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Literatura académica sobre el tema "Early Detection of Parkinson's Disease"

Autor: Grafiati

Publicado: 12 de abril de 2025

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Artículos de revistas sobre el tema "Early Detection of Parkinson's Disease"

Kiruthika, S. "The Parkinson’s Puzzle: Early Detection & Diagnosis". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, n.º 01 (22 de enero de 2025): 1–9. https://doi.org/10.55041/ijsrem41001.

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Parkinson's disease is a neurological disorder that affects movement and becomes progressively worse over time. Currently, there is no cure for the disease, but early detection and appropriate management can significantly improve the patient's quality of life. This project presents a web-based application designed to detect Parkinson's disease based on the analysis of audio recordings of patient's voices. The application utilizes a machine learning model that has been trained on a dataset consisting of audio recordings from both Parkinson's disease patients and healthy individuals. The web application extracts relevant features from the audio recordings, employing machine learning algorithms to predict the probability of Parkinson's disease. The machine learning models used in this research are Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, MLP Classifier, Gaussian NB, XGB Classifier, Random Forest Classifier, and Cat Boost Classifier and stacked three models (XG Boost, Random Forest, and Cat Boost) using Stacking CV Classifier out of which the model with the highest performance will be chosen to develop the web application. Keywords: Parkinson’s Disease, Web Application, Ensemble Learning, Stacked Model

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N., Chandana, Divya C. D. y Radhika A. D. "A Review on Parkinsons Disease Detection". Applied and Computational Engineering 2, n.º 1 (22 de marzo de 2023): 760–65. http://dx.doi.org/10.54254/2755-2721/2/20220675.

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Parkinson's Disease affects hundreds of thousands of people . Even with today's many sciences and advances, detecting this disorder in early stage remains a challenge. The detection of any neurological disorder could be critical. Various tools and techniques are now available on a global scale. These strategies are primarily built on mobile ,web-based utility .These strategies are also user-friendly because disease caregivers can use them while sitting at home and can display disease progression. Clinicians and researchers who want to conduct research also use these applications to display disease progressions. According to the current information review, certain algorithms have been used to obtain good results, In this case, the difficulty is defining the utmost effective classifier for Parkinsons Disease detection. Also emphasises a organized assessment of several styles of programmes employed globally for Parkinson's disease detection , prognosis, proposes a Parkinson's disease detection system that is entirely based on mobile and internet utility.

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Irin Akter Liza, Ekramul Hasan, Md Musa Haque, Shah Foysal Hossain, Md Al Amin y Shahriar Ahmed. "Predictive Modeling and Early Detection of Parkinson's Disease Using Machine Learning". Journal of Medical and Health Studies 5, n.º 4 (12 de noviembre de 2024): 97–107. http://dx.doi.org/10.32996/jmhs.2024.5.4.12.

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Parkinson's disease is a progressive neurodegenerative disorder that impacts millions of citizens in the USA, mainly targeting the motor system and causing debilitating symptoms such as rigidity, tremors, and bradykinesia. The diagnosis of Parkinson's disease is presently heavily dependent on clinical evaluations and neurological examinations, targeting the detection of motor dysfunction. The principal aim of this study was to use machine learning as a means for early detection and prediction of Parkinson's disease. The dataset utilized for this study was the Parkinson’s Disease Dataset, retrieved from the UCI Machine Learning Repository, which included comprehensive biomedical voice measurements from a cohort of individuals, 23 of whom are diagnosed with Parkinson’s disease and 8 who are healthy controls. This dataset included a set of features extracted from voice recordings. These include parameters like fundamental frequency (pitch), amplitude variation, jitter, shimmer, and several phonation-related measures known to reflect early vocal impairments associated with the disease in question. This research project deployed three credible and proven algorithms, namely, logistic regression, random forest, and the Support Vector Machines. Besides, this study employed a combination of metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, which are more holistic toward the model performance. According to the metric performance results of the three models, several key insights were drawn into the early detection of Parkinson's Disease. Particularly, it was clear that the Random Forest model had superior accuracy and was the most reliable in classifying positive cases and healthy patients, which could be that this model turned out to be most reliable in an early detection setting. In that respect, predictive modeling in Parkinson's Disease is a capability frontier that has the potential to make much difference in clinical decision-making. Supported by Machine Learning algorithms, clinicians can trace the minute patterns in patient data, which are not easily visible through any other diagnostic means. In such cases, early detection of Parkinson's Disorder with vocal biomarkers or motor assessment may afford healthcare professionals opportunities for early interventions that might retard the disease process.

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Kavitha Soppari, Bharath Vupperpally, Harshini Adloori, Kumar Agolu y Sujith kasula. "AI-powered early detection of neurological disease: Parkinson's disease". International Journal of Science and Research Archive 14, n.º 1 (30 de enero de 2025): 278–82. https://doi.org/10.30574/ijsra.2025.14.1.0041.

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Parkinson's disease (PD), a neurological illness that gradually compromises motor abilities. Tremors, muscle rigidity, and bradykinesia (slowness of movement) are symptoms of PD. Effective therapy depends on a prompt and accurate diagnosis, yet traditional diagnostic methods can be laborious and subjective. The goal of this study is to create a machine learning-based model that uses clinical information, like vocal characteristics, to detect Parkinson's disease. Through the use of advanced machine learning algorithms and the extraction of important data patterns, the project hopes to develop a trustworthy diagnostic tool that will help physicians identify Parkinson's disease (PD) early on, facilitating quicker interventions and improved patient care.

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S, Rohan, R. Subrahmanya y Vignesh M. "Parkinson’s Disease Detection using YOLO Algorithm". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 12 (30 de diciembre de 2024): 1–9. https://doi.org/10.55041/ijsrem40409.

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Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non- motor symptoms, which significantly impact patients' quality of life. Early and accurate diagnosis is important for effective management and treatment planning. Recent advancements in deep learning techniques, particularly with the You Only Look Once (YOLO) algorithm, have shown promise in enhancing medical imaging analysis and automated diagnosis. This study explores the use of YOLO, a real-time object detection algorithm, for identifying Parkinson’s disease-related abnormalities in medical images such as MRI and PET scans. The YOLO framework is modified and trained to detect specific biomarkers and structural changes associated with PD in imaging data, which allows for rapid and accurate identification of disease patterns. Results demonstrate that YOLO’s high-speed detection and localization capabilities make it suitable for processing large volumes of medical data, offering an efficient and cost- effective solution for PD screening. This application of YOLO in PD detection may improve early diagnosis, aid in monitoring disease progression, and support the development of individualized treatment strategies. Further research is needed to refine the model for greater accuracy and clinical applicability across diverse patient populations. Keywords—YOLO, F1 Score, Parkinson’s Disease, Convolutional Neural Networks, Neurodegenerative disorder, Parkinson's Progression Markers Initiative (PPMI).

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Samita Ganveer, Himani Bire, Rutuja Deshmukh, Shweta S. Salunkhe,. "Early Detection of Parkinson’s Disease Using Machine Learning". Journal of Electrical Systems 20, n.º 2 (4 de abril de 2024): 2255–66. http://dx.doi.org/10.52783/jes.1992.

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A hallmark of Parkinson's disease is the degeneration of dopaminergic neurons in the midbrain's substantia nigra pars compacta. However, machine learning is needed to design and implement early Parkinson's disease detection. by using machine learning methods such as CNN and SVM, which can reliably identify voice signals and spiral images to identify early indications of Parkinson's disease. Using machine learning on handwriting, tremor, and gait datasets, the method addresses the shortcomings of individual analyses for a more complete diagnosis solution by investigating relationships between symptoms. This increases accuracy. When voice and spiral drawing data were combined, Parkinson's disease diagnosis accuracy showed promise, with the machine learning model successfully differentiating between unaffected patients and those who were affected. This observation suggests a viable path for precise Parkinson's disease diagnosis: an integrated method that combines machine learning techniques with data from spiral drawings and voice.

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Adekunle, Abiona Akeem, Oyerinde Bolarinwa Joseph y Ajinaja Micheal Olalekan. "Early Parkinson's Disease Detection Using by Machine Learning Approach". Asian Journal of Research in Computer Science 16, n.º 2 (9 de junio de 2023): 36–45. http://dx.doi.org/10.9734/ajrcos/2023/v16i2337.

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Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects movement and motor skills. Early diagnosis and treatment of Parkinson's disease are crucial for improving patient outcomes; however, traditional diagnostic methods are time-consuming and subject to observer bias. This study aims to use a machine learning model for the detection of Parkinson's disease. The model will be trained on a public repository dataset of biomedical voice measurements from individuals with and without Parkinson's disease and its performance will be evaluated in terms of accuracy and precision. The results of this study have the potential to revolutionize the diagnosis of Parkinson's disease by providing a fast, non-invasive, and reliable diagnostic tool. The study's results could also have implications for the development of similar diagnostic tools for other neurodegenerative disorders.

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Montgomery, Erwin B. "Olfaction and early detection of Parkinson's disease". Annals of Neurology 57, n.º 1 (2004): 157. http://dx.doi.org/10.1002/ana.20354.

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I, Kalaiyarasi, Amudha P y Sivakumari S. "Parkinson\'s Disease Detection Using Deep Learning Technique". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de mayo de 2023): 1789–96. http://dx.doi.org/10.22214/ijraset.2023.51916.

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Abstract: Parkinson’s Disease is a degenerative nervous system ailment primarily impacting middle-aged and older persons. Tremors, stiffness of the muscles, and slow, clumsy movement mark it. Parkinson's Disease is believed to be caused by genetic and environmental factors, while its precise cause is yet unclear. Levodopa can aid patients' quality of life and manage their symptoms, but there is no proven treatment for Parkinson's Disease. A unique deep-learning approach is developed to determine whether a person has PD based on premotor traits. This study has looked explicitly at several signs to identify PD at an early stage using spiral drawing. Measuring the changes in the handwritten spiral drawing allows for the early and accurate diagnosis of PD. Deep learning algorithms have been used to track the development of the illness and the effectiveness of treatment in PD patients in addition to diagnosis and prediction. By utilizing efficient treatments and medications, the findings will showcase how early illness detection can enhance a patient's life expectancy and enable them to live peacefully. One commonly utilized deep learning technique in PD research is Convolutional Neural Networks (CNNs). This work uses Modified Convolutional Neural Networks (MCNN) to predict the normal and abnormal of Parkinson's Disease. The complete model's performance after being trained on data from 36 patients was 96% overall accuracy, with average recall, precision, and f1 scores of 96.73%, 96.05%, and 96%, respectively.

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Generaldo Maylem, Genica Lynne Maylem, Isaac Angelo M. Dioses, Loida Hermosura, James Bryan Tababa, Aldrin Bryan Tababa, Marc Zenus Labuguen y Dave Miracle Cabanilla. "Speech-based biomarkers for Parkinson’s disease detection and classification using AI Approach". World Journal of Advanced Research and Reviews 25, n.º 2 (28 de febrero de 2025): 2127–33. https://doi.org/10.30574/wjarr.2025.25.2.0595.

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Parkinson's disease (PD) is a progressive neurological condition that impairs motor and speech function. Early and precise detection is critical for prompt intervention and illness treatment. This work applies machine learning approaches to classify Parkinson's disease using speech biomarkers collected from voice recordings. The dataset includes a variety of acoustic parameters that capture speech anomalies often seen in people with Parkinson's disease. The Chi-Square (Chi2) approach was used to pick the most important predictors, which improved model performance and reduced computational complexity. The fine K-Nearest Neighbors (KNN) classifier was implemented, achieving a validation accuracy of 74.7%. The model demonstrated a moderate ability to distinguish between Parkinson’s and non-Parkinson’s cases, as indicated by an area under the curve (AUC) score of 0.7421. However, the confusion matrix revealed challenges in misclassification, with false positives leading to potential unnecessary medical evaluations and false negatives resulting in missed diagnoses. This study highlights the potential of machine learning in Parkinson’s detection while emphasizing the need for further refinement to enhance classification accuracy

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Tesis sobre el tema "Early Detection of Parkinson's Disease"

Figueiredo, Isabel De. "Early Detection of Parkinson's Disease through Microfluidics and Ion Mobility - Mass Spectrometry Integration". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASF070.

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L'alpha-synucléine est un biomarqueur crucial pour la maladie de Parkinson, mais sa détection précoce est difficile en raison de sa faible abondance et de la nature intrinsèquement désordonnée de la protéine. Le développement de méthodes de diagnostic précoces repose fortement sur la compréhension et la différenciation des caractéristiques structurales de l'alpha-synucléine native par rapport à ses formes pathologiques, ces variations offrant des informations précieuses sur le début et la progression de la maladie. Cette thèse de doctorat examine le paysage conformationnel de l'alpha-synucléine et explore les techniques permettant de capturer et de concentrer cette protéine sans altérer sa structure. Deux types de dispositifs microfluidiques sont présentés : le premier intègre un module de micro-immunopurification optimisé pour la capture de l'alpha-synucléine et un module de micro-chromatographie d'exclusion de taille conçu pour le déssalement et l'échange de tampon pour être détecté par Spectrométrie de Masse couplée avec la Mobilité Ionique. De plus, une puce intégrée 2-en-1 combine ces deux modules en une seule plateforme, simplifiant le processus expérimental pour une efficacité et une précision accrues dans l'analyse de l'alpha-synucléine. Le couplage de ces dispositifs microfluidiques avec la Spectrométrie de Masse et la Mobilité Ionique permet la caractérisation structurale de l'alpha-synucléine, contribuant ainsi au développement de méthodes de diagnostic précoces en permettant la différenciation des abondances des conformères entre les formes natives et pathologiques de la protéine
Alpha-synuclein is a critical biomarker for Parkinson's disease, however its early detection is challenging due to its low abundance and intrinsically disordered protein nature. The development of early diagnostic methods relies heavily on understanding and differentiating the structural characteristics of native alpha-synuclein versus its pathological forms, as these variations provide valuable insights into disease onset and progression. This Ph.D. thesis, investigates the conformational landscape of alpha-synuclein and explores techniques to capture and concentrate this protein without disrupting its structure. Two types of microfluidic devices are presented: the first device integrates a micro-immunopurification module optimized for alpha-synuclein capture and a micro-size exclusion chromatography module designed for desalting and buffer exchange to facilitate coupling with Ion Mobility-Mass Spectrometry. Additionally, an integrated 2-in-1 chip combines these modules into a single platform, streamlining the workflow for enhanced efficiency and accuracy in alpha-synuclein analysis. The coupling of these microfluidic devices with the Ion Mobility-Mass Spectrometry advances the structural characterization of alpha-synuclein, contributing to the development of early diagnostic methods by enabling the differentiation between native and pathological forms of the protein

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Filali, razzouki Anas. "Deep learning-based video face-based digital markers for early detection and analysis of Parkinson disease". Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAS002.

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Cette thèse vise à développer des biomarqueurs numériques robustes pour la détection précoce de la maladie de Parkinson (MP) en analysant des vidéos faciales afin d'identifier les changements associés à l'hypomimie. Dans ce contexte, nous introduisons de nouvelles contributions à l'état de l'art : l'une fondée sur l'apprentissage automatique superficiel et l'autre fondée sur l'apprentissage profond. La première méthode utilise des modèles d'apprentissage automatique qui exploitent des caractéristiques faciales extraites manuellement, en particulier les dérivés des unités d'action faciale (AUs). Ces modèles intègrent des mécanismes d'interprétabilité qui permettent d'expliquer leur processus de décision auprès des parties prenantes, mettant en évidence les caractéristiques faciales les plus distinctives pour la MP. Nous examinons l'influence du sexe biologique sur ces biomarqueurs numériques, les comparons aux données de neuroimagerie et aux scores cliniques, et les utilisons pour prédire la gravité de la MP. La deuxième méthode exploite l'apprentissage profond pour extraire automatiquement des caractéristiques à partir de vidéos faciales brutes et des données de flux optique en utilisant des modèles fondamentaux basés sur les Vision Transformers pour vidéos. Pour pallier le manque de données d'entraînement, nous proposons des techniques avancées d'apprentissage par transfert adaptatif, en utilisant des modèles fondamentaux entraînés sur de grands ensembles de données pour la classification de vidéos. De plus, nous intégrons des mécanismes d'interprétabilité pour établir la relation entre les caractéristiques extraites automatiquement et les AUs faciales extraites manuellement, améliorant ainsi la clarté des décisions des modèles. Enfin, nos caractéristiques faciales générées proviennent à la fois de données transversales et longitudinales, ce qui offre un avantage significatif par rapport aux travaux existants. Nous utilisons ces enregistrements pour analyser la progression de l'hypomimie au fil du temps avec ces marqueurs numériques, et sa corrélation avec la progression des scores cliniques. La combinaison des deux approches proposées permet d'obtenir une AUC (Area Under the Curve) de classification de plus de 90%, démontrant l'efficacité des modèles d'apprentissage automatique et d'apprentissage profond dans la détection de l'hypomimie chez les patients atteints de MP à un stade précoce via des vidéos faciales. Cette recherche pourrait permettre une surveillance continue de l'hypomimie en dehors des environnements hospitaliers via la télémédecine
This thesis aims to develop robust digital biomarkers for early detection of Parkinson's disease (PD) by analyzing facial videos to identify changes associated with hypomimia. In this context, we introduce new contributions to the state of the art: one based on shallow machine learning and the other on deep learning.The first method employs machine learning models that use manually extracted facial features, particularly derivatives of facial action units (AUs). These models incorporate interpretability mechanisms that explain their decision-making process for stakeholders, highlighting the most distinctive facial features for PD. We examine the influence of biological sex on these digital biomarkers, compare them against neuroimaging data and clinical scores, and use them to predict PD severity.The second method leverages deep learning to automatically extract features from raw facial videos and optical flow using foundational models based on Video Vision Transformers. To address the limited training data, we propose advanced adaptive transfer learning techniques, utilizing foundational models trained on large-scale video classification datasets. Additionally, we integrate interpretability mechanisms to clarify the relationship between automatically extracted features and manually extracted facial AUs, enhancing the comprehensibility of the model's decisions.Finally, our generated facial features are derived from both cross-sectional and longitudinal data, which provides a significant advantage over existing work. We use these recordings to analyze the progression of hypomimia over time with these digital markers, and its correlation with the progression of clinical scores.Combining these two approaches allows for a classification AUC (Area Under the Curve) of over 90%, demonstrating the efficacy of machine learning and deep learning models in detecting hypomimia in early-stage PD patients through facial videos. This research could enable continuous monitoring of hypomimia outside hospital settings via telemedicine

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Taleb, Catherine. "Parkinson's desease detection by multimodal analysis combining handwriting and speech signals". Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT039.

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La maladie de Parkinson (MP) est un trouble neurologique causé par une diminution du niveau de dopamine dans le cerveau. Cette maladie est caractérisée par des symptômes moteurs et non moteurs qui s'aggravent avec le temps. Aux stades avancés de la maladie de Parkinson, le diagnostic clinique est clair. Cependant, dans les premiers stades, lorsque les symptômes sont souvent incomplets ou subtils, le diagnostic devient difficile et, parfois, le sujet peut rester non diagnostiqué. En outre, il n'existe pas de méthodes efficaces et fiables permettant d'établir avec certitude un diagnostic précoce du MP. La difficulté de la détection précoce est une forte motivation pour les outils d'évaluation informatisés/outils d'aide à la décision/instruments de test qui peuvent aider au diagnostic précoce et à la prédiction de la progression de la maladie de Parkinson. La détérioration de l'écriture et la déficience vocale peuvent être l'un des premiers indicateurs de l'apparition de la maladie. Selon la documentation examinée, un modèle indépendant du langage pour détecter la maladie de Parkinson à l'aide de signaux multimodaux n'a pas été suffisamment étudié. L'objectif principal de cette thèse est de construire un système multimodal indépendant du langage pour évaluer les troubles moteurs chez les patients atteints de la maladie de Parkinson à un stade précoce, basé sur des signaux combinés d'écriture et de parole, en utilisant des techniques d'apprentissage automatique. Dans ce but et en raison de l'absence d'un ensemble de données multimodales et multilingues, une telle base de données, également répartie entre les témoins et les patients atteints de la maladie de Parkinson, a d'abord été construite. La base de données comprend des enregistrements de l'écriture, de la parole et des mouvements oculaires recueillis auprès de patients témoins et de patients atteints de la maladie de Parkinson en deux phases (avec médication et sans médication). Dans cette thèse, nous nous sommes concentrés sur l'analyse de l'écriture et de la parole, où les patients atteints de la maladie de Parkinson ont été étudiés avec médication.Des modèles indépendants du langage pour la détection de la maladie de Parkinson basés sur les caractéristiques de l'écriture ont été construits ; deux approches ont été envisagées, étudiées et comparées : une approche classique d'extraction et de classification des caractéristiques et une approche d'apprentissage approfondi. Les deux approches ont permis d'atteindre une précision de classification d'environ 97 %. Un classificateur SVM multi-classes pour la détection des étapes basées sur les caractéristiques de l'écriture a été construit. Les performances obtenues n'étaient pas satisfaisantes par rapport aux résultats obtenus pour la détection de MD en raison des nombreux obstacles rencontrés.Un autre ensemble de caractéristiques acoustiques indépendantes de la langue et de la tâche a été construit pour évaluer les troubles moteurs chez les patients atteints de la maladie de Parkinson. Nous avons réussi à construire un modèle SVM indépendant du langage pour le diagnostic de la MP par analyse vocale avec une précision de 97,62%. Enfin, un système multimodal indépendant du langage pour la détection de la maladie de Parkinson en combinant l'écriture et les signaux vocaux a été mis au point, où le modèle SVM classique et les modèles d'apprentissage profond ont tous deux été analysés. Une précision de classification de 100 % est obtenue lorsque des caractéristiques artisanales des deux modalités sont combinées et appliquées au SVM. Malgré les résultats encourageants obtenus, il reste encore du travail à faire avant de mettre notre modèle multimodal de détection de la MP en usage clinique en raison de certaines limitations inhérentes à cette thèse
Parkinson’s disease (PD) is a neurological disorder caused by a decreased dopamine level on the brain. This disease is characterized by motor and non-motor symptoms that worsen over time. In advanced stages of PD, clinical diagnosis is clear-cut. However, in the early stages, when the symptoms are often incomplete or subtle, the diagnosis becomes difficult and at times, the subject may remain undiagnosed. Furthermore, there are no efficient and reliable methods capable of achieving PD early diagnosis with certainty. The difficulty in early detection is a strong motivation for computer-based assessment tools/decision support tools/test instruments that can aid in the early diagnosing and predicting the progression of PD.Handwriting’s deterioration and vocal impairment may be ones of the earliest indicators for the onset of the illness. According to the reviewed literature, a language independent model to detect PD using multimodal signals has not been enough addressed. The main goal of this thesis is to build a language independent multimodal system for assessment the motor disorders in PD patients at an early stage based on combined handwriting and speech signals, using machine learning techniques. For this purpose and due to the lack of a multimodal and multilingual dataset, such database that is equally distributed between controls and PD patients was first built. The database includes handwriting, speech, and eye movements’ recordings collected from control and PD patients in two phases (“on-state” and “off-state”). In this thesis we focused on handwriting and speech analysis, where PD patients were studied in their “on-state”.Language-independent models for PD detection based on handwriting features were built; where two approaches were considered, studied and compared: a classical feature extraction and classifier approach and a deep learning approach. Approximately 97% classification accuracy was reached with both approaches. A multi-class SVM classifier for stage detection based on handwriting features was built. The achieved performance was non-satisfactory compared to the results obtained for PD detection due to many obstacles faced.Another language and task-independent acoustic feature set for assessing the motor disorders in PD patients was built. We have succeeded to build a language independent SVM model for PD diagnosis through voice analysis with 97.62% accuracy. Finally, a language independent multimodal system for PD detection by combining handwriting and voice signals was built, where both classical SVM model and deep learning models were both analyzed. A classification accuracy of 100% is obtained when handcrafted features from both modalities are combined and applied to the SVM. Despite the encouraging results obtained, there is still some works to do before putting our PD detection multimodal model into clinical use due to some limitations inherent to this thesis

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Munder, Tonia [Verfasser]. "Investigation of early histopathological changes in rodent models of Alzheimer's Disease, Parkinson's Disease and CADASIL : brain magnet resonance elastography for early disease detection and staging correlated to histopathology and analysis of neurogenesis and cell survival / Tonia Munder". Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1160514887/34.

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Munder, Tonia Laura [Verfasser]. "Investigation of early histopathological changes in rodent models of Alzheimer's Disease, Parkinson's Disease and CADASIL : brain magnet resonance elastography for early disease detection and staging correlated to histopathology and analysis of neurogenesis and cell survival / Tonia Munder". Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1160514887/34.

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Konstantopoulos, Konstantinos. "Dysarthria in early Parkinson's disease". Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/10055767/.

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The aim of the present study was threefold. First, to examine the incidence of dysarthria in patients in the beginning of Parkinson's disease by using a standardised test (Frenchay Dysarthria Assessment/FDA) and an intelligibility assessment tool. Second, to identify differences in speech and in measures of phonation between the Parkinsonian group and a matched control geriatric group using the FDA and electrolaryngography. Finally, to identify the effect of medication on speech and phonation in the dysarthric Parkinsonian group. The results showed that 8 out of 12 (66%) Parkinsonian subjects exhibited lower scores in the FDA compared to controls. Qualitative differences between the two groups were found in the isolated movements of the articulators but not in running speech and speech intelligibility. An improvement in the FDA scoring was found 3-3.5 months after medication. This improvement focused on the areas of tongue and lips and was accompanied with significant increases in intelligibility. No differences in measures of phonation were found either between the two groups or in the same group after medication. The above results suggest that in the beginning of Parkinson's disease, dysarthria is expressed as slowness and may be related to the primary diagnostic symptom of bradykinesia. Due to the small sample and the lack of dosage control, the significance of these findings appears to be inconclusive and warrants further investigation. Future research should employ instrumental quantitative measures on isolated movements of the articulators that may correlate with running speech and will aim to find clinical markers of speech in the diagnosis of Parkinson's disease.

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Kudlicka, Aleksandra Katarzyna. "Executive functioning in early stage Parkinson's disease". Thesis, Bangor University, 2013. https://research.bangor.ac.uk/portal/en/theses/executive-functioning-in-early-stage-parkinsons-disease(4985b570-fd51-48ba-8c39-f377b5e2edf0).html.

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Background: Cognitive decline is commonly reported in Parkinson’s disease (PD), with some deficits evident even at the onset of PD. Executive functions (EF) are extensively studied in PD and emerge as the domain involving the most profound deficits. Nevertheless, there are some inconsistencies in the literature with regard to the exact pattern of executive deficits and their impact on everyday life in PD. The aim of the literature review presented in this thesis was to synthesise and clarify existing research evidence on EF in early stage PD, and to explore what are the possible factors affecting the consistency of research findings. The empirical studies had three distinct aims: to clarify the pattern of EF deficits in PD; to determine how accurately PwPD appraise potential EF-related difficulties; and to identify how executive deficits impact on people with PD (PwPD) and their families. Method: Studies of EF in PD were systematically reviewed and the findings were synthesised in a series of meta-analyses. Three empirical studies drew on cross-sectional data collected from PwPD and their caregivers, and from healthy older controls. Sixty-five PwPD in mild to moderate stages of PD completed an assessment of EF, awareness, quality of life, and health status, and 43 healthy older controls completed assessment of EF and awareness. Fifty caregivers of PwPD rated the EF of the PwPD and their own burden associated with caring for a PwPD. A sub-group of 34 PwPD, identified as having potential EF deficits, completed a more extensive neuropsychological assessment of executive abilities. Results: The systematic review included 33 studies of EF in early stage PD, and metaanalysis of data from 5 commonly-used tests of EF revealed consistent evidence for executive deficits. The review suggested that the consistency of the research evidence may be improved by more precision in defining EF and more careful selection and interpretation of EF measures. A data-driven analysis examining the pattern of EF impairment distinguished differences between two groups of standard tests of EF, with attentional control tests more frequently compromised than abstract thinking in early stage PD. PwPD were found to be accurate when making general evaluative judgments about their own functioning, but in specific tasks PwPD with executive deficits overestimated their performance in comparison to PwPD without EF deficits and healthy controls. EF-related behavioural difficulties were shown to impact on subjective quality of life in PwPD and on burden in their caregivers. Conclusions: The results of this thesis suggest that EF-related difficulties are frequently present in early stage PD, with attentional control aspects of EF particularly affected, that it may be difficult for PwPD to accurately appraise their own ability to carry out specific activities, and that EF-related difficulties have a significant impact on quality of life in PwPD and their families. A thorough understanding of executive deficits in PD is important in the provision of adequate person-centred care for PwPD and their family members, and could help to inform the development of PD-specific rehabilitative interventions aimed at reducing activity limitation and restrictions on social participation and supporting PwPD in living well with the condition.

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Pursiainen, V. (Ville). "Autonomic dysfunction in early and advanced Parkinson's disease". Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514283888.

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Abstract Parkinson's disease (PD) is known to affect both the extrapyramidal system and the autonomic nervous system even in the early phases of the disease. This study was designed to evaluate cardiovascular autonomic regulation in early PD by measuring heart rate (HR) variability from 24-hour ECG recordings. The dynamics of blood pressure (BP), HR and sweating in patients with and without wearing-off were assessed during clinical observations after a morning dose of levodopa. In patients with wearing-off the tests were repeated after selegiline withdrawal. The power spectral components of HR variability and the SD1 value of the Poincaré analysis that quantifies the short-term beat-to-beat variability were suppressed at night in the PD patients. During the daytime only the SD1 of the Poincaré was suppressed. The results indicate impairment of parasympathetic cardiovascular regulation in untreated patients with PD. The dysfunction was more pronounced at night and in patients with more severe PD. The patients with wearing-off had fluctuation of BP during the observation period, BP increasing when the motor performance worsened and vice versa (p < 0.001). The patients without wearing-off did not show fluctuation of BP. Sweating increased during the observation period, and reached its maximum level at the time of the highest UPDRS motor score phase (off-stage) in patients with wearing-off, but in the patients without wearing-off no changes in sweating were observed. Sweating of the hands was significantly higher in PD patients with motor fluctuations than in those without. Selegiline withdrawal decreased systolic BP significantly during the on-stage in a supine position as well as during the orthostatic test. The initial drop of BP in the orthostatic test was significantly smaller after selegiline withdrawal. The HR and sweating remained unaffected. The results show that the autonomic nervous system is affected in the early phases of PD. The dysfunction becomes more pronounced with the disease progression. Wearing-off type motor fluctuations are associated with fluctuation of BP and sweating and these fluctuations may represent autonomic dysfunction caused by PD, the effect of PD medication, or both. Selegiline withdrawal seems to alleviate the orthostatic reaction in patients with advanced PD.

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Szewczyk-Krolikowski, Konrad. "Clinical and imaging characteristics of early Parkinson's disease". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:c118f620-19a9-4d0c-bcfc-018e3dd9ff3d.

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Background. Pathological processes in Parkinson’s disease (PD) start long before the first symptoms appear and by the time the disease is clinically established the results of neurodegeneration may be irreversible. Efforts to prevent or stem disease progression need to start in early disease and good characterization and new markers of early PD are urgently needed. Objectives. This thesis aims to characterize early disease stages in three projects. Firstly, clinical features of PD within 3 years of diagnosis will be explored in an incident cohort of patients and controls, using a range of tools to cover the whole breadth of clinical presentation of PD. Secondly, functional imaging studies in PD published so far will be examined through a meta-analysis to identify the most robust functional imaging markers. Thirdly, a functional MRI resting-state study in early PD will be performed to identify reproducible differences between patients and matched control subjects. Results. The cohort analysis found that age was a strong predictor of disease severity, independent of disease duration, while gender was seen to affect disease severity depending on the body region. A meta-analysis of all published functional imaging studies across all disease stages showed abnormal activations in the Basal Ganglia but also in a wide range of motor and non-motor brain areas. Dopamine supplementation normalized activations in the Basal Ganglia and some other areas, while other circuits remained resistant to medication suggesting non-dopaminergic abnormality. In the resting-state study, the Basal Ganglia Network showed greatly reduced connectivity in early PD compared to controls, which normalized on administration of dopaminergic medication. Reduced BGN connectivity was also validated on a separate group of PD subjects achieving very good separation of patients from controls. Conclusions. The effect of gender and age on early presentation of PD has potential significance for early diagnosis and choice of outcome measures for clinical trials. Within the realm of imaging, traditional task-based fMRI studies fail to show a clear and reproducible pattern of activations making this method unfeasible for early diagnostic testing. In contrast, resting-state fMRI connectivity in the Basal Ganglia Network appears to be a promising and reliable method even in the early stages of PD. Clinical profiling and resting imaging changes offer avenues for developing future biomarkers in early PD.

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Saad, Ali. "Detection of Freezing of Gait in Parkinson's disease". Thesis, Le Havre, 2016. http://www.theses.fr/2016LEHA0029/document.

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Le risque de chute provoqué par le phénomène épisodique de ‘Freeze of Gait’ (FoG) est un symptôme commun de la maladie de Parkinson. Cette étude concerne la détection et le diagnostic des épisodes de FoG à l'aide d'un prototype multi-capteurs. La première contribution est l'introduction de nouveaux capteurs (télémètres et goniomètres) dans le dispositif de mesure pour la détection des épisodes de FoG. Nous montrons que l'information supplémentaire obtenue avec ces capteurs améliore les performances de la détection. La seconde contribution met œuvre un algorithme de détection basé sur des réseaux de neurones gaussiens. Les performance de cet algorithme sont discutées et comparées à l'état de l'art. La troisième contribution est développement d'une approche de modélisation probabiliste basée sur les réseaux bayésiens pour diagnostiquer le changement du comportement de marche des patients avant, pendant et après un épisode de FoG. La dernière contribution est l'utilisation de réseaux bayésiens arborescents pour construire un modèle global qui lie plusieurs symptômes de la maladie de Parkinson : les épisodes de FoG, la déformation de l'écriture et de la parole. Pour tester et valider cette étude, des données cliniques ont été obtenues pour des patients atteints de Parkinson. Les performances en détection, classification et diagnostic sont soigneusement étudiées et évaluées
Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of Parkinson's disease (PD). This research is headed toward implementing a detection, diagnosis and correction system that prevents FoG episodes using a multi-sensor device. This particular study aims to detect/diagnose FoG using different machine learning approaches. In this study we validate the choice of integrating multiple sensors to detect FoG with better performance. Our first level of contribution is introducing new types of sensors for the detection of FoG (telemeter and goniometer). An advantage in our work is that due to the inconsistency of FoG events, the extracted features from all sensors are combined using the Principal Component Analysis technique. The second level of contribution is implementing a new detection algorithm in the field of FoG detection, which is the Gaussian Neural Network algorithm. The third level of contribution is developing a probabilistic modeling approach based on Bayesian Belief Networks that is able to diagnosis the behavioral walking change of patients before, during and after a freezing event. Our final level of contribution is utilizing tree-structured Bayesian Networks to build a global model that links and diagnoses multiple Parkinson's disease symptoms such as FoG, handwriting, and speech. To achieve our goals, clinical data are acquired from patients diagnosed with PD. The acquired data are subjected to effective time and frequency feature extraction then introduced to the different detection/diagnosis approaches. The used detection methods are able to detect 100% of the present appearances of FoG episodes. The classification performances of our approaches are studied thoroughly and the accuracy of all methodologies is considered carefully and evaluated

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Libros sobre el tema "Early Detection of Parkinson's Disease"

E, Lyons Kelly, ed. Management of early Parkinson's disease. [Oxford]: Oxford University Press, 2009.

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P, Dostert, Erbamont Inc y Fondazione Carlo Erba, eds. Early markers in Parkinson's and Alzheimer's diseases. Wien: Springer-Verlag, 1990.

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Carlos, Kaski Juan y Holt David W, eds. Myocardial damage: Early detection by novel biochemical markers. Dordrecht: Kluwer Academic, 1998.

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1933-, Fahn Stanley, ed. Parlodel® (bromocriptine mesylate) in the early management of Parkinson's disease: Excerpts from Recent developments in Parkinson's disease, volume 2. Florham Park, N.J: Macmillan Healthcare Information, 1987.

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McCarthy, Joseph C. Early hip disorders: Advances in detection and minimally invasive treatment. New York: Springer, 2011.

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name, No. Early hip disorders: Advances in detection and minimally invasive treatment. New York, NY: Springer, 2003.

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editor, Mordini E. (Emilio) y Green Manfred editor, eds. Internet-based intelligence in public health emergencies: Early detection and response in disease outbreak crises. Amsterdam, Netherlands: IOS Press, 2011.

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Long, Katrina M. Pre-active PD: A Therapist Delivered Physical Activity Behavior Change Program for People With Early Stage Parkinson's Disease. [New York, N.Y.?]: [publisher not identified], 2020.

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Fitzgerald, Rebecca C. Pre-invasive disease: Pathogenesis and clinical management. New York: Springer, 2011.

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Christophe, Trivalle, ed. Gérontologie préventive: Éléments de prévention du vieillissement pathologique. Paris: Masson, 2002.

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Capítulos de libros sobre el tema "Early Detection of Parkinson's Disease"

Cotogni, Marco, Lucia Sacchi, Dejan Georgiev y Aleksander Sadikov. "Detection of Parkinson's Disease Early Progressors Using Routine Clinical Predictors". En Artificial Intelligence in Medicine, 163–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77211-6_18.

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Dostert, P., M. Strolin Benedetti y G. Dordain. "Salsolinol and the early detection of Parkinson’s disease". En New Vistas in Drug Research, 93–97. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-9098-2_11.

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Agarwal, Priyal, Vipin Talreja, Rutuja Patil, Vaishnavi Jadhav y Indu Dokare. "Early Detection of Parkinson’s Disease Using Spiral Test". En Data-Intensive Research, 391–402. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9179-2_30.

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Tandon, Sabina y Saurav Verma. "Early Detection of Parkinson’s Disease Using Computer Vision". En Data Management, Analytics and Innovation, 199–208. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2937-2_15.

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Bansal, Mohit, Satya Jeet Raj Upali y Sukesha Sharma. "Early Parkinson Disease Detection Using Audio Signal Processing". En Emerging Technologies in Data Mining and Information Security, 243–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4193-1_23.

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Boucherouite, Jihad, Abdelilah Jilbab y Atman Jbari. "Automatic SPECT Image Processing for Parkinson’s Disease Early Detection". En Communications in Computer and Information Science, 17–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20490-6_2.

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Taleb, Catherine, Laurence Likforman-Sulem y Chafic Mokbel. "Language-Independent Bimodal System for Early Parkinson’s Disease Detection". En Document Analysis and Recognition – ICDAR 2021, 397–413. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86334-0_26.

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Faouzi, Johann, Olivier Colliot y Jean-Christophe Corvol. "Machine Learning for Parkinson’s Disease and Related Disorders". En Machine Learning for Brain Disorders, 847–77. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3195-9_26.

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AbstractParkinson’s disease is a complex heterogeneous neurodegenerative disorder characterized by the loss of dopamine neurons in the basal ganglia, resulting in many motor and non-motor symptoms. Although there is no cure to date, the dopamine replacement therapy can improve motor symptoms and the quality of life of the patients. The cardinal symptoms of this disorder are tremor, bradykinesia, and rigidity, referred to as parkinsonism. Other related disorders, such as dementia with Lewy bodies, multiple system atrophy, and progressive supranuclear palsy, share similar motor symptoms although they have different pathophysiology and are less responsive to the dopamine replacement therapy. Machine learning can be of great utility to better understand Parkinson’s disease and related disorders and to improve patient care. Many challenges are still open, including early accurate diagnosis, differential diagnosis, better understanding of the pathologies, symptom detection and quantification, individual disease progression prediction, and personalized therapies. In this chapter, we review research works on Parkinson’s disease and related disorders using machine learning.

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Basnin, Nanziba, Tahmina Akter Sumi, Mohammad Shahadat Hossain y Karl Andersson. "Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings". En Brain Informatics, 433–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86993-9_39.

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Sanyal, Saptarsi, Shanmugarathinam y Naveen Vijayakumar Watson. "PDEDX: A Comprehensive Expert System for Early Detection of Parkinson’s Disease". En Lecture Notes in Networks and Systems, 397–406. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2671-4_30.

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Actas de conferencias sobre el tema "Early Detection of Parkinson's Disease"

P, Anandha Ponni, Avaniya Seireena y Shiny R. M. "Early Detection of Parkinson's Disease Through Vocal Features". En 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), 1214–19. IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894297.

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Saideepthi, Pabba, Sravanthi Kollimarla, Pramod Gaur, Ashish Gupta y Siddhaling Urolagin. "Automated Early Detection of Parkinson's Disease Using Graph Convolution Networks". En 2024 International Conference on Computational Intelligence and Network Systems (CINS), 1–6. IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864454.

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Cabrera, Marjorie, Kevin Sánchez y Manuel Cardona. "Hand Tracker for the Early Detection of Neurodegenerative Parkinson's Disease". En 2024 IEEE Central America and Panama Student Conference (CONESCAPAN), 1–6. IEEE, 2024. https://doi.org/10.1109/conescapan62181.2024.10891121.

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Devi, S. Vijaya Amala, K. Vijayalakshmi, R. Santhana Krishnan, J. Relin Francis Raj, R. Umesh y N. Soundiraraj. "Hybrid Deep Learning Methods for Enhancing Parkinson's Disease Early Detection". En 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), 1462–69. IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933259.

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Lakkshmanan, Ajanthaa, Venna Venkata Karthik y Javvadi Prabhas. "Early Detection of Parkinson's Disease Through Predictive Analytics and Machine Learning". En 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), 867–74. IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10864118.

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Pariselvam, S., S. Ashok Kumar, R. Sathishkumar, M. Govindarajan, C. Mukeshkumar y R. Avinash Raj. "Enhanced Early Parkinson's Disease Detection Using Resnet-101 Based on MRI Images". En 2024 International Conference on System, Computation, Automation and Networking (ICSCAN), 1–5. IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894502.

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Razzouki, Anas Filali, Laetitia Jeancolas, Graziella Mangone, Sara Sambin, Alizé Chalançon, Manon Gomes, Stéphane Lehéricy et al. "Early-Stage Parkinson's Disease Detection Based on Optical Flow and Video Vision Transformer". En 2024 16th International Conference on Human System Interaction (HSI), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/hsi61632.2024.10613585.

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Prafulla, P. S., H. C. Sahana, K. Shwetha, M. N. Anusha, K. Prabhavathi y S. N. Shwetha. "Machine Learning Technique for early Parkinson’s Disease Detection". En 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET), 1–6. IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10894963.

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Nandankar, Praful V., Arnav Kothiyal, Kiran Kumar D, Anuradha Patil, Harshal Patil y Ramya Maranan. "Parkinson's Disease Early Detection and Classification based on EMG Signal using Spherical Convolutional Neural Network". En 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 1140–46. IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714636.

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Zebidi, Hadjer, Zeineb BenMessaoud y Mondher Frikha. "A Comparative and Explainable Study of Machine Learning Models for Early Detection of Parkinson's Disease Using Spectrograms". En 14th International Conference on Pattern Recognition Applications and Methods, 272–82. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013183900003905.

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Informes sobre el tema "Early Detection of Parkinson's Disease"

Doty, Richard L., Jacob Dubroff, Gui-Shang Ying, Thelma E. McCloskey, James Wilson, Jennifer Rotz, Michele Morris, James W. Hall, Neil T. Shepard y Allen Osman. Sensory Dysfunction in Early Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, julio de 2011. http://dx.doi.org/10.21236/ada550800.

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Christian Agudelo, Christian Agudelo. Physical experience of emotion: an early marker of Parkinson's Disease? Experiment, mayo de 2013. http://dx.doi.org/10.18258/0471.

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Wu, Meiye, Ryan Wesley Davis y Anson Hatch. Portable microfluidic raman system for rapid, label-free early disease signature detection. Office of Scientific and Technical Information (OSTI), septiembre de 2015. http://dx.doi.org/10.2172/1222536.

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Rostaminejad, Marzieh. Early Diagnosis of Alzheimer's disease using Electrochemical-based Nanobiosensors for miRNA Detection. Peeref, julio de 2022. http://dx.doi.org/10.54985/peeref.2207p6024343.

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Deshpande, Alina. RED Alert – Early warning or detection of global re-emerging infectious disease (RED). Office of Scientific and Technical Information (OSTI), julio de 2016. http://dx.doi.org/10.2172/1261795.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease via X-Ray Phase CT. Fort Belvoir, VA: Defense Technical Information Center, junio de 2014. http://dx.doi.org/10.21236/ada612057.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease via X-Ray Phase CT. Fort Belvoir, VA: Defense Technical Information Center, junio de 2013. http://dx.doi.org/10.21236/ada582946.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease Via X-ray Phase CT. Fort Belvoir, VA: Defense Technical Information Center, junio de 2015. http://dx.doi.org/10.21236/ada620373.

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Li, Jiangwei. Applications of a single-molecule detection in early disease diagnosis and enzymatic reaction study. Office of Scientific and Technical Information (OSTI), enero de 2008. http://dx.doi.org/10.2172/964365.

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Gabrieli, John D. SPECT and fMRI Analysis of Motor and Cognitive Indices of Early Parkinson's Disease: The Relationship of Striatal Dopamine and Cortical Function. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2001. http://dx.doi.org/10.21236/ada406147.

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