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Статті в журналах з теми "PARKINSON'S DISEASE DETECTION"
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N., Chandana, Divya C. D., and Radhika A. D. "A Review on Parkinsons Disease Detection." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 760–65. http://dx.doi.org/10.54254/2755-2721/2/20220675.
Повний текст джерелаАнотація:
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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Singh, Manju, and Vijay Khare. "Detection of Parkinson’s Disease Using the Spiral Diagram and Convolutional Neural Network." Ingénierie des systèmes d information 27, no. 6 (December 31, 2022): 991–97. http://dx.doi.org/10.18280/isi.270616.
Повний текст джерелаАнотація:
This study intends to propose a PD detection using spiral sketching and CNN. The fundamental idea is to analyze a person's spiral drawings and classify them as healthy or having Parkinson's disease. Spiral sketches drawn by healthy people look almost like standard spiral shapes. However, the spirals drawn by people with Parkinson's disease look distorted because they deviate significantly from their perfect spiral shape due to slow movement, and poor hand-brain coordination. In this paper Convolution, Neural Network is used to detect Parkinson’s, and 83.6% classification accuracy is obtained.
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I, Kalaiyarasi, Amudha P, and Sivakumari S. "Parkinson\'s Disease Detection Using Deep Learning Technique." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1789–96. http://dx.doi.org/10.22214/ijraset.2023.51916.
Повний текст джерелаАнотація:
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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Kolekar, Sachchit, Naman Jain, Amit Mete, and Prof Nilesh Kulal. "Parkinson’s Disease Detection using Ensemble Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4189–93. http://dx.doi.org/10.22214/ijraset.2023.51241.
Повний текст джерелаАнотація:
Abstract: In this decade of rapid developments in medical science, most research fail to focus on age related disorders. These are illnesses that manifest their symptoms at a far later stage, making complete recovery practically impossible. Parkinson's disease (PD) is the brain's second most prevalent neurodegenerative condition. One may claim that it is nearly incurable and causes significant suffering to people. All of this indicates that there is an impending demand for accurate, trustworthy, and expandable Parkinson's disease diagnosis. A problem of this magnitude necessitates the automation of the diagnostic to lead accurate and reliable results.Most Parkinson's disease patients have some type of speech impairment or dysphonia,making speech measures and indicators one of the most essential parts in PD prediction. The Goal of this work is to compare various machine learning models in successfully predicting the severity of Parkinson's disease and develop an effective and accurate model to help diagnose the disease accurately at an earlier stage, which could help doctors assist in cure and recovery of PD patients. We want to use the Parkinson's Telemonitoring dataset obtained from the UCI ML repository for the aforementioned purpose.Five Different Classification algorithms, including decision tree, random forest, logistic regression, support vector machine, and knearest neighbors, were used to create individual models. The Ensemble learning method was then applied to combine the predictions of these individual.
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Degadwala, Sheshang D., S. Leopauline, D. Sarathy, C. Augustine, and M. Kamesh. "Detection of Parkinson's disease using CNN." International Journal of Medical Engineering and Informatics 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijmei.2022.10048478.
Повний текст джерела
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M, Sakshi, Dr Sankhya N. Nayak, Skanda G N, Shreyas R. Adiga, and Pavana R. "A Review on Detection of Parkinsons Disease Using ML Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 696–99. http://dx.doi.org/10.22214/ijraset.2023.49497.
Повний текст джерелаАнотація:
Abstract: Parkinson disease prediction is an area of active research in healthcare and machine learning. Even though Parkinson's disease is not well-known worldwide, its negative impacts are detrimental and should be seriously considered. Furthermore, because individuals are so immersed in their busy lives, they frequently disregard the early signs of this condition, which could worsen as it progresses. There are many techniques for Parkinson disease prediction. In this paper we are going to discuss some of the possible technical solutions proposed by researchers
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Adekunle, Abiona Akeem, Oyerinde Bolarinwa Joseph, and Ajinaja Micheal Olalekan. "Early Parkinson's Disease Detection Using by Machine Learning Approach." Asian Journal of Research in Computer Science 16, no. 2 (June 9, 2023): 36–45. http://dx.doi.org/10.9734/ajrcos/2023/v16i2337.
Повний текст джерелаАнотація:
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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Mittal, Vikas, and R. K. Sharma. "Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (October 2021): 1–22. http://dx.doi.org/10.4018/ijhisi.20211001.oa30.
Повний текст джерелаАнотація:
The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.
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Lamba, Rohit, Tarun Gulati, and Anurag Jain. "An Intelligent System for Parkinson's Diagnosis Using Hybrid Feature Selection Approach." International Journal of Software Innovation 10, no. 1 (January 2022): 1–13. http://dx.doi.org/10.4018/ijsi.292027.
Повний текст джерелаАнотація:
Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.
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Sekhar, Ch, M. S. Rao, and D. Bhattacharyya. "Machine Learning Algorithms for Parkinson's Disease Detection." Asia-Pacific Journal of Neural Networks and Its Applications 4, no. 1 (August 30, 2020): 29–36. http://dx.doi.org/10.21742/ajnnia.2020.4.1.04.
Повний текст джерелаДисертації з теми "PARKINSON'S DISEASE DETECTION"
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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.
Повний текст джерелаАнотація:
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 évaluéesFreezing 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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Chen, Lei [Verfasser]. "Computer-aided detection of Parkinson's Disease using transcranial sonography / Lei Chen." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1046712691/34.
Повний текст джерела
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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.
Повний текст джерелаАнотація:
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 thèseParkinson’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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Jalloul, Nahed. "Development of a system of acquisition and movement analysis : application on Parkinson's disease." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S096/document.
Повний текст джерелаАнотація:
Le travail présenté dans ce mémoire porte sur le développement d'un système de surveillance ambulatoire pour la détection de la dyskinésie induite par la Levodopa (LID) chez les patients de la maladie de Parkinson (PD). Le système est composé d’unités de mesure inertielle (IMUs) qui recueillent des signaux de mouvement chez des sujets sains et des patients parkinsoniens. Des méthodes différentes sont évaluées pour la détection de LID avec et sans classification des activités. Les données recueillies auprès des sujets sains sont utilisées pour concevoir un classificateur d'activité fiable. Par la suite, un algorithme qui effectue la classification des activités et la détection de la dyskinésie sur les données recueillies auprès de des patients parkinsoniens est proposé. Une nouvelle approche basée sur l'analyse de réseau complexe est également explorée et présente des résultats intéressants. Les méthodes de traitement développées ont été intégrées dans une plateforme complète d’analyse nommée PARADYSEThe work presented in this thesis is concerned with the development of an ambulatory monitoring system for the detection of Levodopa Induced Dyskinesia (LID) in Parkinson’s disease (PD) patients. The system is composed of Inertial Measurement Units (IMUs) that collect movement signals from healthy individuals and PD patients. Different methods are evaluated which consist of LID detection with and without activity classification. Data collected from healthy individuals is used to design a reliable activity classifier. Following that, an algorithm that performs activity classification and dyskinesia detection on data collected from PD patients is tested. A new approach based on complex network analysis is also explored and presents interesting results. The evaluated analysis methods are incorporated into a platform PARADYSE in order to further advance the system’s capabilities
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F, Miraglia. "Development of molecular biosensors for the detection of alpha-synuclein aggregation in cells." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1096217.
Повний текст джерелаАнотація:
Introduction: Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, which affects more than 6 million people worldwide. PD is a progressive multiorgan proteinopathy, also called synucleinopathy, because of the abnormal neuronal accumulation of misfolded alpha-synuclein (αS), an intrinsically disorder protein (IDP) of 140 amino acids. αS is expressed ubiquitously in the cells and has been particularly found associated with membranes in the pre-synaptic terminal, suggesting a role in the regulation of vesicle trafficking, synaptic transmission and synaptic plasticity. Environmental toxins, genetic mutations, amplification of αS gene trigger αS misfolding and aggregation in Lewy Bodies (LBs) and Lewy Neurites (LNs), typical histopathological marker in PD. This event leads to a gradual loss of dopaminergic neurons in substantia nigra pars compacta (SNpc) and the spreading of LB pathology not only in neuronal networks of the central nervous system, but also in the autonomic and peripheral nervous systems. For these reasons, PD manifest with a broad range of motor (e.g. rigidity, tremor and bradykinesia) and non-motor symptoms (e.g. anosmia, constipation, cognitive dysfunction). However, the onset of motor symptoms occurs only when about the 50% of dopaminergic neurons are lost. Therefore, identifying the early step that trigger neuronal toxicity might help in halting neurodegeneration. Misfolding, oligomerization, and fibrillization of αS are thought to be central events in the onset and progression of PD. In fact, αS oligomers acts as seeds for the nucleation dependent process of αS intracellular aggregation, and as a template for the cell-to-cell transmission of αS pathology in a prion like manner. In light of this, we developed Alpha-synuclein FRET-based Biosensors (AFBs) capable to monitor any early αS conformational changes ubiquitously in the cell or in a specific subcellular compartment such as the ER, an early site of αS aggregation, under normal and stress conditions.
Methods: AFBs were designed to detect different types of αS assembly such as:
- relaxed vs globular αS conformation, where both CFP and YFP are cloned respectively at the C-terminal and N-terminal of the same molecule of WT human αS (intramolecular AFB);
- parallel multimeric structures based on N-N and C-C termini interactions between molecules, where both fluorophores (CFP and YFP) are cloned separately at the C-terminal of WT human αS (intermolecular AFBs CC);
- antiparallel multimers based on C-N termini interactions, where CFP and YFP are cloned separately and respectively at the C-terminal and N-terminal of WT human αS (intermolecular AFBs CN).
AFBs were tested in two different cell lines: SH-SY5Y human neuroblastoma cell line and an inducible cell line stably expressing αS in the ER (iSH-SY5Y). Two different FRET methodologies were used to analyses AFBs behaviour: Sensitized Emission (SE) in SH-SY5Y living cells and Acceptor Photobleaching (AP) in iSH-SY5Y fixed cells.
Results: interesting outcomes arose by the analysis of AFBs in iSH-SY5Y cells with AP. AFBs provided a radical different behaviour in the ability of detecting conformational differences in αS structure. Intramolecular AFB revealed a closed conformation of monomeric αS under normal conditions, whereas intermolecular AFBs highlighted a multimerization of αS in the ER, which occurred in an antiparallel orientation of αS monomers in normal condition and with a parallel orientation under the effect of leupeptin, an inhibitor of αS degradation pathway.
Discussion and Conclusions: these findings suggested that AFBs represent a valuable strategy to detect conformational changes in αS structures in physiological condition and under the effect of stress stimuli. To the best of our knowledge they are the first fluorescent probes capable of detecting different type of αS multimers in the ER, an early site of pathological αS aggregation.
Therefore, AFBs represent an interesting investigative approach on αS multimerization and, in the future, might be employed as a powerful tool for the screening of therapeutic agents which aim to halt αS pathological aggregation from the early phase and to prevent the irreversible progression of PD.
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Takač, Boris. "Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/668652.
Повний текст джерелаАнотація:
Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment.
Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons.
The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art
FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared.
The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificidad
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Ruffmann, Claudio. "Detection of alpha-synuclein conformational variants from gastro-intestinal biopsy tissue as a potential biomarker for Parkinson's disease." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:3cddebda-aaf4-40c5-b026-9365aa16fdd7.
Повний текст джерелаАнотація:
Gastrointestinal (GI) alpha-synuclein (ASN) detection may represent a clinically useful biomarker of Parkinson's disease (PD), but this has been challenged by conflicting results of recent studies employing different immunohistochemical (IHC) methods and reporting diverse morphological patterns with variable biological interpretation. To increase sensitivity and specificity, we applied three different techniques to detect different possible conformations of ASN in GI tissue derived from biopsies of the GI tract, which were obtained from a longitudinally followed, clinically well-characterized cohort of PD subjects and healthy controls (HC) (Oxford Discovery study). With IHC, we used antibodies reactive for total (T-ASN-Abs), phosphorylated (P-ASN-Abs) and oligomeric (O-ASN-Abs) ASN; with the ASN Proximity Ligation Assay (AS-PLA), we targeted oligomeric ASN species specifically; finally, with the Paraffin-Embedded Tissue Blot (PET-Blot) we aimed to detect fibrillary conformations of ASN specifically. Optimisation and validation of the PET-Blot and PLA techniques was carried out with studies on brain tissue from subjects with ASN pathology, and these experiments were used to gain insight into morphology and distribution of different conformational variants of ASN in the brain of subjects with Lewy pathology. We specified all the detected morphological staining patterns with each technique interpreting them as pathologic or non-specific. Correlation to clinical symptoms was assessed to investigate the potential predictive or diagnostic value of specific staining patterns as biomarkers. A total of 163 GI tissue blocks were collected from 51 PD patients (113 blocks) and 21 healthy controls (50 blocks). In 31 PD patients, GI biopsies had been taken before PD diagnosis (Prodromal PD group); while in 20 PD patients biopsies were obtained after PD diagnosis (Manifest PD group). The majority of these tissues blocks were from large intestine (62%), followed by small intestine (21%), stomach (10%) and oesophagus (7%). With IHC, four ASN staining patterns were detected in GI tissue (Neuritic, Ganglionic, Epithelial, and Cellular), while two distinct staining patterns were detected with AS-PLA (cellular and diffuse signal) and with AS-PET-Blot (ASN-localised and peri-crypt signal). The level of agreement between different techniques was generally low, and no single technique or staining pattern was able to reliably distinguish PD patients (Prodromal or Manifest) from HC. Overall, our study suggests that even specific detection of ASN conformational variants currently considered pathologic was not adequate for the prediction of PD. Future studies with these or other novel techniques focusing on the upper part of the GI tract could overcome current limitations in sensitivity and specificity.
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Hu, Kun. "Fine-grained Human Action Recognition for Freezing of Gait Detection." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27286.
Повний текст джерелаАнотація:
Freezing of gait (FoG) presents as a sudden and brief episode of movement cessation despite the intention to continue walking. As a common symptom of Parkinson's Disease, early detection and quantification of FoG are of great importance in clinical practice. Therefore, this thesis focuses on vision-based and pressure-based FoG detection methods, which were seldom investigated in the past. The task can be treated as a human action recognition problem. Although various deep architectures have achieved encouraging performance for general action recognition, FoG events contain fine-grained patterns, which requires novel deep architectures to learn the domain knowledge. Hence, fine-grained deep architectures are studied, and the major contributions of this thesis are as follows:
1. A graph-based neural network is proposed for vision-based FoG detection by representing a temporal video segment as a directed graph where FoG related candidate regions are the vertices. A weakly-supervised learning strategy is studied to eliminate the resource expensive annotations.
2. A graph sequence recurrent neural network is proposed to formulate long-term graph temporal patterns, which takes graph sequences of dynamic structures as inputs and characterizes FoG patterns by graph recurrent cells.
3. An adversarial spatio-temporal network is proposed to learn FoG patterns across multiple levels using footstep pressure sequences. The adversarial training scheme aims to obtain subject-independent representations to alleviate the issue of high inter-subject variance.
4. A graph fusion neural network is introduced for multimodal learning using footstep pressure maps, video recordings and their associated optical flows. Multimodal graphs are introduced by treating the encoded features of each modality as vertex-level inputs.
Comprehensive experiments demonstrate the effectiveness of the proposed methods.
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Ahlrichs, Claas [Verfasser], Michael [Akademischer Betreuer] Lawo, and Albert [Akademischer Betreuer] Samà. "Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms / Claas Ahlrichs. Gutachter: Michael Lawo ; Albert Samà. Betreuer: Michael Lawo." Bremen : Staats- und Universitätsbibliothek Bremen, 2015. http://d-nb.info/1075609321/34.
Повний текст джерела
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Mohammadian, Rad Nastaran. "Deep Learning for Abnormal Movement Detection using Wearable Sensors: Case Studies on Stereotypical Motor Movements in Autism and Freezing of Gait in Parkinson's Disease." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/368163.
Повний текст джерелаАнотація:
Inertial measurement sensing technology with the capability of capturing disease-relevant data has a great potential for improving the current clinical assessments and enhancing the quality of life in patients with neuro-developmental and neuro-degenerative diseases such as autism spectrum disorders (ASD) and Parkinson's disease (PD). The current clinical assessments can be improved by developing objective tools for the disease diagnosis and continuous monitoring of patients in out of clinical settings. To this end, it is necessary to develop automatic abnormal movement detection methods with the capability of adjusting on new patients' data in real-life settings. However, achieving this goal is challenging mainly because of the inter and intra-subject variability in acquired signals and the lack of labeled data. The research presented in this thesis investigates the application of deep neural networks to address these challenges of abnormal movement detection using inertial measurement unit (IMU) sensors with case studies on stereotypical motor movements in ASD and freezing of gait in PD patients. In this direction, this thesis provides four main contributions: i) A convolutional neural network (CNN) architecture is proposed to learn discriminative features which are sufficiently robust to inter and intra-subject variability. It is further shown how the proposed CNN architecture can be used for parameter transfer learning to enhance the adaptability of the abnormal movement detection system to new data in a longitudinal study. ii) An application of recurrent neural networks and more specifically long short-term memory (LSTM) in combination with CNN is proposed in order to incorporate more the temporal dynamics of IMU signals in the process of feature learning for abnormal movement detection. iii) An ensemble learning approach is proposed to improve the detection accuracy and at the same time to reduce the variance of models. iv) In the normative modeling framework, the problem of abnormal movement detection is redefined in the context of novelty detection and it is shown how a probabilistic denoising autoencoder can be used to learn the distribution of the normal human movements. The resulting deep normative model then is used in a novelty detection setting for unsupervised abnormal movement detection. The experimental results on three benchmark datasets collected from ASD and PD patients illustrate the high potentials of deep learning paradigm to address the crucial challenges toward real-time abnormal movement detection systems using wearable technologies.
Книги з теми "PARKINSON'S DISEASE DETECTION"
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McKeown, Martin J., Xun Chen, Meeko Oishi, and Aiping Liu, eds. New Technologies for Detection, Monitoring and Treatment of Parkinson’s Disease. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-886-4.
Повний текст джерела
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Postuma, Ronald B. REM sleep behavior disorder. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0038.
Повний текст джерелаАнотація:
A diagnosis of REM sleep behavior disorder (RBD), a disorder characterized by “acting out” of dreams during REM sleep, has critical implications for a patient’s future. Aside from being a treatable parasomnia, usually managed with melatonin or clonazepam, RBD is the most powerful risk factor for Parkinson disease and dementia with Lewy bodies yet discovered. Over 70% of patients with idiopathic RBD will develop a neurodegenerative synucleinopathy. Moreover, the disease course is more severe in patients with RBD than those without. Numerous screens have been developed to aid detection, and clinical history can help distinguish RBD from NREM parasomnia. However, final diagnosis relies on polysomnographic documentation of REM atonia loss. Given the profound implications of idiopathic RBD, patients need careful counseling and the offer of neurological follow-up to detect and treat prodromal disease symptoms. Recognition of RBD is also a means to discover and test protective therapies against neurodegenerative disease.
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Roze, Emmanuel, and Nenad Blau. Biogenic Monoamine Disorders. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0031.
Повний текст джерелаАнотація:
Biogenic monoamine disorders are a group of inherited diseases characterized by a defect in the synthesis, transport, or degradation of catecholamines and serotonin. The phenotype mostly reflects the pattern and severity of the monoamine deficiency. Movement disorders due to cerebral dopamine deficiency are almost always prominent, mostly in the form of dystonia and/or parkinsonism. These disorders are potentially devastating yet treatable. Early diagnosis and treatment are crucial to prevent ongoing brain dysfunction. Detection of hyperphenylalaninemia in a neonate could be a good clue to the diagnosis. Final diagnosis is often based on a detailed biochemical investigation of the cerebrospinal fluid and can be confirmed by molecular analysis. Treatment is aimed at restoring neurotransmitter homeostasis using monoamine precursors, monoamine agonists, and inhibitors of monoamine degradation. It also comprises the control of hyperphenylalaninemia and the prevention of cerebral folate deficiency, when applicable.
Частини книг з теми "PARKINSON'S DISEASE DETECTION"
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Bhardwaj, Satyankar, Dhruv Arora, Bali Devi, Venkatesh Gauri Shankar, and Sumit Srivastava. "Machine Learning Assisted Binary and Multiclass Parkinson's Disease Detection." In Intelligent Sustainable Systems, 191–206. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2894-9_15.
Повний текст джерела
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Cotogni, Marco, Lucia Sacchi, Dejan Georgiev, and Aleksander Sadikov. "Detection of Parkinson's Disease Early Progressors Using Routine Clinical Predictors." In 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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Ferrante, Claudio, Licia Sbattella, Vincenzo Scotti, Bindu Menon, and Anitha S. Pillai. "Analysis of Features for Machine Learning Approaches to Parkinson's Disease Detection." In Machine Learning and Deep Learning in Natural Language Processing, 169–83. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003296126-13.
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Baranauskas, Mindaugas, Rytis Jurkonis, Arūnas Lukoševičius, Vaidas Matijošaitis, Rymantė Gleiznienė, and Daiva Rastenytė. "Diagnostic Ability of Radiofrequency Ultrasound in Parkinson's Disease Compared to Conventional Transcranial Sonography and Magnetic Resonance Imaging." In Advances in Medical Imaging, Detection, and Diagnosis, 285–302. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003298038-8.
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Huang, Debin, Wenting Yang, Simeng Li, Hantao Li, Lipeng Wang, Wei Zhang, and Yuzhu Guo. "Artificial Intelligence for Accurate Detection and Analysis of Freezing of Gait in Parkinson's Disease." In Recent Advances in AI-enabled Automated Medical Diagnosis, 173–214. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003176121-13.
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Ganotra, Reema, and Shailender Gupta. "Detection of Parkinson's Disease Using Support Vector Machine and Combination of Various Tissue Density Features." In Lecture Notes in Electrical Engineering, 65–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7993-4_6.
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Ykhlef, Faycal, and Djamel Bouchaffra. "A Comparative Performance Study of Feature Selection Techniques for the Detection of Parkinson's Disease from Speech." In Image Processing and Intelligent Computing Systems, 185–93. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003267782-12.
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Faouzi, Johann, Olivier Colliot, and Jean-Christophe Corvol. "Machine Learning for Parkinson’s Disease and Related Disorders." In 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.
Повний текст джерелаАнотація:
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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Guo, Pei-Fang, Prabir Bhattacharya, and Nawwaf Kharma. "Advances in Detecting Parkinson’s Disease." In Lecture Notes in Computer Science, 306–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13923-9_33.
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Patra, Raj Kumar, Akanksha Gupta, Maguluri Sudeep Joel, and Swati Jain. "Parkinson’s Disease Detection Using Voice Measurements." In Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing, 143–58. First edition. | Boca Raton: CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526-10.
Повний текст джерелаТези доповідей конференцій з теми "PARKINSON'S DISEASE DETECTION"
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Anthoniraj, S., A. Naresh Kumar, Galiveeti Hemakumar Reddy, Sadhan Gope, and More Raju. "Parkinson's Disease Detection Using Machine Learning." In 2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS). IEEE, 2022. http://dx.doi.org/10.1109/ssteps57475.2022.00070.
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Roobini, M. S., Yaragundla Rajesh Kumar Reddy, Udayagiri Sushmanth Girish Royal, Amandeep K. Singh, and K. Babu. "Parkinson's Disease Detection Using Machine Learning." In 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT). IEEE, 2022. http://dx.doi.org/10.1109/ic3iot53935.2022.9768002.
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Omar, Najiya M., and M. E. El-Hawary. "Optimizing classifier performance for Parkinson's Disease detection." In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2017. http://dx.doi.org/10.1109/ccece.2017.7946697.
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Sorensen, G. L., J. Kempfner, P. Jennum, and H. B. D. Sorensen. "Detection of arousals in Parkinson's disease patients." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090757.
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Goel, Shubham, Amrendra Tripathi, Tanupriya Choudhury, and Vivek Kumar. "Parkinson's Disease Detection using Soft Computing Technique." In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART). IEEE, 2019. http://dx.doi.org/10.1109/smart46866.2019.9117384.
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V, Sanjay, and Swarnalatha P. "Machine Learning Techniques for Parkinson's Disease Detection." In 2022 Smart Technologies, Communication and Robotics (STCR). IEEE, 2022. http://dx.doi.org/10.1109/stcr55312.2022.10009074.
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Vikas and R. K. Sharma. "Early detection of Parkinson's disease through Voice." In 2014 International Conference on Advances in Engineering and Technology (ICAET). IEEE, 2014. http://dx.doi.org/10.1109/icaet.2014.7105237.
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Hansen, Ingeborg H., Mikkel Marcussen, Julie A. E. Christensen, Poul Jennum, and Helge B. D. Sorensen. "Detection of a sleep disorder predicting Parkinson's disease." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610868.
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Afroz, Nur, and Boshir Ahmed. "Deep Transfer Learning for Early Parkinson's Disease Detection." In 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2023. http://dx.doi.org/10.1109/ecce57851.2023.10101591.
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Kumari, L. V. Rajani, Mohammad Aatif Jaffery, K. Saketh Sai Nigam, G. Manaswi, and P. Tharangini. "Detection of Parkinson's Disease using Extreme Gradient Boosting." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021. http://dx.doi.org/10.1109/icoei51242.2021.9453088.
Повний текст джерелаЗвіти організацій з теми "PARKINSON'S DISEASE DETECTION"
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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.
Повний текст джерелаАнотація:
Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.