[PDF][PDF] A systematic literature review of deep and machine learning algorithms in cardiovascular diseases diagnosis
During the whole cardiac cycle, heart sounds are created, and blood enters the heart
chambers as the cardiac regulators open and close. Blood flow produces aural noises; the …
chambers as the cardiac regulators open and close. Blood flow produces aural noises; the …
A review of deep learning-based contactless heart rate measurement methods
The interest in contactless or remote heart rate measurement has been steadily growing in
healthcare and sports applications. Contactless methods involve the utilization of a video …
healthcare and sports applications. Contactless methods involve the utilization of a video …
Integrated multimodal artificial intelligence framework for healthcare applications
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next
decades. Specifically, AI systems leveraging multiple data sources and input modalities are …
decades. Specifically, AI systems leveraging multiple data sources and input modalities are …
hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost
P Srinivas, R Katarya - Biomedical Signal Processing and Control, 2022 - Elsevier
Cardiovascular disease is a dangerous disorder that causes the most significant number of
deaths across the world. In the past years, researchers proposed several automated …
deaths across the world. In the past years, researchers proposed several automated …
A comprehensive review of deep learning-based models for heart disease prediction
C Zhou, P Dai, A Hou, Z Zhang, L Liu, A Li… - Artificial Intelligence …, 2024 - Springer
Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden
on society, families, and patients. Real-time prediction of HD can reduce mortality rates and …
on society, families, and patients. Real-time prediction of HD can reduce mortality rates and …
MSGformer: A multi-scale grid transformer network for 12-lead ECG arrhythmia detection
C Ji, L Wang, J Qin, L Liu, Y Han, Z Wang - Biomedical Signal Processing …, 2024 - Elsevier
The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to identify
arrhythmias that are characterized by anomalous waveform morphology and erratic …
arrhythmias that are characterized by anomalous waveform morphology and erratic …
[HTML][HTML] Multi-modality cardiac image computing: A survey
Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …
cardiovascular diseases. It allows a combination of complementary anatomical …
Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions
Myocardial infarction, commonly known as heart attack, is one of the most common heart
diseases prevailing in the human world. Heart or cardiac disease is one of the leading …
diseases prevailing in the human world. Heart or cardiac disease is one of the leading …
[HTML][HTML] Review of phonocardiogram signal analysis: insights from the PhysioNet/CinC challenge 2016 database
The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring,
accurate diagnosis, and efficient management of cardiovascular diseases. It has the …
accurate diagnosis, and efficient management of cardiovascular diseases. It has the …
SpectroCardioNet: An attention-based deep learning network using triple-spectrograms of PCG signal for heart valve disease detection
The phonocardiogram (PCG) signal is used for the early detection of cardiovascular
diseases (CVDs) as it captures the heart sound characteristics. In this article, a spectral …
diseases (CVDs) as it captures the heart sound characteristics. In this article, a spectral …