• 산업공학
• /
• 제23권4호
• /
• pp.365-371
• /
• 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.

• 센서학회지
• /
• 제32권6호
• /
• pp.378-385
• /
• 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.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2019년도 춘계학술대회
• /
• pp.283-284
• /
• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제3권10호
• /
• pp.421-428
• /
• 2014

파킨슨병은 뇌의 흑질 영역에서 도파민계 신경이 파괴되는 질병으로 알츠하이머병과 함께 대표적인 퇴행성 뇌 질환이다. 현재까지 병을 완치시킬 수 있는 치료법은 없지만 병의 진행을 완화시킬 수 있는 치료법이 존재하기 때문에 병의 진단이 굉장히 중요하다. 파킨슨병을 진단하기 위한 과거의 연구는 대부분 단일 바이오마커를 이용한 것으로 이러한 방법은 파킨슨병 환자를 높은 정확도로 진단할 수 있지만 정상인에 대한 진단은 상대적으로 낮은 성능의 한계성이 존재한다. 따라서 본 연구에서는 생화학적 바이오마커인 뇌척수액 내의 ${\alpha}$-synuclein 단백질 수치와 영상학적 바이오마커인 확산 텐서 영상의 여러 모수들을 결합하여 특징으로 사용하는 파킨슨병 진단 모델을 개발하고 성능을 평가하였다. 진단을 위해 개발된 모든 모델은 10-fold cross validation 성능평가에서 정확도가 최고 91.3%의 높은 성능을 보였으며, test 성능평가에서는 확산 텐서 영상의 모수들 중 FA와 ${\alpha}$-synuclein 단백질 수치가 결합된 모델, MO와 ${\alpha}$-synuclein 단백질 수치가 결합된 두 모델에서 최고 72%의 정확도 성능을 보여 파킨슨병의 진단에 유용하게 사용될 수 있는 가능성을 제시하였다. 파킨슨병의 진단을 위해 개발된 모델의 영상학적 특징 벡터를 통하여 파킨슨병 환자와 정상인의 신경섬유 경로의 특징을 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권9호
• /
• pp.4367-4385
• /
• 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)
• /
• 제17권10호
• /
• pp.2627-2642
• /
• 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.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2013년도 춘계학술대회 논문집
• /
• pp.977-978
• /
• 2013

• Journal of Korean Neurosurgical Society
• /
• 제29권11호
• /
• pp.1415-1420
• /
• 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.

• 대한한의학회지
• /
• 제30권4호
• /
• pp.79-92
• /
• 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
• /
• 제20권4호
• /
• pp.323-327
• /
• 2005

조직을 이용한 역전사 (RT)-PCR법을 이용하면 원하는 특정유전자의 발현을 비교적 정확하게 알 수 있지만 조직의 RNA를 이용하므로 세포단위의 정확한 유전자 발현을 알기에는 한계가 있다. 특히 그 기능과 성질이 다른 세포가 무수하게 많이 혼재하는 두뇌와 같은 조직은 신경계의 각종 뉴런(신경세포), 글리어 (glial cell) 등이 서로 얽혀 있다. 대표적인 신경세포의 degeneration 질병으로는 파킨슨병 (Parkinson's disease; PD)이 있다. 파킨슨병은 사람의 신경세포 관련 질병에 있어서 가장 일반적인 질병의 하나이다. PD의 가장 중요한 원인은 도파민 생성 신경세포의 퇴행 혹은 사멸에 기인하여 도파민 (dopamine)이라는 신경전달물질이 감소하는 것이 그 원인이다. 도파민과 같은 카테콜아민의 생합성에 관련된 효소는 타이로신 하이드록실레이스 (TH), 도파 데카르복실레이스 (DDC) 등이 알려져 있다. 그러나 그런 효소들의 생화학적 연구는 많이 되어 있음에도 불구하고 단일 흑질 신경세포에서의 이들 관련 유전자의 발현 양상에 대해서는 알려진 바가 거의 없다. PD와 관련된 유전자의 발현 정도를 밝히기 위하여, 레이저 다이섹터 (laser micro-dissector)에 의한 단일 신경세포의 분리에 착수하였다. 정해진 방법에 따라 정상 대조구 (비PD)와 PD 환자에서 각각 한 개 또는 여러 개를 성공적으로 분리한 흑질 신경세포를 이용하여 유전자 특이적 프라이머를 사용하여 RT-PCR을 행하였다. 그 결과, 단 한 개의 신경세포에서도 여러 개의 세포를 사용한 것과 같은 동일한 결과를 얻는 데 성공하였다. PD환자의 뇌에서 분리한 10개의 독립적인 세포의 예에서는 각 세포간의 발현차이가 인정되었으며, 특히 TH 유전자의 발현은 상당히 높은 확률로 검출되지 않았다. 이 결과로 단일 신경세포에서의 mRNA양을 검출하기 위해서는 본 본문의 RT-PCR법이 효과적인 방법임을 알 수 있다.