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Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm
AbstractSevere atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the m...
Source: Journal of Medical Systems - May 13, 2017 Category: Information Technology Source Type: research

Combined Transcranial Direct Current Stimulation and Virtual Reality-Based Paradigm for Upper Limb Rehabilitation in Individuals with Restricted Movements. A Feasibility Study with a Chronic Stroke Survivor with Severe Hemiparesis
We present a virtual reality-based paradigm for upper limb rehabilitation that allows for interaction of individuals with restricted movements from active responses triggered when they attempt to perform a movement. The experimental system also provides multisensory stimulation in the visual, auditory, and tactile channels, and transcranial direct current stimulation coherent to the observed movements. A feasibility study with a chronic stroke survivor with severe hemiparesis who seemed to reach a rehabilitation plateau after two years of its inclusion in a physical therapy program showed clinically meaningful improvement ...
Source: Journal of Medical Systems - April 2, 2018 Category: Information Technology 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

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

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

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

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

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department
AbstractHemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance.  A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department (ED) of Kaohsiung Veteran General Hospital were collected. From 2020 January-1st to April-30th, the physicians read NCCT images without DeepCT. From 2020May-1st to August-31st, the physicians were assisted by DeepCT. The...
Source: Journal of Medical Systems - June 8, 2022 Category: Information Technology Source Type: research