Strokes

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose ...

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, hyperglycemia, dyslipidemia, and a range of risk factors from retinal fundus images obtained from a cross-sectional study of chronic diseases in rural ...

Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance.

PURPOSE: To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast...

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial pa...

Acute and sub-acute stroke lesion segmentation from multimodal MRI.

BACKGROUND AND OBJECTIVE: Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-cri...

Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms.

OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (...

Olle Höök Lectureship 2019: The changing world of stroke rehabilitation.

The paper presents a summary of the Olle Höök lecture, which was presented at the Baltic North-Sea Forum in Oslo, Sweden, in October 2019. The paper a...

Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography.

BACKGROUND: Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; ho...

Machine learning volumetry of ischemic brain lesions on CT after thrombectomy-prospective diagnostic accuracy study in ischemic stroke patients.

PURPOSE: Ischemic lesion volume (ILV) is an important radiological predictor of functional outcome in patients with anterior circulation stroke. Our a...

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection ...

Quantitative Assessment of Motor Function for Patients with a Stroke by an End-Effector Upper Limb Rehabilitation Robot.

With the popularization of rehabilitation robots, it is necessary to develop quantitative motor function assessment methods for patients with a stroke...

Three-dimensional modelling of wheelchair contrived with lower limb exoskeleton for right hemiplegic dysfunction.

Hemiplegia is a type of paralysis that affects one side of the body due to stroke, characterizing severe weakness or rigid movement. Many people of di...

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome.

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical ...

Augmented Performance Feedback during Robotic Gait Therapy Results in Moderate Intensity Cardiovascular Exercise in Subacute Stroke.

BACKGROUND: Low cardiovascular fitness is common poststroke. Conventional subacute stroke rehabilitation does not meet Australian National Stroke Guid...

A Novel Soft Robotic Supernumerary Hand for Severely Affected Stroke Patients.

Upper limb functions are severely affected in 23% of the chronic stroke patients, compromising their life quality. To re-enable hand use, providing a ...

Ratchet-integrated pneumatic actuator (RIPA): a large-stroke soft linear actuator inspired by sarcomere muscle contraction.

Pneumatic artificial muscles (PAMs) have a wide range of robotics applications, especially in soft robots, for their ability to generate linear force ...

Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.

BACKGROUND: In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to...

Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.

. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for cl...

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to...

Stroke Prediction with Machine Learning Methods among Older Chinese.

Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with...