Emotion recognition in EEG signals using deep learning methods: A review
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …
planning, reasoning, and other mental states. As a result, they are considered a significant …
FPGA-based real-time epileptic seizure classification using Artificial Neural Network
Epilepsy is a neurological disorder characterised by unusual brain activity widely known as
seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently …
seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently …
[HTML][HTML] Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals
Electrocardiogram (ECG) is a widely used technique to diagnose cardiovascular diseases. It
is a non-invasive technique that represents the cyclic contraction and relaxation of heart …
is a non-invasive technique that represents the cyclic contraction and relaxation of heart …
FPGA-based implementation of classification techniques: A survey
Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …
large dataset in a reasonable time has become a major challenge. This made classification …
[HTML][HTML] Identification of high-risk COVID-19 patients using machine learning
MA Quiroz-Juárez, A Torres-Gómez, I Hoyo-Ulloa… - Plos one, 2021 - journals.plos.org
The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory
syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss …
syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss …
A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals
This work presents an efficient hybridized approach for the classification of
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
Homecare-oriented ECG diagnosis with large-scale deep neural network for continuous monitoring on embedded devices
S Ran, X Yang, M Liu, Y Zhang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The accurate electrocardiogram (ECG) interpretation is important for several potentially life-
threatening cardiac diseases. Recently developed deep learning methods show their ability …
threatening cardiac diseases. Recently developed deep learning methods show their ability …
Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture
Heavy-duty CNC machines are important equipment in manufacturing large-scale and high-
end products. During the machining processes, a significant amount of heat is generated to …
end products. During the machining processes, a significant amount of heat is generated to …
[HTML][HTML] Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA
This article introduces a method for realizing the Gaussian activation function of radial-basis
(RBF) neural networks with their hardware implementation on field-programmable gaits area …
(RBF) neural networks with their hardware implementation on field-programmable gaits area …
ULECGNet: An ultra-lightweight end-to-end ECG classification neural network
ECG classification is a key technology in intelligent electrocardiogram (ECG) monitoring. In
the past, traditional machine learning methods such as support vector machine (SVM) and K …
the past, traditional machine learning methods such as support vector machine (SVM) and K …