Trends in extreme learning machines: A review
Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …
recently. In this review, we aim to report the current state of the theoretical research and …
Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey
Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow
or too fast. It occurs due to improper electrical impulses that coordinates the heart beats …
or too fast. It occurs due to improper electrical impulses that coordinates the heart beats …
Evaluation of a decided sample size in machine learning applications
Background An appropriate sample size is essential for obtaining a precise and reliable
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …
[HTML][HTML] ECG-based heartbeat classification for arrhythmia detection: A survey
An electrocardiogram (ECG) measures the electric activity of the heart and has been widely
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
ECG classification using wavelet packet entropy and random forests
T Li, M Zhou - Entropy, 2016 - mdpi.com
The electrocardiogram (ECG) is one of the most important techniques for heart disease
diagnosis. Many traditional methodologies of feature extraction and classification have been …
diagnosis. Many traditional methodologies of feature extraction and classification have been …
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals
Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an
electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart …
electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart …
Electrocardiogram soft computing using hybrid deep learning CNN-ELM
S Zhou, B Tan - Applied Soft Computing, 2020 - Elsevier
Electrocardiogram (ECG) can reflect the state of human heart and is widely used in clinical
cardiac examination. However, the electrocardiogram signal is very weak, the anti …
cardiac examination. However, the electrocardiogram signal is very weak, the anti …
Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system
P Pławiak - Expert Systems with Applications, 2018 - Elsevier
This article presents an innovative research methodology that enables the efficient
classification of cardiac disorders (17 classes) based on ECG signal analysis and an …
classification of cardiac disorders (17 classes) based on ECG signal analysis and an …
Extreme learning machine and its applications
S Ding, X Xu, R Nie - Neural Computing and Applications, 2014 - Springer
Recently, a novel learning algorithm for single-hidden-layer feedforward neural networks
(SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The …
(SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The …
Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks
Effective monitoring of heart patients according to heart signals can save a huge amount of
life. In the last decade, the classification and prediction of heart diseases according to ECG …
life. In the last decade, the classification and prediction of heart diseases according to ECG …