• ETRI Journal
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• v.39 no.6
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• pp.851-858
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• 2017

Parkinson's disease is a growing and chronic movement disorder, and its diagnosis is difficult especially at the initial stages. In this paper, movement characteristics extracted by a computer using multilayer back propagation neural network mapping are converted to the symptoms of this disease. Then, modulation of three classifiers of C4.5, k-nearest neighbors, and support vector machine with majority voting are applied to support experts in diagnosing the disease. The purpose of this study is to choose appropriate characteristics and increase the accuracy of the diagnosis. Experiments were performed to demonstrate the improvement of Parkinson's disease diagnosis using this method.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.19-24
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• 2022

Through the development and dissemination of embedded devices, studies that may help patients are rapidly emerging. Recently, as wearable devices have become one of the ways to diagnose diseases in daily life, they are being studied as a way to assist severely ill patients to lead their daily lives. Among them, a method of detecting and giving signals to detect and solve symptoms using acceleration sensors to diagnose Parkinson's disease is being studied, and there is no study to measure and analyze various factors that can affect Parkinson's disease. To solve them, we designed and developed a wearable device, P-Band, with various sensors capable of diagnosing related symptoms, including acceleration sensors capable of diagnosing Parkinson's disease. In this paper, the overall structure of the P-Band and the description and operation method of the measurable sensors are presented. In addition, it was confirmed that the symptoms of Parkinson's patients could be determined complexly through the results measured in actual patients.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.353-358
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• 2021

Parkinson's disease is the second most common degenerative brain disease after Alzheimer's in old age. Symptoms of Parkinson's disease are factors that reduce the quality of life in daily life, such as shaking hands, slowing behavior and cognitive function. Parkinson's disease that can slow the progression of the disease through early diagnosis. To diagnoze Parkinson's disease early, an algorithm was implemented to extract features using wav2vec and to diagnose the presence or absence of Parkinson's disease with deep learning(ANN). As a results of the experiment, the accuracy was 97.47%. It was better than the results of diagnosing Parkinson's disease using the existing neural network. The audio voice file could simply reduce the experiment process and obtain improved results.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.308-313
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• 2019

Parkinson's disease, one of the three major diseases in old age, has more than 70 % of patients with speech disorders, and recently, diagnostic methods of Parkinson's disease through speech signals have been devised. In this paper, we propose a method of diagnosis of Parkinson's disease based on deep residual gated recurrent neural network using speech features. In the proposed method, the speech features for diagnosing Parkinson's disease are selected and applied to the deep residual gated recurrent neural network to classify Parkinson's disease patients. The proposed deep residual gated recurrent neural network, an algorithm combining residual learning with deep gated recurrent neural network, has a higher recognition rate than the traditional method in Parkinson's disease diagnosis.

• 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.

• 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.

• Journal of Biomedical Engineering Research
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• v.36 no.4
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• pp.103-108
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• 2015

Discrimination of Parkinson's disease (PD) from Essential tremor (ET) is often misdiagnosed in clinical practice. Since tremor is time-varying signal, and dominant and harmonic frequencies are shown in tremor only with moderate or severe symptom, there are some limitations to use frequency related features. Moreover, patients with PD or ET can suffer from both resting tremor and postural tremor. In this study, 28 patients with PD and 17 patients with ET were enrolled. Tremor was measured with accelerations on the more affected hand during resting and postural conditions. The ratio of root mean square (RMS) of resting tremor to RMS of postural tremor, the mean coefficients of autocorrelation function (ACF), and the mean of differences of two adjacent coefficients of ACF at resting and postural were calculated and compared between PD and ET. The performance showed 98% accuracy with support vector machine and leave-one-out cross validation. In addition, the method accurately differentiated the patients with tremor-dominant PD from patients with ET, with 100% accuracy. Therefore, the developed algorithm can assist clinicians in diagnosing and categorizing patients with tremor, especially, patients with mild symptom or the early stage of a disease, for proper treatment.

• Progress in Medical Physics
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• v.14 no.4
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• pp.268-278
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• 2003

Purpose：The aim of this study was to evaluate the striatal binding ratio, the anterior/posterior ratio and reproducibility using a template based registration method using the standard MR template as a replacement for each patients MR image. Materials and Methods：This study analyzed the 123I IPT SPECT images of 30 patients with IPD, who were subdivided into 17 patients (56.6$\pm$10.8 yr, M/F : 8/9.) with mild IPD, and 13 patients (56.4$\pm$11.1 yr, M/F : 8/5) with severe IPD. In addition, 11 normal controls (57.8$\pm$14.4 yr, M/F : 4/7) were also analyzed. The ROIs were positioned manually in the same slice showing the highest striatal activity using the traditional manual method, whereas those were positioned automatically in a mid striatal slice of the SPECT image coregistered to the standard T1 weighted MR template. Results : The specific binding ratio (SBR) obtained using the template based registration method strongly correlated with those using the manual method in all groups : normal controls (r=0.85, P<0.001), mild IPD (r=0.84, P<0.001) and severe IPD (r=0.7, P=0.01). The SBRs obtained using both methods were significantly different among the three groups (P=0.05) and the SBRs obtained by the template based registration method were higher than those by the manual method (P=0.05) in all three groups. The APRs obtained by the template based registration correlated with those using manual method in only mild IPD (r=0.72, P=0.0). The APRs obtained by the template based registration method were significantly different from the normal controls and those with mild or severe IPD (P<0.05), whereas those obtained using the manual method were not significantly different among the three groups (P＞0.1). The reproducibility (rmsCV) of the template based registration method was 7.2% (normal controls：5.2%, mild IPD：4.2%, severe IPD：10.8%), whereas the reproducibility of the manual method was 31% (normal controls：19.7%, mild IPD：21.7%, severe IPD：46.2%). Conclusion：These results show that the use of $^{123}$ I-IPT SPECT for assessing IPD is affected by the methods used to position the striatal ROI. The template based registration method using the standard MR template can be useful in diagnosing IPD and assessing the disease severity with a high reproducibility. Therefore, the template based registration method appears to be a good replacement for the manual method.