A powerful paradigm for cardiovascular risk stratification using multiclass, multi-label, and ensemble-based machine learning paradigms: A narrative review
Abstract Background and Motivation: Cardiovascular disease (CVD) causes the highest
mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment …
mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment …
[HTML][HTML] Cardiovascular disease/stroke risk stratification in deep learning framework: a review
The global mortality rate is known to be the highest due to cardiovascular disease (CVD).
Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as …
Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as …
Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics
Z Long, J Li, J Fan, B Li, Y Du, S Qiu, J Miao… - Frontiers in Aging …, 2023 - frontiersin.org
Introduction Multi-modal neuroimaging metrics in combination with advanced machine
learning techniques have attracted more and more attention for an effective multi-class …
learning techniques have attracted more and more attention for an effective multi-class …
Reliable detection of myocardial ischemia using machine learning based on temporal-spatial characteristics of electrocardiogram and vectorcardiogram
Background: Myocardial ischemia is a common early symptom of cardiovascular disease
(CVD). Reliable detection of myocardial ischemia using computer-aided analysis of …
(CVD). Reliable detection of myocardial ischemia using computer-aided analysis of …
Identification and localization of myocardial infarction based on analysis of ECG signal in cross spectral domain using boosted SVM classifier
N Sinha, A Das - IEEE Transactions on Instrumentation and …, 2021 - ieeexplore.ieee.org
Automated and accurate detection of myocardial infarction (MI) is a momentous task
because of its association with damage of heart muscles and sudden cardiac arrest. This …
because of its association with damage of heart muscles and sudden cardiac arrest. This …
Advanced time-frequency methods for ecg waves recognition
ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-
QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart …
QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart …
Identification of type 2 diabetes based on a ten‐gene biomarker prediction model constructed using a support vector machine algorithm
J Li, J Ding, DU Zhi, K Gu… - BioMed Research …, 2022 - Wiley Online Library
Background. Type 2 diabetes is a major health concern worldwide. The present study is
aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes …
aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes …
An adaptive ECG noise removal process based on empirical mode decomposition (EMD)
AF Hussein, WR Mohammed… - Contrast Media & …, 2022 - Wiley Online Library
The electrocardiogram (ECG) is a generally used instrument for examining cardiac
disorders. For proper interpretation of cardiac illnesses, a noise‐free ECG is often preferred …
disorders. For proper interpretation of cardiac illnesses, a noise‐free ECG is often preferred …
Optimized solutions of electrocardiogram lead and segment selection for cardiovascular disease diagnostics
Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all
12 leads, which undoubtedly results in a large amount of calculation and is not suitable for …
12 leads, which undoubtedly results in a large amount of calculation and is not suitable for …
rECGnition_v1. 0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG
A substantial amount of variability in ECG manifested due to patient characteristics hinders
the adoption of automated analysis algorithms in clinical practice. None of the ECG …
the adoption of automated analysis algorithms in clinical practice. None of the ECG …