an executive function, which was tested with the trail making test, was correlated with modulation of F0 and intensity in PwPD: the worse the task-switching performance, the stronger intensity and F0 were modulated (target overshoot). Moreover, motor impairment within the PwPD group was related to a decrease in the acoustic vowel space (target undershoot), which further resulted in a decrease in speech intelligibility and naturalness. This behaviour of target over- and undershoot indicates an inefficient way of prominence marking in PwPD with mildly affected speech. CONCLUSION PwPD with signs of mild dysarthria did not differ from the control speakers with respect to their strategies of prominence marking. However, only the PwPD overused F0 and intensity in prominent positions. Overmodulation of F0 and intensity was correlated with the patient's task-switching ability and reflected abnormalities in the regulatory mechanism for expressing prosodic prominence. This is the first study to report a link between cognitive skills and speech production at the phonetic level in PwPD.
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Affiliation(s)
- Tabea Thies
- University of Cologne, Faculty of Arts and Humanities, IfL - Phonetics, Herbert-Lewin-Str. 6, 50931, Köln, Germany; University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Kerpener Str. 62, 50937, Köln, Germany.
| | - Doris Mücke
- University of Cologne, Faculty of Arts and Humanities, IfL - Phonetics, Herbert-Lewin-Str. 6, 50931, Köln, Germany.
| | - Anja Lowit
- University of Strathclyde, School of Psychological Sciences and Health, 40 George Street, G1 1QE, Glasgow, Scotland, UK.
| | - Elke Kalbe
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Medical Psychology, Neuropsychology and Gender Studies & Center for Neuropsychological Diagnostics and Intervention (CeNDI), Kerpener Str. 62, 50937, Köln, Germany.
| | - Julia Steffen
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Kerpener Str. 62, 50937, Köln, Germany.
| | - Michael T Barbe
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Kerpener Str. 62, 50937, Köln, Germany.
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22
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Karlsson F, Hartelius L. How Well Does Diadochokinetic Task Performance Predict Articulatory Imprecision? Differentiating Individuals with Parkinson’s Disease from Control Subjects. Folia Phoniatr Logop 2019; 71:251-260. [DOI: 10.1159/000498851] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 02/06/2019] [Indexed: 11/19/2022] Open
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23
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Brabenec L, Klobusiakova P, Barton M, Mekyska J, Galaz Z, Zvoncak V, Kiska T, Mucha J, Smekal Z, Kostalova M, Rektorova I. Non-invasive stimulation of the auditory feedback area for improved articulation in Parkinson's disease. Parkinsonism Relat Disord 2019; 61:187-192. [DOI: 10.1016/j.parkreldis.2018.10.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/05/2018] [Accepted: 10/09/2018] [Indexed: 01/24/2023]
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24
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Changes in Phonation and Their Relations with Progress of Parkinson’s Disease. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8122339] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Hypokinetic dysarthria, which is associated with Parkinson’s disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients’ voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11–26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/.
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25
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Mekyska J, Galaz Z, Kiska T, Zvoncak V, Mucha J, Smekal Z, Eliasova I, Kostalova M, Mrackova M, Fiedorova D, Faundez-Zanuy M, Solé-Casals J, Gomez-Vilda P, Rektorova I. Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson's Disease. Cognit Comput 2018; 10:1006-1018. [PMID: 30595758 PMCID: PMC6294819 DOI: 10.1007/s12559-018-9575-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 06/13/2018] [Indexed: 12/27/2022]
Abstract
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.
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Affiliation(s)
- Jiri Mekyska
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Zoltan Galaz
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Tomas Kiska
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Vojtech Zvoncak
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Jan Mucha
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Zdenek Smekal
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Ilona Eliasova
- First Department of Neurology, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
| | - Milena Kostalova
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
- Department of Neurology, Faculty Hospital and Masaryk University, Jihlavska 20, 63900 Brno, Czech Republic
| | - Martina Mrackova
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
| | - Dagmar Fiedorova
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
| | - Marcos Faundez-Zanuy
- Escola Superior Politecnica, Tecnocampus, Avda. Ernest Lluch 32, 08302 Mataro, Barcelona Spain
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic – Central University of Catalonia, Perot Rocaguinarda 17, 08500 Vic, Catalonia Spain
| | - Pedro Gomez-Vilda
- Neuromorphic Processing Laboratory (NeuVox Lab), Center for Biomedical Technology, Universidad Politécnica de Madrid Campus de Montegancedo, s/n, 28223, Pozuelo de Alarcón, Madrid Spain
| | - Irena Rektorova
- First Department of Neurology, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
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