Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
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) …
Joint learning method with teacher–student knowledge distillation for on-device breast cancer image classification
The deep learning models such as AlexNet, VGG, and ResNet achieved a good
performance in classifying the breast cancer histopathological images in BreakHis dataset …
performance in classifying the breast cancer histopathological images in BreakHis dataset …
Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis
Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment
planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation …
planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation …
Overcoming limitation of dissociation between MD and MI classifications of breast cancer histopathological images through a novel decomposed feature-based …
Magnification-independent (MI) classification is considered a promising method for detecting
the histopathological images of breast cancer. However, it has too many parameters for real …
the histopathological images of breast cancer. However, it has too many parameters for real …
Performance evaluation of quantum-based machine learning algorithms for cardiac arrhythmia classification
The electrocardiogram (ECG) is the most common technique used to diagnose heart
diseases. The electrical signals produced by the heart are recorded by chest electrodes and …
diseases. The electrical signals produced by the heart are recorded by chest electrodes and …
Teacher–student knowledge distillation based on decomposed deep feature representation for intelligent mobile applications
According to the recent studies on feature-based knowledge distillation (KD), a student
model will not be able to imitate a teacher's behavior properly if there is a high variance …
model will not be able to imitate a teacher's behavior properly if there is a high variance …
Horizons in single-lead ECG analysis from devices to data
Single-lead wearable electrocardiographic (ECG) devices for remote monitoring are
emerging as critical components of the viability of long-term continuous health and wellness …
emerging as critical components of the viability of long-term continuous health and wellness …
Develo** graph convolutional networks and mutual information for arrhythmic diagnosis based on multichannel ECG signals
Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can
be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic …
be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic …