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A survey on ECG analysis
The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network
Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance
for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods …
for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods …
Detection of abnormal heart conditions based on characteristics of ECG signals
Heart diseases are one of the most important death causes across the globe. Therefore,
early detection of heart diseases is crucial to reduce the rising death rate. Electrocardiogram …
early detection of heart diseases is crucial to reduce the rising death rate. Electrocardiogram …
[HTML][HTML] Phonocardiogram signal processing for automatic diagnosis of congenital heart disorders through fusion of temporal and cepstral features
Congenital heart disease (CHD) is a heart disorder associated with the devastating
indications that result in increased mortality, increased morbidity, increased healthcare …
indications that result in increased mortality, increased morbidity, increased healthcare …
MLBF-Net: A multi-lead-branch fusion network for multi-class arrhythmia classification using 12-lead ECG
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a
critical role in early prevention and diagnosis of cardiovascular diseases. In the previous …
critical role in early prevention and diagnosis of cardiovascular diseases. In the previous …
ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
The advance in wearable and wireless sensors technology have made it possible to monitor
multiple vital signs (eg heart rate, blood pressure) of a patient anytime, anywhere. Vital signs …
multiple vital signs (eg heart rate, blood pressure) of a patient anytime, anywhere. Vital signs …
Current trends in feature extraction and classification methodologies of biomedical signals
Biomedical signal and image processing is the study of the dynamic behavior of various bio-
signals, which benefits academics and research. Signal processing is used to assess the …
signals, which benefits academics and research. Signal processing is used to assess the …
Deep transfer learning for chronic obstructive pulmonary disease detection utilizing electrocardiogram signals
The motivation of this research is to introduce the first research on automated Chronic
Obstructive Pulmonary Disease (COPD) diagnosis using deep learning and the first …
Obstructive Pulmonary Disease (COPD) diagnosis using deep learning and the first …
A parallel ensemble learning model for fault detection and diagnosis of industrial machinery
Accurate fault detection and diagnosis (FDD) is critical to ensure the safe and reliable
operation of industrial machines. Deep learning has recently emerged as effective methods …
operation of industrial machines. Deep learning has recently emerged as effective methods …
A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs
Background and objectives In home-based context-aware monitoring patient's real-time data
of multiple vital signs (eg heart rate, blood pressure) are continuously generated from …
of multiple vital signs (eg heart rate, blood pressure) are continuously generated from …