Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques

CH Karadal, MC Kaya, T Tuncer, S Dogan… - Expert Systems with …, 2021 - Elsevier
Automated classification of remote sensing images is one of the complex issues in robotics
and machine learning fields. Many models have been proposed for remote sensing image …

A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

A Subasi, S Dogan, T Tuncer - Journal of Ambient Intelligence and …, 2023 - Springer
Electrocardiography (ECG) signal recognition is one of the popular research topics for
machine learning. In this paper, a novel transformation called tower graph transformation is …

The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models

HM Rai, K Chatterjee, S Dashkevych - Computers in Biology and Medicine, 2022 - Elsevier
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 …

A novel interpretable method based on dual-level attentional deep neural network for actual multilabel arrhythmia detection

Y Jin, J Liu, Y Liu, C Qin, Z Li, D Xiao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Arrhythmia accounts for more than 80% of sudden cardiac death, and its incidence rate has
increased rapidly recently. Nowadays, many studies have applied artificial intelligence (AI) …

Sudden cardiac death prediction based on the complete ensemble empirical mode decomposition method and a machine learning strategy by using ECG signals

MA Centeno-Bautista, AV Perez-Sanchez… - Measurement, 2024 - Elsevier
Cardiovascular diseases are a significant global health problem, often culminating in
sudden cardiac death (SCD). Approximately 4 million annual deaths worldwide are …

Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network

ÖF Ertuğrul, E Acar, E Aldemir, A Öztekin - Biomedical Signal Processing …, 2021 - Elsevier
Electrocardiography has been employed successfully in medicine for many years to provide
vital knowledge about the cardiovascular system. Although processing and evaluation of …

Review of closed-loop brain–machine interface systems from a control perspective

H Pan, H Song, Q Zhang, W Mi - IEEE Transactions on Human …, 2022 - ieeexplore.ieee.org
In recent years, brain–machine interface (BMI) technology has made great progress in
controlling external devices and restoring motor function for people with disabilities. To …

Parallel classification model of arrhythmia based on DenseNet-BiLSTM

Y Gan, J Shi, W He, F Sun - Biocybernetics and Biomedical Engineering, 2021 - Elsevier
In order to improve the classification performance of the model for different kinds of
arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is …

[HTML][HTML] ECG signal fusion reconstruction via hash autoencoder and margin semantic reinforcement

Y Fang, C Wang, Y Ren, F Xu - Journal of King Saud University-Computer …, 2024 - Elsevier
The ECG signal is often accompanied by noise, which can affect its shape characteristics, so
it is important to perform signal de-noising. However, the commonly used signal noise …

A fast and accurate recognition of ECG signals based on ELM-LRF and BLSTM algorithm

F Qiao, B Li, Y Zhang, H Guo, W Li, S Zhou - IEEE Access, 2020 - ieeexplore.ieee.org
Extreme learning machine based on local receptive fields (ELM-LRFs) is a very fast method
that can be used for feature extraction and classification. Bidirectional long-short time …