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[HTML][HTML] Analysis of various techniques for ECG signal in healthcare, past, present, and future
Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …
Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets
Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related
complications that can increase the risk of strokes and heart failure. Manual …
complications that can increase the risk of strokes and heart failure. Manual …
[HTML][HTML] Phonocardiogram signal processing for automatic diagnosis of congenital heart disorders through fusion of temporal and cepstral features
Congenital heart disease (CHD) is a heart disorder associated with the devastating
indications that result in increased mortality, increased morbidity, increased healthcare …
indications that result in increased mortality, increased morbidity, increased healthcare …
[HTML][HTML] Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review
Aims Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is
essential in order to prevent complications and optimize treatment of AF. Many algorithms …
essential in order to prevent complications and optimize treatment of AF. Many algorithms …
[HTML][HTML] A deep learning approach for atrial fibrillation classification using multi-feature time series data from ecg and ppg
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …
Afibri-net: A lightweight convolution neural network based atrial fibrillation detector
By considering limited resource-constraints of medical devices and advanced deep learning
networks, in this paper, we explore a lightweight convolutional neural network (CNN) based …
networks, in this paper, we explore a lightweight convolutional neural network (CNN) based …
An automated detection of atrial fibrillation from single‑lead ECG using HRV features and machine learning
Background Atrial fibrillation (AF) is a disorder of the heart rhythm where irregular and rapid
heartbeats are observed. This supraventricular arrhythmia may increase the risk of blood …
heartbeats are observed. This supraventricular arrhythmia may increase the risk of blood …
Multilevel classification and detection of cardiac arrhythmias with high-resolution superlet transform and deep convolution neural network
Atrial fibrillation and ventricular fibrillation are the two most common cardiac arrhythmia.
These cardiac arrhythmias cause heart strokes and other heart complications leading to an …
These cardiac arrhythmias cause heart strokes and other heart complications leading to an …
[HTML][HTML] An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease
Introduction Cardiovascular disease (CVD) is a leading cause of death and disability
globally, with ischemia and arrhythmias being critical contributors. Ischemia, due to reduced …
globally, with ischemia and arrhythmias being critical contributors. Ischemia, due to reduced …
Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability
S Porebski - Expert systems with applications, 2022 - Elsevier
Objective: Decision support systems focus on their interpretability with a different caution.
The majority of approaches utilize reliable optimization techniques to achieve high …
The majority of approaches utilize reliable optimization techniques to achieve high …