• IE interfaces
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• v.23 no.4
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• pp.365-371
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• 2010

In this study, we propose a novel remote health monitoring system to accurately predict Parkinson's disease severity using a signomial regression method. In order to characterize the Parkinson's disease severity, sixteen biomedical voice measurements associated with symptoms of the Parkinson's disease, are used to develop the telemonitoring model for early detection of the Parkinson's disease. The proposed approach could be utilized for not only prediction purposes, but also interpretation purposes in practice, providing an explicit description of the resulting function in the original input space. Compared to the accuracy performance with the existing methods, the proposed algorithm produces less error rate for predicting Parkinson's disease severity.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.421-428
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• 2014

Like Alzheimer's disease, Parkinson's Disease(PD) is one of the most common neurodegenerative brain disorders. PD results from the deterioration of dopaminergic neurons in the brain region called the substantia nigra. Currently there is no cure for PD, but diagnosing in its early stage is important to provide treatments for relieving the symptoms and maintaining quality of life. Unlike many diagnosis methods of PD which use a single biomarker, we developed a diagnosis method that uses both biochemical biomarkers and imaging biomarkers. Our method uses ${\alpha}$-synuclein protein levels in the cerebrospinal fluid and diffusion tensor images(DTI). It achieved an accuracy over 91.3% in the 10-fold cross validation, and the best accuracy of 72% in an independent testing, which suggests a possibility for early detection of PD. We also analyzed the characteristics of the brain fiber pathways of Parkinson's disease patients and normal elderly people.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4367-4385
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• 2016

With millions of people across the globe suffering from Parkinson's disease (PD), an objective, confirmatory test for the same is yet to be developed. This research aims to develop a system which can assist the doctor in objectively saying whether the patient is normal or under risk of PD. The proposed work combines the eye-hand co-ordination behaviour with the DaTscan images in order to determine the risk of this disorder. Initially, eye-hand coordination level of the patient is assessed through a hardware module. Then, the DaTscan image is analysed and used to extract certain geometrical parameters which shall indicate the presence of PD. These parameters are then finally fed into a Multi-Layer Perceptron Neural Network using Levenberg-Marquardt (LM) Back propagation training algorithm. Experimental results indicate that the proposed system exhibits an accuracy of around 93%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.977-978
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• 2013

• Journal of Korean Neurosurgical Society
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• v.29 no.11
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• pp.1415-1420
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• 2000

Essential tremor(ET) is the most common movement disorder however there has been little agreement in the neurologic literature regarding diagnostic criteria for ET. Familial ET is an autosomal dominant disorder presenting as an isolated postural tremor. The main feature of ET is postural tremor of the arms with later involvement of the head, voice, or legs. In previous studies, it was reported that ET susceptibility was inherited in an autosomal dominant inheritance. As with previous results, it would suggest that ET might be associated with defect of mitochondrial or nuclear DNA. Recent studies are focusing molecular genetic detection of movement disorders, such as essential tremor and restless legs syndrome. Parkinson's disease(PD) is a neurodegenerative disease involving mainly the loss of dopaminergic neurons in substantia nigra by several factors. The cause of dopaminergic cell death is unknown. Recently, it has been suggested that Parkinson's disease many result from mitochondrial dysfunction. The authors have analysed mitochondrial DNA(mtDNA) from the blood cell of PD and ET patients via long and accurate polymerase chain reaction(LA PCR). Blood samples were collected from 9 PD and 9 ET patients. Total DNA was extracted twice with phenol followed by chloroform : isoamylalcohol. For the analysis of mtDNA, LA PCR was performed by mitochondrial specific primers. With LA PCR, 1/3 16s rRNA~1/3 ATPase 6/8 and COI~3/4 ND5 regions were observed in different patterns. But, in the COI~1/3 ATPase 6/8 region, the data of PCR were observed in same pattern. This study supports the data that ET and PD are genentic disorders with deficiency of mitochondrial DNA multicomplexes.

• The Journal of Korean Medicine
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• v.30 no.4
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• pp.79-92
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• 2009

Objectives: The aim of the present study was to explore the neuroprotective effect and the possible mechanism of the PD-1 extracts on 1-methyl-4-phenyl-1,2,3,6-tetrahydrophridine (MPTP)-lesioned C57BL/6 mouse model of Parkinson's disease (PD). Methods: The mice were supplemented (or not) with 50 or 100 mg/kg/day of PD-1 for 2 weeks, after which MPTP was injected intraperitoneally. We observed that daily administration of PD-1 prevented MPTP-induced depletion of striatal DA, and maintained striatal and nigral tyrosine hydroxylase (TH) protein levels. Results: Our results demonstrated that mice treated with PD-1 prior to MPTP administration showed more abundant TH-immunopositive (TH-ir) fibers and neurons than mice given only MPTP, indicating that PD-1 protects dopaminergic striatal fibers and nigral neurons from MPTP insults. Possible neuroprotective effect of PD-1 was further studied by the detection of antiapoptotic protein (bcl-2) and proapoptotic protein (Bax). In this assay, MPTP elevated the Bax protein and decreased the bcl-2 protein, while these expressions were prevented by PD-1 pre-treatment. Conclusions: The present results suggest that PD-1 is able to protect dopaminergic neurons from MPTP-induced neuronal injury with anti-apoptotic activity being one of the possible mechanisms.

• KSBB Journal
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• v.20 no.4
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• pp.323-327
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• 2005

RT-PCR is an useful method to investigate the expression of target gene as detection tools. Although RT-PCR is the powerful detection method for tissues, it was difficult to amplify the target gene product using the single cell. To clarify the expression level of the genes related to Parkinson's disease (PD), I performed the laser dissection of single cell from Substantia nigra. I examined the mRNA expression level in the dopaminergic neuron isolated from the PD patients by the single cell RT-PCR method. It is known that tyrosine hydroxylase (TH), DOPA decarboxylase (DDC) are involved in biosynthesis of the catecholamine such as dopamine. Little has been known about the gene expression features of these enzymes in single dopaminergic neuron. I could detect the specific gene products in single cell level. The different expression was observed in PD-related gene products from the single neuron of PD patients. Interestingly, TH gene expression was significantly decreased with comparing the ratio of decrease in other PD-related genes. Hence, I represented data that indicate the RT-PCR method described in this report is an effective method in detecting a specific single-cell mRNA level related with diseases.