[Retracted] Machine Learning‐Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic …
A Javeed, SU Khan, L Ali, S Ali… - … Methods in Medicine, 2022 - Wiley Online Library
One of the leading causes of deaths around the globe is heart disease. Heart is an organ
that is responsible for the supply of blood to each part of the body. Coronary artery disease …
that is responsible for the supply of blood to each part of the body. Coronary artery disease …
The use of time-frequency moments as inputs of lstm network for ecg signal classification
This paper refers to the method of using the deep neural long-short-term memory (LSTM)
network for the problem of electrocardiogram (ECG) signal classification. ECG signals …
network for the problem of electrocardiogram (ECG) signal classification. ECG signals …
The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models
Cardiovascular disease (CVD) is the most fatal disease in the world, so its accurate and
automated detection in the early stages will certainly support the medical expert in timely …
automated detection in the early stages will certainly support the medical expert in timely …
Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification
P Yang, D Wang, WB Zhao, LH Fu, JL Du… - … Signal Processing and …, 2021 - Elsevier
Automatic heartbeat classification technology based on the ECG plays an important role in
assisting doctors with arrhythmia diagnosis. While many heartbeat classification studies can …
assisting doctors with arrhythmia diagnosis. While many heartbeat classification studies can …
ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG
The electrocardiogram (ECG) is a standard test to monitor the activity of the heart. Many
cardiac abnormalities are manifested in the ECG including arrhythmia that refers to an …
cardiac abnormalities are manifested in the ECG including arrhythmia that refers to an …
Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks
JS Huang, BQ Chen, NY Zeng, XC Cao, Y Li - Journal of Ambient …, 2023 - Springer
Accurate classification of electrocardiogram (ECG) signals is of significant importance for
automatic diagnosis of heart diseases. In order to enable intelligent classification of …
automatic diagnosis of heart diseases. In order to enable intelligent classification of …
Deep learning algorithm classifies heartbeat events based on electrocardiogram signals
Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their
numerous complications mean that many countries remain unable to prevent the rapid …
numerous complications mean that many countries remain unable to prevent the rapid …
Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models
In this study, the cyclic of a spark ignition engine using octane fuel is modeled using extreme
learning machine, an emergent technology related to single-hidden layer feedforward …
learning machine, an emergent technology related to single-hidden layer feedforward …
A new image steganography method with optimum pixel similarity for data hiding in medical images
Steganography is one of the approaches used in data hiding. Image steganography, is a
type of steganography that the image is used as a covering object. Data hiding capacity and …
type of steganography that the image is used as a covering object. Data hiding capacity and …
FPGA-based system for artificial neural network arrhythmia classification
The automatic detection and cardiac classification are essential tasks for real-time cardiac
diseases diagnosis. In this context, this paper describes a field programmable gates array …
diseases diagnosis. In this context, this paper describes a field programmable gates array …