[PDF][PDF] Arrhythmia modern classification techniques: A review
M Saber, M Abotaleb - J. Artif. Intell. Metaheuristics, 2022 - researchgate.net
Artificial intelligence methods are utilized in biological signal processing to locate and
extract interesting data. The examination of ECG signal characteristics is crucial for the …
extract interesting data. The examination of ECG signal characteristics is crucial for the …
A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach
ASM Faizal, TM Thevarajah, SM Khor… - Computer methods and …, 2021 - Elsevier
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global
health issue. Traditionally, statistical models are used commonly in the risk prediction and …
health issue. Traditionally, statistical models are used commonly in the risk prediction and …
[PDF][PDF] Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution.
Electrocardiogram (ECG) signal is a measure of the heart's electrical activity. Recently, ECG
detection and classification have benefited from the use of computer-aided systems by …
detection and classification have benefited from the use of computer-aided systems by …
Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ECG
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial
diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can …
diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can …
ECG heartbeat classification using multimodal fusion
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
Predicting aqueous adsorption of organic compounds onto biochars, carbon nanotubes, granular activated carbons, and resins with machine learning
Predictive models are useful tools for aqueous adsorption research; existing models such as
multilinear regression (MLR), however, can only predict adsorption under specific …
multilinear regression (MLR), however, can only predict adsorption under specific …
[HTML][HTML] A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings
Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are
common in low-and middle-income countries (LMICs). Accurate referrals can reduce the …
common in low-and middle-income countries (LMICs). Accurate referrals can reduce the …
Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds
M Gjoreski, A Gradišek, B Budna, M Gams… - Ieee …, 2020 - ieeexplore.ieee.org
Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is
increasing by 2% annually. Despite the significant burden that CHF poses and despite the …
increasing by 2% annually. Despite the significant burden that CHF poses and despite the …
Time adaptive ECG driven cardiovascular disease detector
Electrocardiograms (ECGs) are widely used to detect cardiovascular disease (CVD). Deep
learning is a topic of interest in healthcare, in which timely detection of ECG anomalies can …
learning is a topic of interest in healthcare, in which timely detection of ECG anomalies can …
Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis
Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous
in the daily practice of medicine largely due to several crucial unmet needs of healthcare …
in the daily practice of medicine largely due to several crucial unmet needs of healthcare …