A survey on computer-assisted Parkinson's Disease diagnosis

Sensors, 2022

Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-bas...

IRJET, 2022

In this Global era, Technology plays an important part in our lives, considering our Lifestyle, Healthcare and maintaining resources and assets. In the field of HealthCare, technology has been growing each day in order to counter different diseases and their symptoms emerging in the present world. One such disease is Parkinson's Disease. Parkinson's Disease is a brain neurological disorder. It causes tremors in the body and hands, and also stiffness in the body. At this moment, there is no proper cure or treatment available. Only when the condition is detected early, or at its onset, is treatment possible. These will not only lower the cost of the sickness, but they may also save lives. As a result, a project called "Parkinson's Disease Detection Using Machine Learning Technologies" was launched in try to diagnose the disease at an early stage. Parkinson's disease is a neurological disease that affects the brain's dopamine-producing neurons and progresses over time. As a result, various machine learning techniques and Python libraries are employed in order to develop a model capable of reliably detecting the presence of disease in one's body. The current models rely on image or audio analysis to diagnose disease, encouraging the development of a new model that uses both.

International Journal for Research in Applied Science & Engineering Technology, 2021

Parkinson's Disease (PD) persistent consideration is constrained by lacking, irregular manifestation checking, rare access to mind, and meager experiences with human services experts prompting poor clinical dynamic and imperfect patient wellbeing related results.Advanced approaches have empowered target and remote checking of impaired motion function with the guarantee of significantly changing the indicative, observing, and helpful detecting in PD. We demonstrated that by using a variety of upper limb functional tests Motor_UPDRS. The objective of this paper is to provide preliminary evidence that machine learning systems allow one to determine whether a person is suffering from Parkinson's disease or not and different features of the disease using various machine learning algorithms .Diagnosis of the Parkinson disease through machine learning provides better understanding from Parkinson's Disease dataset in the present. Jupyter notebook has been used in the present experimentation for the statistical analysis, classification, Evaluation of supervised and unsupervised learning methods. Voice dataset for Parkinson disease has been taken from UCI Machine learning repository from Center for Machine Learning and Intelligent Systems. A study on feature relevance analysis and the accuracy using different classification methods was carried out on Parkinson data-set.

Sensors, 2022

Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hund...

Frontiers in Aging Neuroscience

IEEE Access

The paper presents a novel sensor-based disease symptoms evaluation method which can be applied in the domain of neurological treatment monitoring and efficiency analysis. The main purpose of the method is to provide a quantitative approach for symptoms recognition and their intensity, which can be used for efficient medicine intake planning for Parkinson's Disease patients. This work presents an innovative method, which enables to objectify the process of clinical trials. The developed solution implements sensor data fusion method, which analyses time correlated wearable sensor biomedical data and symptoms survey. We have merged two separate methods of recognizing and assessing the intensity of Parkinson's Disease (PD) symptoms using time-constrained survey as well as sensor and interaction-based algorithms, which enable to objectively assess the intensity of disease symptoms. Based on process-based analysis and clinical trials observations, a set of requirements for validating symptoms of neurological diseases have been formulated. Proposed solution concentrates on PD indicators connected with arms movement and mental reaction delays, which can be registered using wearable sensors. Since 2017 the tool has been tested by a group of four selected neurologists and 10 users, 3 of which are PD patients. To meet the project's supplementary (efficiency, security) requirements, a test clinical trial has been performed involving 3 patients executing trials which lasted two weeks and was supported by the continuous application usage. After successful deployment the method and software tools has been presented for commercial use and further development in order to adjust its usage for other neurological disorders. INDEX TERMS Machine learning, biomedical signal processing, computer aided diagnosis, Parkinson's disease, medicine intake prediction.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

For many years, Parkinson's disease (PD) has been viewed as a tragedy for humanity. A lot of research is being done on how to detect it using an automated method. This necessitates the use of a machine learning model for PD early diagnosis. Studying the currently employed computational intelligence strategies in the field of research used for PD detection is a crucial requirement for developing a full proof model. Numerous models now in use either concentrate on a single modality or only briefly consider several modalities. This prompted us to conduct a comparative literature review of four primary PD early detection techniques, specifically tremor at rest, bradykinesia, stiffness, and voice impairment. Modern Machine learning methods including K-nearest neighbours, Decision Tree, Support Vector Machine, and Logistic Regression (KNN), Stochastic Gradient Descent (SGD), and Gaussian Naive Bayes (GNB) are applied in these modalities with the corresponding datasets. Additionally, ensemble methods like Hard Voting (HV), Adaptive Boosting (AB), and Random Forest Classifier (RF) are used.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Parkinson's disease (PD), or simply Parkinson's is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. A quantitative analysis of handwriting samples would be valuable as it could supplement and support clinical assessments, help monitor micrographic, and link it to PD. Such an analysis would be especially useful if it could detect subtle yet relevant changes in handwriting morphology, thus enhancing solution of the detection procedure. We can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this study, we conducted a literature review of studies that applied machine learning models to movement data to diagnose PD published in 2019, using the PubMed and IEEE Xplore databases, to provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of Parkinson's disease. In this research, we investigated their goals, data sources, data kinds, machine learning methodologies, and associated outcomes.

Measurement, 2019

The use of machine learning techniques for early diseases diagnosis has attracted the attention of scholars worldwide. Parkinson's Disease (PD) is one of the most common neurological and complicated diseases affecting the central nervous system. Unified Parkinson's Disease Rating Scale (UPDRS) is widely used for tracking PD symptom progression. Motor-and Total-UPDRS are two important clinical scales of PD. The aim of this study is to predict UPDRS scores through analyzing the speech signal properties which is important in PD diagnosis. We take the advantages of ensemble learning and dimensionality reduction techniques and develop a new hybrid method to predict Total-and Motor-UPDRS. We accordingly improve the time complexity and accuracy of the PD diagnosis systems, respectively, by using Singular Value Decomposition (SVD) and ensembles of Adaptive Neuro-Fuzzy Inference System (ANFIS). We evaluate our method on a large PD dataset and present the results. The results showed that the proposed method is effective in predicting PD progression by improving the accuracy and computation time of the disease diagnosis. The method can be implemented as a medical decision support system for real-time PD diagnosis when big data from the patients is available in the medical datasets.

IEEE, 2021

Parkinson's disease (PD) is disabling disease that affects the quality of life. It belimps due to the death of cells that produce dopamine's in the substantia nigra part of the central nervous system (CNS) which affects the human body. People who have Parkinson's disease feel difficulty in doing activities like speaking, writing, and walking. In the recent past, speech, gait and EEG signals have been investigated for the detection of PD. However, speech analysis is the most considered technique to be used. Researches have shown that 90% of the people who suffer from Parkinson's disease have speech disorders. With the increase in the severity of the disease, the patient's voice gets more and more deteriorated. The non-invasive treatments for voice analysis are available that helps in ameliorating the life quality of a patient. Thus, for building the telemonitoring and telediagnosis models for prediction, the speech analysis has been tremendously increased. The proper interpretation of speech signals is one of the important classification problems for Parkinson's disease diagnosis. The main purpose of this paper is to contemplate the survey work of the machine learning techniques and deep learning procedures used for Parkinson's disease classification. Deep learning and machine learning techniques have been used as a part of the discovery for the efficient classification of PD. The various classification models like support vector machines, naive Bayes, deep neural networks, decision tree and random forest are effectively employed for classification purposes. The analysis of results of different research works showed that both machine learning and deep learning algorithms have shown promising future and therefore paving a better way for the detection of Parkinson's disease at its earlier stages. The classification accuracy achieved by the machine learning classifier. Among deep learning approaches, the deep neural network has achieved the best accuracy of 99.49%. The results obtained from different works suggest that artificial intelligence is becoming a powerful learning tool that has much to offer to data scientists as well as neurologists. In general the learning methods are adding value to decision-making problems especially in the field of medical diagnosis.

2016

Parkinson's disease (PD) is a progressive and chronic nervous system disease that impairs the ability of speech, gait, and complex muscle-and-nerve actions. Early diagnosis of PD is quite important for alleviating the symptoms. Cost effective and convenient telemedicine technology helps to distinguish the patients with PD from healthy people using variations of dysphonia, gait or motor skills. In this study, a novel telemedicine technology was developed to detect PD remotely using dysphonia features. Feature transformation and several machine learning (ML) methods with 2-, 5-and 10-fold cross-validations were implemented on the vocal features. It was observed that the combination of principal component analysis (PCA) as a feature transformation (FT) and k-nearest neighbor (k-NN) as a classifier with 10-fold cross-validation has the best accuracy as 99.1%. All ML processes were applied to the prerecorded PD dataset using a newly created program named ParkDet 2.0. Additionally, the blind test interface was created on the ParkDet so that users could detect new patients with PD in future. Clinicians or medical technicians, without any knowledge of ML, will be able to use the blind test interface to detect PD at a clinic or remote location utilizing internet as a telemedicine application.

Over the past fifteen years, quantitative monitoring of human motor control and movement disorders has been an emerging field of research. Recent studies state the fact that Malaysia has been experiencing improved health, longer life expectancy, and low mortality as well as declining fertility like other developing countries. As the population grows older, the prevalence of neurodegenerative diseases also increases exponentially. Parkinson disease (PD) is one of the most common chronic progressive neurodegenerative diseases that are related to movement disorders. After years of research and development solutions for detecting and assessing the symptoms severity in PD are quite limited. With current ongoing advance development sensor technology, development of various uni-modal approaches: technological tools to quantify PD symptom severity had drawn significance attention worldwide. The objective of this review is to compare some available technological tools for monitoring the seve...

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Parkinson Disease (PD) is a neurodegenerative disorder, progressive in nature which has no cure. The delay of PD progression is possible by incorporation of early diagnosis system. Early diagnosis can be made effective and accurate by the usage of Artificial Intelligence (AI) techniques. AI is prevalent in almost all the fields due to its intuitiveness and accuracy which covers the small applications in education sectors to the large applications in healthcare diagnosis system. This paper aims to provide an intensive review in the advancements of PD diagnosis by providing taxonomy, classification of PD diagnosis system and mapping the symptoms with its modalities. This paper also focuses on presenting the advancements of PD Clinical Decision Support System (CDSS) along with telemonitoring and telediagnosis in chronological order. A generic framework is presented for early PD diagnosis with the state-of-the-art technique. The paper is concluded with challenges and future prospects in the field of early diagnosis of PD.

2021

Parkinson's disease (PD) is a neurodegenerative condition that affects the normal motor function of the brain. Automated sensor systems are unique sensor modalities that have been combined together for a specific function. In this thesis, an automated sensor system that tracks and records PD motor symptoms in a clinical setting is proposed. The motor symptoms that were targeted for quantification were rigidity, bradykinesia and tremor. The automated sensor system produced here is called the Parkinson's diagnostic device (PDD) and comprised of a force sensor, motion sensors and muscle activity sensors to measure PD motor symptoms. All accompanying software interfacing libraries and graphical user interfaces developed were described in this thesis as well. The Unified Parkinson's disease Rating Scale (UPDRS) is currently the gold standard used by clinicians to measure and rate the severity of motor symptoms. As such, during subject testing, the UPDRS score given by the cli...

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Many current issues, as well as issues that will arise in the near and distant future, are being resolved in large part thanks to machine learning (ML). Problems are being solved by machine learning in every industry. ML is making a significant contribution to real-time applications, robotics, and health care. In this essay, we have chosen to address Parkinson's disease, one of the rare diseases, as one of the key emerging challenges. Parkinson's disease (PD) is a neurological condition that worsens over time and manifests as rigidity, bradykinesia (slowed movements), postural instability, tremor, and freezing of gait, among other symptoms (FOG). We have chosen to deploy a select handful of the ML-related strategies to combat the disease early on in an effort to completely eradicate it. I.