Deep learning-based ECG arrhythmia classification: A systematic review
Deep learning (DL) has been introduced in automatic heart-abnormality classification using
ECG signals, while its application in practical medical procedures is limited. A systematic …
ECG signals, while its application in practical medical procedures is limited. A systematic …
A systematic review of machine learning and IoT applied to the prediction and monitoring of cardiovascular diseases
A Cuevas-Chavez, Y Hernandez, J Ortiz-Hernandez… - Healthcare, 2023 - mdpi.com
According to the Pan American Health Organization, cardiovascular disease is the leading
cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper …
cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper …
BAED: A secured biometric authentication system using ECG signal based on deep learning techniques
Biometric authentication technology has become increasingly common in our daily lives as
information protection and control regulation requirements have grown worldwide. A …
information protection and control regulation requirements have grown worldwide. A …
A novel proposed CNN–SVM architecture for ECG scalograms classification
Nowadays, the number of sudden deaths due to heart disease is increasing with the
coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) …
coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) …
Ensemble machine learning framework for predicting maternal health risk during pregnancy
Maternal health risks can cause a range of complications for women during pregnancy. High
blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health …
blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health …
Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals
Real-time data collection and pre-processing have enabled the recognition, realization, and
prediction of diseases by extracting and analysing the important features of physiological …
prediction of diseases by extracting and analysing the important features of physiological …
Novel cascade filter design of improved sparse low-rank matrix estimation and kernel adaptive filtering for ECG denoising and artifacts cancellation
AS Eltrass - Biomedical Signal Processing and Control, 2022 - Elsevier
ElectroCardioGram (ECG) signals are highly vulnerable to disturbances caused by noise
and artifact sources which can degrade the ECG signal quality and increase the difficulty in …
and artifact sources which can degrade the ECG signal quality and increase the difficulty in …
Optical electrocardiogram based heart disease prediction using hybrid deep learning
AL Golande, T Pavankumar - Journal of Big Data, 2023 - Springer
The diagnosis and categorization of cardiac disease using the low-cost tool
electrocardiogram (ECG) becomes an intriguing study topic when contemplating intelligent …
electrocardiogram (ECG) becomes an intriguing study topic when contemplating intelligent …
Gsmd-srst: Group sparse mode decomposition and superlet transform based technique for multi-level classification of cardiac arrhythmia
Cardiac arrhythmia is caused due to the irregularity of the heartbeat and heart rhythm, which
increases the complications leading to the risk of heart strokes. Atrial fibrillation (AF) and …
increases the complications leading to the risk of heart strokes. Atrial fibrillation (AF) and …
A Hybrid Compressive Sensing and Classification Approach for Dynamic Storage Management of Vital Biomedical Signals
The efficient compression and classification of medical signals, particularly
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …