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Journal articles on the topic 'PARKINSON'S DISEASE DETECTION'

Author: Grafiati

Published: 11 September 2023

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1

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.

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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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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.

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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.

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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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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.

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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.

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6

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.

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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.

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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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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.

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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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9

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.

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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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10

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.

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11

Brooks, D. J. "Detection of preclinical Parkinson's disease with PET." Neurology 41, Issue 5, Supplement 2 (May 1, 1991): 24–27. http://dx.doi.org/10.1212/wnl.41.5_suppl_2.24.

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12

Parker, W. D. "Preclinical detection of Parkinson's disease: Biochemical approaches." Neurology 41, Issue 5, Supplement 2 (May 1, 1991): 34–36. http://dx.doi.org/10.1212/wnl.41.5_suppl_2.34.

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13

Jordal, Peter Lüttge, Thomas F. Dyrlund, Kristian Winge, Martin R. Larsen, Erik H. Danielsen, James A. Wells, Daniel E. Otzen, and Jan J. Enghild. "Detection of proteolytic signatures for Parkinson's disease." Future Neurology 11, no. 1 (February 2016): 15–32. http://dx.doi.org/10.2217/fnl.16.3.

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14

Mochizuki, Hideki, Keigo Goto, Hideo Mori, and Yoshikuni Mizuno. "Histochemical detection of apoptosis in Parkinson's disease." Journal of the Neurological Sciences 137, no. 2 (May 1996): 120–23. http://dx.doi.org/10.1016/0022-510x(95)00336-z.

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15

Montgomery, Erwin B. "Olfaction and early detection of Parkinson's disease." Annals of Neurology 57, no. 1 (2004): 157. http://dx.doi.org/10.1002/ana.20354.

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16

Xu, Jiaxin. "Detection methods of Parkinson's Disease based on physiological signals and machine learning methods." Highlights in Science, Engineering and Technology 36 (March 21, 2023): 813–22. http://dx.doi.org/10.54097/hset.v36i.6105.

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Parkinson's disease (PD) is an extremely complex motor disorder due to the lack of dopaminergic neurons in the substantia nigra. and other dopaminergic and non-dopaminergic regions of the brain. The high rate of misdiagnosis in Parkinson's disease often causes patients to miss out on the best treatment opportunities. Since some of the symptoms of Parkinson's disease are mild in the initial stages and become severe over time, it is particularly important to correctly diagnose Parkinson's disease timely. The traditional tremor detection method of Parkinson's disease is more complex and the misdiagnosis rate is high. Methods based on physiological signals such as Local field potential (LFP), Electromyographic signal (EMG) and EEG signal et.al and research by using the machine learning strategies including the traditional machine learning and deep leaning methods are increasing. Get a precise diagnosis for Parkinson's disease, this paper analyzes physiological signals and machine learning methods that commonly used in PD detection, which may provide theoretical and practical references to future studies.

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17

Langston, J. W., and W. C. Koller. "The next frontier in Parkinson's disease: Presymptomatic detection." Neurology 41, Issue 5, Supplement 2 (May 1, 1991): 5–7. http://dx.doi.org/10.1212/wnl.41.5_suppl_2.5.

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18

Yuldashov, S., K. Maksudova, I. Masalimova, and A. Davlatbaev. "Earlier detection of cerebrovascular disorders in Parkinson's disease." Parkinsonism & Related Disorders 79 (October 2020): e103-e104. http://dx.doi.org/10.1016/j.parkreldis.2020.06.376.

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19

Rossi, Maija, Hanna Ruottinen, Seppo Soimakallio, Irina Elovaara, and Prasun Dastidar. "Clinical MRI for iron detection in Parkinson's disease." Clinical Imaging 37, no. 4 (July 2013): 631–36. http://dx.doi.org/10.1016/j.clinimag.2013.02.001.

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20

Govindu, Aditi, and Sushila Palwe. "Early detection of Parkinson's disease using machine learning." Procedia Computer Science 218 (2023): 249–61. http://dx.doi.org/10.1016/j.procs.2023.01.007.

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21

Ezzati, Ali, Fatemeh Khadjevand, Amin Zandvakili, and Abdolhossein Abbassian. "Higher-level motion detection deficit in Parkinson's disease." Brain Research 1320 (March 2010): 143–51. http://dx.doi.org/10.1016/j.brainres.2010.01.022.

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22

Stephenson, Randolph, Andrew Siderowf, and Matthew B. Stern. "Premotor Parkinson's disease: Clinical features and detection strategies." Movement Disorders 24, S2 (2009): S665—S670. http://dx.doi.org/10.1002/mds.22403.

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23

Miyamoto, Tomoyuki, Masayuki Miyamoto, Masaoki Iwanami, and Koichi Hirata. "Idiopathic REM Sleep Behavior Disorder: Implications for the Pathogenesis of Lewy Body Diseases." Parkinson's Disease 2011 (2011): 1–8. http://dx.doi.org/10.4061/2011/941268.

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Objectives. Both results of the odor identification and cardiac123I-metaiodobenzylguanidine accumulation have been investigated for their potential to enhance the detection of pathogenesis resembling that of Lewy body-relatedα-synucleinopathies in patients clinically diagnosed as having idiopathic REM sleep behavior disorder.Methods. We performed both the Odor Stick Identification Test for Japanese and123I-metaiodobenzylguanidine scintigraphy in 30 patients with idiopathic REM sleep behavior disorder, 38 patients with Parkinson's disease, and 20 control subjects.Results. In idiopathic REM sleep behavior disorder, reduced odor identification score and an early or delayed heart to mediastinum ratio on123I-metaiodobenzylguanidine were almost as severe as in Parkinson's disease patients. Delayed cardiac123I-metaiodobenzylguanidine uptake was even more severe in the idiopathic REM sleep behavior disorder group than in the Parkinson's disease group.Conclusions. Reduced cardiac123I-metaiodobenzylguanidine uptake, which is independent of parkinsonism, may be more closely associated with idiopathic REM sleep behavior disorder than olfactory impairment.

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Shaikh, Mr Zaki Ahmed, Mr Viraj Tilekar, Mr Vedant Suryawanshi, Mr Atharva Pawar, and Mr Nitin R. Talhar. "Parkinson Disease Detection from Spiral and Wave Drawings using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 7523–27. http://dx.doi.org/10.22214/ijraset.2023.53503.

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Abstract: Research in biometrics has experienced significant growth in recent years, leading to a wide range of applications. One prominent area where biometrics has proven invaluable is healthcare. In the field of healthcare, identifying relevant biomarkers specific to certain fitness issues and detecting them accurately is crucial for improving medical decision assistance systems. Parkinson's Disease (PD) is a condition where impairment in handwriting has been observed to correlate directly with disease severity. Additionally, individuals with Parkinson's disease tend to exhibit lower velocity and pressure while using a pen for sketching or writing. Detecting such biomarkers accurately and precisely at the early stages of the disease can greatly enhance medical diagnosis. Consequently, a system has been designed to analyse spiral and wave drawing patterns of individuals affected by Parkinson's disease. By leveraging various machine learning algorithms, it becomes possible to analyse these patterns and determine whether a person is suffering from Parkinson's disease or not.

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Ho, Nguyen H. B., Dalton Lee Glasco, Rhys N. Sopp, and Jeffrey Gordon Bell. "Multiplexed Electrochemical Device for the Detection of Biomarkers of Parkinson’s Disease Using 3D Printing." ECS Meeting Abstracts MA2022-02, no. 61 (October 9, 2022): 2231. http://dx.doi.org/10.1149/ma2022-02612231mtgabs.

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Neurotransmitters are chemical messengers that carry signals between cells. Imbalances in neurotransmitter levels have been linked to physical, psychotic, and neurodegenerative diseases such as Alzheimer's, Parkinson's, dementia, addiction, depression, and schizophrenia. Therefore, the rapid detection of neurotransmitters such as dopamine, acetylcholine, glutamate, serotine, and norepinephrine are essential for early diagnosis of various neurological disorders. This talk will focus on the development of a multiplexed electrochemical device using 3D printing for the simultaneous detection of biomarkers related to Parkinson’s disease. By combining the capabilities of stereolithographic (SLA) and fused-deposition modelling (FDM) 3D printing, we demonstrate the ability to fabricate and integrate potentiometric and voltametric sensors into the device. Importantly, we show that this approach is highly modifiable and scalable, providing a pathway towards the development of a full-panel diagnostic test for neurological disorders.

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Rozo Hoyos, Laura Carolina, Juan Pablo Pulgarín González, Paula Andrea Morales Fandiño, and Jonathan Gallego Londoño. "Wearable device intended for detection of fog episodes in Parkinson´s disease." Visión electrónica 13, no. 1 (February 5, 2019): 10–16. http://dx.doi.org/10.14483/22484728.14422.

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The episodes of Freezing of Gait (FOG) are a recurring symptom in people suffering from advanced stages of Parkinson's disease (PD). These are severe occurrences because they may cause falls to the patients, generating further traumas and concussions. In order to solve this yet ineffectively treated issue, this article describes the research that developed a device capable of predicting freezing episodes. On this project a wearable device was developed, which was able to predict freezing episodes based on the calculation of a freezing index (FI) determined by the signals obtained from an inertial measurement unit (IMU). This device was tested in three Patients and signals corresponding to normal gait and simulated Parkinson gait were taken. The results showed that FI obtained from Parkinson gait were much higher than those from a normal gait, validating this parameter as a key aspect in FOG prediction.

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Khubetova, Iryna. "Cognitive Disorders in Patients with Parkinson's Disease." Archive of Clinical Medicine 28, no. 1 (October 17, 2022): 24–28. http://dx.doi.org/10.21802/acm.2022.1.6.

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The aim of the study was to identify cognitive disorders in patients with Parkinson's disease Material and methods. The study was conducted on the basis of the neurological department of the Regional Clinical Hospital (Odesa) during 2011-2021. 364 patients were diagnosed with Parkinson disease (PD) on the basis of diagnostic criteria of the British Brain Bank. Clinical and demographic data were studied: age, sex, severity according to the UPDRS scale, stage of the disease according to the Hoen-Yahr scale, the presence of cognitive impairment and their nature. MMSE (Mini-Mental State Examination;) and PD-CRS (Parkinson’s Disease-Cognitive Rating Scale) were used to study cognitive functions. Statistical processing was performed by analysis of variance, correlation and factor analysis using Statistica 13.0 software (TIBCO, USA). Results. At the onset of the disease, left lesions were noted in 126 of 364 patients, ie 34.6%, right - 127 (34.9%), the remaining 111 (30.5%) - bilateral lesions. The structure of the disease was dominated by mixed forms. Akinetic-rigid form was observed in 92 (25.3%) cases, trembling - in 27 (7.4%), mixed rigid-trembling in 157 (43.1%) cases, trembling-rigid in 92 (7.4%). Patients with stage 2 and 3 CP were most often registered in 28.6% and 28.0% of cases, respectively. 31 (8.5%) patients had more severe motor disorders (stages 3.5 and 4). Cognitive impairment was detected in a significant number of patients (238 or 45.2%), the mean score on the MMSE scale was 25.3 ± 0.3. Accordingly, on the PD-CRS scale, the average score was 91.2 ± 3.4 points. Subdemental changes were present in 82 (15.6%) patients. Accordingly, mild dementia was found in 15 (2.8%) patients, moderate dementia - in 18 (3.4%). Conclusions: The frequency of detection of cognitive impairment in patients with CP was 45.2% with a mean score on the MMSE scale of 25.3 ± 0.3, and on the PD-CRS scale - 91.2 ± 3.4 points Dementia changes were in 82 (15.6%) patients, mild dementia was found in 15 (2.8%) patients, moderate dementia - in 18 (3.4%) There is a direct correlation between the age and severity of intellectual disabilities (r = 0.50). Self-care disorders are determined by the severity of both motor and cognitive disorders by 72% (R = 0.851; R2 = 0.723; Adjusted R2 = 0.721; F (2.2) = 309; p <10-4; SE = 4.6 ). Key words: Parkinson's disease, cognitive impairment, quality of life

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28

Raghavan, Ravi, Clare Khin-Nu, Andrew Brown, Dorothy Irving, Paul G. Ince, Kenneth Day, Stephen P. Tyrer, and Robert H. Perry. "Detection of Lewy Bodies in Trisomy 21 (Down's Syndrome)." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 20, no. 1 (February 1993): 48–51. http://dx.doi.org/10.1017/s0317167100047405.

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ABSTRACT:The presence of cortical senile plaques and neurofibrillary tangles sufficient to warrant a neuropatho-logical diagnosis of Alzheimer's disease is well established in middle-aged individuals with Trisomy 21 (Down's syndrome). In contrast a relationship between Down's syndrome and Lewy bodies, one of the major neuropathological features of Parkinson's disease, has not been previously reported. In a cliniconeuropathological survey of 23 cases of Down's Syndrome, two patients, aged 50 and 56 years respectively, were found to have Lewy body formation in the substantia nigra in addition to cortical Alzheimer-type pathology. Neither case showed significant substantia nigra neuron loss although locus coeruleus loss was present in both. Since substantia nigra Lewy bodies are a characteristic neu-rohistological feature of idiopathic Parkinson's disease, their occurrence in cases of Down's syndrome with evidence of Alzheimer-type pathology supports an aetiopathological connection between Parkinson's disease, Alzheimer's disease, and Down's syndrome; and suggests that common pathogenic mechanisms may underlie aspects of neuronal degeneration in these three disorders, some of which may relate to aberrant chromosome 21 expression.

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29

Chen, Rong, Xuan Gu, and Xiaoying Wang. "α-Synuclein in Parkinson's disease and advances in detection." Clinica Chimica Acta 529 (April 2022): 76–86. http://dx.doi.org/10.1016/j.cca.2022.02.006.

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Hundza, Sandra R., William R. Hook, Christopher R. Harris, Sunny V. Mahajan, Paul A. Leslie, Carl A. Spani, Leonhard G. Spalteholz, Benjamin J. Birch, Drew T. Commandeur, and Nigel J. Livingston. "Accurate and Reliable Gait Cycle Detection in Parkinson's Disease." IEEE Transactions on Neural Systems and Rehabilitation Engineering 22, no. 1 (January 2014): 127–37. http://dx.doi.org/10.1109/tnsre.2013.2282080.

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Tetrud, J. W. "Preclinical Parkinson's disease: Detection of motor and nonmotor manifestations." Neurology 41, Issue 5, Supplement 2 (May 1, 1991): 69–71. http://dx.doi.org/10.1212/wnl.41.5_suppl_2.69.

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Senarath Yapa, S. C. "Detection of subclinical ascorbate deficiency in early Parkinson's disease." Public Health 106, no. 5 (September 1992): 393–95. http://dx.doi.org/10.1016/s0033-3506(05)80188-x.

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Dujardin, K., A. Duhamel, F. Tison, J. J. Péré, I. Bourdeix, and B. Dubois. "P1.030 Detection of Parkinson's disease dementia in routine practice." Parkinsonism & Related Disorders 15 (December 2009): S36. http://dx.doi.org/10.1016/s1353-8020(09)70152-x.

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Camara, Carmen, Pedro Isasi, Kevin Warwick, Virginie Ruiz, Tipu Aziz, John Stein, and Eduard Bakštein. "Resting tremor classification and detection in Parkinson's disease patients." Biomedical Signal Processing and Control 16 (February 2015): 88–97. http://dx.doi.org/10.1016/j.bspc.2014.09.006.

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35

Jobbagy, A., E. Furnee, P. Harcos, and M. Tarczy. "Early detection of Parkinson's disease through automatic movement evaluation." IEEE Engineering in Medicine and Biology Magazine 17, no. 2 (1998): 81–88. http://dx.doi.org/10.1109/51.664035.

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36

Ahmed, Md Toukir. "Parkinsons Disease Detection Using Machine Learning Algorithm: A Review of Literature." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3339–44. http://dx.doi.org/10.22214/ijraset.2022.44340.

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Abstract: 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.

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Ranjan, Nihar M., Gitanjali Mate, and Maya Bembde. "Detection of Parkinson's Disease using Machine Learning Algorithms and Handwriting Analysis." Journal of Data Mining and Management 8, no. 1 (March 29, 2023): 21–29. http://dx.doi.org/10.46610/jodmm.2023.v08i01.004.

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Parkinson's Disease is a progressive neurodegenerative disorder of movement that affects your ability to control movement. This disease can prove fatal if not detected at an earlier stage. Motor and non-motor symptoms are raised by the loss of dopamine-producing neurons. Currently, there is no test available to detect disease at early stages where the symptoms may be poorly characterised. Handwriting analysis is one of the traditional aspects of studying human personality and also can be used to identify the symptoms of this disease. Identifying such accurate biomarkers provides roots for better clinical diagnosis. In this paper, we proposed a system that makes use of two types of handwriting analysis, spiral and wave drawings of healthy as well as Parkinson's patients as an input to the system. For feature extraction, we are using a histogram of the oriented gradient. The developed system uses a machine learning algorithm and a random forest classifier for the detection of Parkinson's disease among patients. Our model achieved an accuracy of 86.67 % in the case of spiral drawing and 83.30% with wave drawing.

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Purba, Petrus Sianggian, and Afrizal Zein. "MENDETEKSI PENYAKIT PARKINSON DENGAN OPENCV, COMPUTER VISION, DAN SPIRAL / WAVE TEST." SAINSTECH: JURNAL PENELITIAN DAN PENGKAJIAN SAINS DAN TEKNOLOGI 32, no. 2 (May 25, 2022): 76–81. http://dx.doi.org/10.37277/stch.v32i2.1303.

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ABSTRACT Parkinson's disease is the second most common neurodegenerative disease in humans after Alzheimer's disease. The disorder causes patients to experience a variety of symptoms, including intellectual and behavioral disturbances, dementia, memory loss, muscle weakness, stiffness (slow and stiff movements), and tremors. This study describes how to detect Parkinson's disease using Open CV and how geometric images can be used to detect and predict Parkinson's. We will then examine our image dataset collected from both patients with and without Parkinson's. After reviewing the dataset, I will teach you how to use the HOG image descriptor to scale the input image and then how we can train the Random Forest classifier over the extracted features. The expected result is that the system can detect and predict Parkinson's disease from a patient with an accuracy rate above 90% Keywords: Parkinson's detection, HOG, OpenCV, Deep learning.

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Stuparu, Alina Zorina, Sanda Jurja, Alexandru Floris Stuparu, and Any Axelerad. "Narrative Review Concerning the Clinical Spectrum of Ophthalmological Impairments in Parkinson’s Disease." Neurology International 15, no. 1 (January 26, 2023): 140–61. http://dx.doi.org/10.3390/neurolint15010012.

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Ophthalmic non-motor impairments are common in Parkinson's disease patients, from the onset of the neurodegenerative disease and even prior to the development of motor symptoms. This is a very crucial component of the potential for early detection of this disease, even in its earliest stages. Since the ophthalmological disease is extensive and impacts all extraocular and intraocular components of the optical analyzer, a competent assessment of it would be beneficial for the patients. Because the retina is an extension of the nervous system and has the same embryonic genesis as the central nervous system, it is helpful to investigate the retinal changes in Parkinson's disease in order to hypothesize insights that may also be applicable to the brain. As a consequence, the detection of these symptoms and signs may improve the medical evaluation of PD and predict the illness' prognosis. Another valuable aspect of this pathology is the fact that the ophthalmological damage contributes significantly to the decrease in the quality of life of patients with Parkinson's disease. We provide an overview of the most significant ophthalmologic impairments associated with Parkinson's disease. These results certainly constitute a large number of the prevalent visual impairments experienced by PD patients.

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Bhatia, Madhulika, Amrinder Kaur, Shaveta Bhatia, Mridula, and Pallavi Dwivedi. "Detection of Parkinson’s Disease in Alzheimer’s Patients Utilizing Brain Imaging." Traitement du Signal 39, no. 4 (August 31, 2022): 1443–51. http://dx.doi.org/10.18280/ts.390439.

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Patients with Alzheimer's infection (AD) and Parkinson's sickness (PD) regularly have cover in clinical show and cognitive neuropathology proposing that these two illnesses share basic fundamental instruments. Parkinson sickness emerges from diminished dopamine creation in the mind. Patients with these two illnesses often cover in clinical introduction and cerebrum neuropathology proposing that they share basic common fundamental systems. Therefore, it become important two find the presence of common affected brain region of interest. The paper proposes the technique to find out the relation between the common affected brain areas. The work with in carried in-depth analysis of brain functional MRI scans using statistical parametric mapping on both AD, PD patients (n=35), which comprises of 5 healthy participants with average age 83, 5 AD participants with average age 75, Early mild cognitive impairment (EMCI) participants whose average age is 65, Late mild cognitive impairment (EMCI) participants whose average age is 75 and PD participants with average age of 64. Using a two sample t-test the ROI (region of interest) was noted using MarsBar plugin of SPM tool.

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Lévay, György, and William J. Bodell. "Detection of dopamine DNA adducts: potential role in Parkinson's disease." Carcinogenesis 14, no. 6 (1993): 1241–45. http://dx.doi.org/10.1093/carcin/14.6.1241.

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Hutchinson, Michael, and Ulrich Raff. "Detection of Parkinson's disease by MRI: Spin-lattice distribution imaging." Movement Disorders 23, no. 14 (October 30, 2008): 1991–97. http://dx.doi.org/10.1002/mds.22210.

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Stern, Matthew B. "The preclinical detection of Parkinson's disease: Ready for prime time?" Annals of Neurology 56, no. 2 (2004): 169–71. http://dx.doi.org/10.1002/ana.20180.

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Siderowf, Andrew, and Matthew B. Stern. "Premotor Parkinson's disease: Clinical features, detection, and prospects for treatment." Annals of Neurology 64, S2 (January 6, 2009): S139—S147. http://dx.doi.org/10.1002/ana.21462.

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Verbaan, Dagmar, Martine Jeukens-Visser, Teus Van Laar, Stephanie M. van Rooden, Erik W. Van Zwet, Johan Marinus, and Jacobus J. van Hilten. "SCOPA-cognition cutoff value for detection of Parkinson's disease dementia." Movement Disorders 26, no. 10 (May 3, 2011): 1881–86. http://dx.doi.org/10.1002/mds.23750.

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Yu, Zhenwei, Tessandra Stewart, Jan Aasly, Min Shi, and Jing Zhang. "Combining clinical and biofluid markers for early Parkinson's disease detection." Annals of Clinical and Translational Neurology 5, no. 1 (December 20, 2017): 109–14. http://dx.doi.org/10.1002/acn3.509.

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Noad, Rupert, Craig Newman, Camille Carroll, and John Zajicek. "iPAD-BASED ASSESSMENT IN PARKINSON'S DISEASE." Journal of Neurology, Neurosurgery & Psychiatry 86, no. 11 (October 14, 2015): e4.189-e4. http://dx.doi.org/10.1136/jnnp-2015-312379.94.

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BackgroundIn order to facilitate neuroprotective trials in Parkinson's disease (PD), there is a need for improved means of early disease detection and measuring disease progression. Computerised assessment may afford easier and increasingly accurate administration of motor and cognitive tests, as well as measurement of indices not readily accessible with standard testing paradigms.AimTo develop and validate iPad-based cognitive and motor measures in PD.Method62 PD patients and 42 age-matched controls completed traditional and iPad versions of the Trail Making Test (TMT) (executive function) and Knox Cube Test (visual memory), as well as a range of other measures.ResultsMedian age was 68 years; median MOCA score was 26. No participant had previously used an iPad. There was significant correlation between traditional and iPad measures: TMTa r=0.74, p<0.001; TMTb r=0.76, p<0.001; Knox r=0.63, p<0.001. Usability data were strong, 90% of participants providing positive feedback.ConclusionThis initial study has demonstrated that two iPad-based measures of cognition are acceptable to PD patients and perform similarly to traditional pen-and-paper tests. Further work will extend the analysis of the measured indices in longitudinal studies to determine correlation with disease progression, and extend the battery of iPad-based tests available for PD assessment.

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Abhishek, Jain, and Raja Rohit. "AI for the detection of neurological condition: Parkinson's disease & emotions." i-manager's Journal on Artificial Intelligence & Machine Learning 1, no. 1 (2023): 34. http://dx.doi.org/10.26634/jaim.1.1.19135.

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Artificial Intelligence (AI) is widely applied by many researchers in the measurement and analysis of signals and images in clinical medicine and the biological sciences. The role of machine learning in processing biomedical signals and its applications in medicine and healthcare is huge, and it is now in a very advanced stage. Several types of biomedical signals have been analyzed by using Deep Learning (DL), Neural Networks (NN), and Artificial Intelligence on Electrocardiogram (ECG) and Electroencephalogram (EEG) signals by many researchers. Parkinson's disease (PD) is a neurodegenerative disorder that progresses over time and is characterized by rigidity, tremor, postural instability, and non-motor symptoms caused by the loss of dopaminergic neurons in the substantia nigra. This paper analyses the current state of the art of EEG analysis using AI techniques for Parkinson's disease detection and emotion detection.

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Sarria Paja, Milton Orlando. "Automatic detection of Parkinson's disease from components of modulators in speech signals." Computer and Electronic Sciences: Theory and Applications 1, no. 1 (December 14, 2020): 71–82. http://dx.doi.org/10.17981/cesta.01.01.2020.05.

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Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. This disorder mainly affects older adults at a rate of about 2%, and about 89% of people diagnosed with PD also develop speech disorders. This has led scientific community to research information embedded in speech signal from Parkinson's patients, which has allowed not only a diagnosis of the pathology but also a follow-up of its evolution. In recent years, a large number of studies have focused on the automatic detection of pathologies related to the voice, in order to make objective evaluations of the voice in a non-invasive manner. In cases where the pathology primarily affects the vibratory patterns of vocal folds such as Parkinson's, the analyses typically performed are sustained over vowel pronunciations. In this article, it is proposed to use information from slow and rapid variations in speech signals, also known as modulating components, combined with an effective dimensionality reduction reduction approach that will be used as input to the classification system. The proposed approach achieves classification rates higher than 88%, surpassing the classical approach based on mel cepstrals coefficients (MFCC). The results show that the information extracted from slow varying components is highly discriminative for the task at hand, and could support assisted diagnosis systems for PD.

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Geeitha, S., and M. Thangamani. "Qualitative Analysis for Improving Prediction Accuracy in Parkinson's Disease Detection Using Hybrid Technique." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 393–99. http://dx.doi.org/10.1166/jctn.2019.7738.

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A PSO based SVM method has been implemented in diagnosing Parkinson's disease. This hybrid method produces parameter optimization and it helps to predict the gene expression pattern of the patient affected from Parkinson's disease. Implementing a computational tool on the PD data set alleviates the symptoms to predict accurately the occurrence of the disease. In data classification, there may arise some incomplete or missing data during pre-processing in the probabilistic model. In order to overcome this, an Expectation Maximization (EM) algorithm is implemented. The proposed Particle Swarm Optimization (PSO) based Support Vector Machine (SVM) technique is also compared with the Bayesian network model that outperforms in prediction accuracy.

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