Results 81 to 90 of about 90,766 (269)
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.Electromagnetic interference (EMI)‐shielding electrocardiogram (ECG) sensor was demonstrated for accurately monitoring human vital signals in real time. The hydrogel nanocomposite‐based ECG patch stably operated on human skin, showing excellent mechanical/electrical resilience and humidity stability.
Sang Yoon Park +4 more
wiley +1 more source
Implementation of Neural Network and feature extraction to classify ECG signals [PDF]
arXiv, 2018 This paper presents a suitable and efficient implementation of a feature extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG) signals, for detection and classification of four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial Fibrillation (AF) and differentiating them from the normal heart
arxiv
ESC Heart Failure, Volume 12, Issue 2, Page 809-818, April 2025.Abstract Aims This study aimed to evaluate the change of the main electrocardiographic (ECG) characteristics and their prognostic role across the main subtypes of cardiac amyloidosis [light‐chain amyloidosis (AL) and hereditary (ATTRv) and wild‐type transthyretin amyloidosis (ATTRwt)].
Alessia Argirò +20 more
wiley +1 more source
Hypercalcaemia Mimicking STEMI on Electrocardiography
Case Reports in Medicine, 2010 Acute coronary syndrome is a common cause of presentation to hospital. ST segment elevation on an electrocardiogram (ECG) is likely to be cardiac in origin, but in low-risk patients other causes must be ruled out.
Joseph Donovan, Mark Jackson
doaj +1 more source
ESC Heart Failure, Volume 12, Issue 2, Page 980-997, April 2025.Abstract Heart failure (HF) creates a considerable clinical, humanistic and economic burden on patients and caregivers as well as on healthcare systems. To attenuate the significant burden of HF, there is a need for enhanced management of patients with HF.
Javed Butler +8 more
wiley +1 more source
Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction [PDF]
arXiv, 2019 Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used
arxiv