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control of stroke of classifiers

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data-driven approach to post-stroke aphasia classification ...

Introduction. Aphasia is an impairment of language production and/or comprehension that is a common and severe consequence of stroke. 1-3 Aphasia diagnosis continues to follow a 19th century model of the neural basis of language, the Wernicke-Lichtheim model, which primarily focuses on three functional aspects of language: fluent speech production, auditory …

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AWS Marketplace: Cerebral Infarction/Stroke

A cerebral infarction is a brain lesion in which a cluster of brain cells die when they don''t get enough blood. Major risk factors for cerebral infarction include Atherosclerosis, High blood pressure, Diabetes mellitus, Smoking and Obesity. Demographics and behavioral aspects play a major role in aggravating the chance of a stroke.

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Establishing a classifier and estimation system of nitric ...

2018-7-13 · function and autoregulation in control LS. It is well known that the heart of SVMs vs ... classifier performance of sigmoid is high whenthe sum of a parameter γ and α is odd numbers. An improvement of classifier performance for NO assessment, when we vary ... 2.3 Endothelial barrier dysfunction in lacunar stroke 6 2.4 Oxidative stress and ...

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Classification of Materials and Types of Classifiers ...

2015-10-31 · Classification of Materials and Types of Classifiers. Size control of particles finer than 1 mm, are out of the practical range of conventional screens. Separation of such particles is carried out by classification. Classification …

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Comparison of machine learning methods in sEMG signal ...

2021-7-1 · Moreover, the volitional control of a robotic assistive device has been proposed to maximize the utility of an exoskeleton, which yielded better clinical outcomes of rehabilitation [6,7]. Surface electromyography (sEMG) processing has been used in volitional control of lower limb [ 8 ] and upper limb [ 9 ] exoskeleton assistive systems.

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Establishment and evaluation of prediction model for ...

2019-7-15 · Metagenome data from 1,079 individuals were collected for the healthy (control samples) and patients with one of six diseases including MS, JIA, ME/CFS, AIDS, Stroke and CRC (Table 1). The study ...

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Classification of stroke disease using machine learning ...

2019-1-25 · This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the …

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Estimation of the LDL subclasses in ischemic stroke as a ...

2020-11-13 · Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of low-density lipoproteins (LDL) subclasses in the occurrence of AIS and develop a risk xprediction model based on these characteristics to identify high-risk people. Five hundred and sixty-six patients with AIS and 197 non-AIS …

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(PDF) Use of Machine Leaning Classifiers and Sensor Data ...

Objective The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and ...

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Application of an LDA Classifier for Determining User ...

To date, control strategies for such a device remain unknown. This work examines the feasibility of using two different linear discriminant analysis classifiers to control shoulder abduction and adduction as well as external and internal rotation simultaneously, two primary degrees of freedom that have gone largely unstudied in hemiparetic stroke.

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Classifiers for Ischemic Stroke Lesion Segmentation: A ...

2015-12-16 · Motivation Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and …

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Using machine learning models to improve stroke risk …

2019-12-10 · Background With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people …

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Use of Machine Learning Classifiers and Sensor Data to ...

2019-9-4 · Original Paper Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients Eunjeong Park1, PhD; Hyuk-Jae Chang2, MD, PhD; Hyo Suk Nam3, MD, PhD 1Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Republic Of Korea 2Department of Cardiology, Yonsei University College of Medicine, …

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Identification of Acute Stroke Using Quantified Brain ...

2015-1-6 · All patients from the parent study who had confirmed strokes, and a control group of stroke mimics (those with final ED diagnoses of migraine or syncope), were selected for this study. All stroke patients underwent head CT scans. Some patients with negative CTs had further imaging with magnetic resonance imaging (MRI).

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Multi-Feature Analysis for Automated Brain Stroke ...

2021-9-7 · A. Davis and N. Gordillo, Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: A fuzzy approach, Biomed. Signal Process. Control 45 (2018) 117–127. Crossref, ISI, Google Scholar; 35. G.

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Implementation of Machine Learning Model to Predict …

2019-6-29 · control, the right time diagnosing and comprehensive analysis are other essential factors, which can ultimately save the lives [4]. Therefore, this paper has taken a small step towards saving the lives of HF patients and describes a way to improve the performance of diagnosing the patients on the bases of their medical history.

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Atrial Fibrillation Burden Signature and Near-Term ...

regression is a linear classifier frequently used for classifica-tion in medical literature,21 and the L1 penalty was imposed to control for the correlated nature of time series features and to prevent overfitting.22 We used the validation dataset to tune model hyper-parameters (Figure 2). For CNN, we assessed the following

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Myoelectric pattern recognition of hand motions for stroke ...

2020-3-1 · In the group III, the classifiers needed more features to identify the motions of the flexion-extension wrist exercise, due to the fact that some volunteers presented muscle weakness to maintain the wrist raised. Future work will focus on the implementation of a feasible myoelectric control system to improve stroke rehabilitation.

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[1308.4017] A Study on Stroke Rehabilitation through Task ...

2013-8-19 · This paper presents a study in task-oriented approach to stroke rehabilitation by controlling a haptic device via near-infrared spectroscopy-based brain-computer interface (BCI). The task is to command the haptic device to move in opposing directions of leftward and rightward movement. Our study consists of data acquisition, signal preprocessing, and classification. In …

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A New Gaze-BCI-Driven Control of an Upper Limb …

2012-12-21 · This paper proposes a new multimodal architecture for gaze-independent brain-computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. At the level of action plan, the patient''s intention is decoded by means of an active vision system, …

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An integrated machine learning approach to stroke ...

2010-7-25 · How do American stroke risk functions perform in a western European population? Neuroepidemiology, 23(5):247--253, September-October 2004. Google Scholar Cross Ref; P. A. Wolf, R. B. D''Agostino, A. J. Belanger, and W. B. Kannel. Probability of stroke: a risk profile from the framingham study. Stroke, 22:312--318, March 1991. Google Scholar ...

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Use of Machine Learning Classifiers and Sensor Data to ...

2017-4-18 · Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Hyo Suk Nam, Department of Neurology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemoon-gu, Seoul, 03722, Republic Of Korea, Phone: 82 2 2228 1617, Fax: 82 2 393 0705, Email: [email protected]

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Types of Classifiers in Mineral Processing

2016-5-26 · Spiral Classifier In mineral processing, the Akins AKA spiral or screw Classifier has been successfully used for so many years that most mill operators are familiar with its principle and operation. This classifier embodies the …

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Application of an LDA Classifier for Determining User ...

2018-7-1 · To date, control strategies for such a device remain unknown. This work examines the feasibility of using two different linear discriminant analysis classifiers to control shoulder abduction and adduction as well as external and internal rotation simultaneously, two primary degrees of freedom that have gone largely unstudied in hemiparetic stroke.

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Maximizing the Usefulness of Statistical Classifiers for ...

2018-5-19 · Maximizing the Usefulness of Statistical Classifiers for Two Populations Outline 1.Background and Motivation 2.Neutral Zones 3.Two-Stage Procedures ... • Stroke: 133,103 • Alzheimer''s disease: 93,541 • Diabetes: 76,488 ... control of False Positiv tˆ ...

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(PDF) Use of Machine Leaning Classifiers and Sensor Data ...

Objective The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm …