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, a novel learning algorithm for single-hidden-layer feedforward neural networks
(SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The …

Abstract Background and Objective: Early detection of arrhythmias has become critical due
to the increased mortality from cardiovascular disease, and ECG is an effective tool for …

Arrhythmia classification is the need of the hour as the world is reporting a higher death troll
as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia …

Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will
seriously harm the life and health of patients. Traditional deep learning methods have weak …

Objective: Global (inter-patient) ECG classification for arrhythmia detection over
Electrocardiogram (ECG) signal is a challenging task for both humans and machines …

Electrocardiogram (ECG), non-stationary signals, is extensively used to evaluate the rate
and tuning of heartbeats. The main purpose of this paper is to provide an overview of …

Autism spectrum disorder (ASD) is a lifelong neurological condition that affects how a
person interacts and learns. The early and accurate diagnosis of ASD is vital to developing a …

Feedforward neural networks (FFNN) have been utilised for various research in machine
learning and they have gained a significantly wide acceptance. However, it was recently …

Time series classification (TSC) aims to predict the class label of a given time series. Modern
applications such as appliance modelling require to model an abundance of long time …