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Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis
AbstractAutomatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision b...
Source: Journal of Medical Systems - August 31, 2020 Category: Information Technology Source Type: research

Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network
AbstractFew studies in the literature have researched the use of surface electromyography (sEMG) for motor assessment post-stroke due to the complexity of this type of signal. However, recent advances in signal processing and machine learning have provided fresh opportunities for analyzing complex, non-linear, non-stationary signals, such as sEMG. This paper presents a method for identification of the upper limb movements from sEMG signals using a combination of digital signal processing, that is discrete wavelet transform, and the enhanced probabilistic neural network (EPNN). To explore the potential of sEMG signals for m...
Source: Journal of Medical Systems - August 22, 2020 Category: Information Technology Source Type: research

RapidAI snags FDA clearance for neuroimaging analysis device
RapidAI, a health tech company specializing in stroke imaging, has received clearance from the FDA for its Rapid ASPECTS neuroimaging analysis device. The product was designed to improve physicians ’ interpretations of Non-Contrast CT (NCCT) scans using the automated ASPECT score, according to the company. It uses the Alberta Stroke Program Early CT Scoring (ASPECTS) and machine learning to come up with an ASPECTS score for certain regions of the brain that have early signs of brain infarct ion.
Source: mobihealthnews - July 7, 2020 Category: Information Technology Source Type: news

Mobile Application as a Learning Aid for Nurses and Nursing Students to Identify and Care for Stroke Patients: Pretest and Posttest Results
In this study, a total of 115 nurses from health services in the South of Brazil and 35 nursing students of a community university participated. The stages focused on development, modeling of clinical cases, problem-based learning, pretest (before) app use, and posttest (after) use of the app. The results of the pretest and posttest corrections showed a substantial statistical difference (P
Source: CIN: Computers, Informatics, Nursing - July 1, 2020 Category: Information Technology Tags: FEATURES Source Type: research

Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning.
CONCLUSION: The developed models outperformed previously published models predicting dysphagia. In future, an implementation in the clinical workflow is needed to determine the clinical benefit. PMID: 32578538 [PubMed - in process]
Source: Studies in Health Technology and Informatics - June 26, 2020 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research

Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health.
Conclusions: Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research. PMID: 32547805 [PubMed]
Source: Healthcare Informatics Research - June 19, 2020 Category: Information Technology Tags: Healthc Inform Res Source Type: research

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network
AbstractAtrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in t...
Source: Journal of Medical Systems - May 9, 2020 Category: Information Technology Source Type: research

A stacked LSTM for atrial fibrillation prediction based on multivariate ECGs
AbstractAtrial fibrillation (AF) is an irregular and rapid heart rate that can increase the risk of various heart-related complications, such as the stroke and the heart failure. Electrocardiography (ECG) is widely used to monitor the health of heart disease patients. It can dramatically improve the health and the survival rate of heart disease patients by accurately predicting the AFs in an ECG. Most of the existing researches focus on the AF detection, but few of them explore the AF prediction. In this paper, we develop a recurrent neural network (RNN) composed of stacked LSTMs for AF prediction, which called SLAP. This ...
Source: Health Information Science and Systems - April 20, 2020 Category: Information Technology Source Type: research

Assessing stroke severity using electronic health record data: a machine learning approach
Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text i...
Source: BMC Medical Informatics and Decision Making - January 8, 2020 Category: Information Technology Authors: Emily Kogan, Kathryn Twyman, Jesse Heap, Dejan Milentijevic, Jennifer H. Lin and Mark Alberts Tags: Research article Source Type: research

Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A  Meta-Analysis
This study confers the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders. Here, one hundred and thirty-six different articles related to neurological and neuropsychiatric disorders diagnosed using different deep learning techniques are studied. The morbidity and mortality rate of major neuropsychiatric and neurological disorders has also bee n delineated. The performance and publication trend of different deep learning techniques employed in the investigation of these diseases has been examined and analyzed. Different performance metrics ...
Source: Journal of Medical Systems - January 3, 2020 Category: Information Technology Source Type: research

Classification and prediction of diabetes disease using machine learning paradigm
ConclusionThe combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.
Source: Health Information Science and Systems - January 2, 2020 Category: Information Technology Source Type: research

Using machine learning models to improve stroke risk level classification methods of China national stroke screening
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 nationa...
Source: BMC Medical Informatics and Decision Making - December 10, 2019 Category: Information Technology Authors: Xuemeng Li, Di Bian, Jinghui Yu, Mei Li and Dongsheng Zhao Tags: Research article Source Type: research

Cochrane author ’s work acknowledged in NIHR co-production publication
Cochrane author, and joint Co-ordinating editor, Alex Pollock, of Glasgow Caledonian University has seen her ground-breaking work in co-producing a Cochrane review included in a new UK ’s National Institute of Health Research (NIHR) INVOLVE publication.Alex involved stroke survivors, carers, physiotherapists and educators in an update of a Cochrane systematic review relating to physiotherapy after stroke. Her innovative work was included inCo-production in Action Number Two, as an example of good practice in Co-production, published in November 2019 by INVOLVE. You can hear Alex talk about her work in a webinar recorded ...
Source: Cochrane News and Events - December 3, 2019 Category: Information Technology Authors: Muriah Umoquit Source Type: news

A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning
AbstractBrain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies...
Source: Journal of Medical Systems - October 22, 2019 Category: Information Technology Source Type: research

A Platform for Collection and Analysis of Image Data on Stroke.
Authors: Dolotova D, Donitova V, Arhipov I, Sharifullin F, Zagriazkina T, Kobrinskii B, Gavrilov A Abstract Identifying imaging biomarkers (IBs) of stroke remains a priority in neurodiagnostics. There is a number of different methods for image analysis and learning rules applicable in this field, but all of them require large arrays of DICOM images and clinical data. In order to amass such dataset,we havedesigneda platform for systematic collection of clinical data and medical images in different modalities. The platform provides easy-to-use tools to create formalized radiology reports, contour and tag the regions ...
Source: Studies in Health Technology and Informatics - July 28, 2019 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research

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