ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network
J Huang, B Chen, B Yao, W He - IEEE access, 2019 - ieeexplore.ieee.org
The classification of electrocardiogram (ECG) signals is very important for the automatic
diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of …
diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of …
[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 …
Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy
Y Cao, Y Sun, G Xie, T Wen - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
The train plug door is the only way for passengers getting on and off. Its failures will make
train operation ineffective. Taking the developed digital signal processing technologies into …
train operation ineffective. Taking the developed digital signal processing technologies into …
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 …
ECG classification using 1-D convolutional deep residual neural network
An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular
diseases (CVDs). The traditional ECG classification methods have complex signal …
diseases (CVDs). The traditional ECG classification methods have complex signal …
Potential of front face fluorescence spectroscopy and fluorescence imaging in discriminating adulterated extra-virgin olive oil with virgin olive oil
KA Omwange, DF Al Riza, Y Saito, T Suzuki, Y Ogawa… - Food Control, 2021 - Elsevier
Due to the health benefits of extra virgin olive oil (EVOO) and its increasing consumption
patterns, adulteration of these oils using lower grade olive oils like virgin olive oil (VOO) and …
patterns, adulteration of these oils using lower grade olive oils like virgin olive oil (VOO) and …
Novel ECG signal classification based on KICA nonlinear feature extraction
H Li, H Liang, C Miao, L Cao, X Feng, C Tang… - Circuits, Systems, and …, 2016 - Springer
Electrocardiogram (ECG) signal feature extraction is important in diagnosing cardiovascular
diseases. This paper presents a new method for nonlinear feature extraction of ECG signals …
diseases. This paper presents a new method for nonlinear feature extraction of ECG signals …
Heartbeat classification by using a convolutional neural network trained with Walsh functions
From recent studies, it is observed that convolutional neural networks are proved to be
extremely successful in classification problems. Accurate and fast classification of …
extremely successful in classification problems. Accurate and fast classification of …
Spectral cross-domain neural network with soft-adaptive threshold spectral enhancement
Electrocardiography (ECG) signals can be considered as multivariable time series (TS). The
state-of-the-art ECG data classification approaches, based on either feature engineering or …
state-of-the-art ECG data classification approaches, based on either feature engineering or …
Ecg-based arrhythmia classification using recurrent neural networks in embedded systems
L Falaschetti, M Alessandrini, G Biagetti… - Procedia Computer …, 2022 - Elsevier
Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing
million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently …
million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently …