Machine learning-based heart disease diagnosis: A systematic literature review
MM Ahsan, Z Siddique - Artificial Intelligence in Medicine, 2022 - Elsevier
Heart disease is one of the significant challenges in today's world and one of the leading
causes of many deaths worldwide. Recent advancement of machine learning (ML) …
causes of many deaths worldwide. Recent advancement of machine learning (ML) …
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …
Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets
Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related
complications that can increase the risk of strokes and heart failure. Manual …
complications that can increase the risk of strokes and heart failure. Manual …
A new machine learning technique for an accurate diagnosis of coronary artery disease
Background and objective Coronary artery disease (CAD) is one of the commonest diseases
around the world. An early and accurate diagnosis of CAD allows a timely administration of …
around the world. An early and accurate diagnosis of CAD allows a timely administration of …
[HTML][HTML] ECG-based machine-learning algorithms for heartbeat classification
S Aziz, S Ahmed, MS Alouini - Scientific reports, 2021 - nature.com
Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and
consist of several waveforms (P, QRS, and T). The duration and shape of each waveform …
consist of several waveforms (P, QRS, and T). The duration and shape of each waveform …
[HTML][HTML] Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
J Lai, H Tan, J Wang, L Ji, J Guo, B Han, Y Shi… - Nature …, 2023 - nature.com
Cardiovascular disease is a major global public health problem, and intelligent diagnostic
approaches play an increasingly important role in the analysis of electrocardiograms …
approaches play an increasingly important role in the analysis of electrocardiograms …
Arrhythmia classification algorithm based on multi-head self-attention mechanism
Y Wang, G Yang, S Li, Y Li, L He, D Liu - Biomedical Signal Processing and …, 2023 - Elsevier
Cardiovascular disease is a major illness that causes human death, especially in the elderly.
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …
[HTML][HTML] Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
P Pławiak, UR Acharya - Neural Computing and Applications, 2020 - Springer
The heart disease is one of the most serious health problems in today's world. Over 50
million persons have cardiovascular diseases around the world. Our proposed work based …
million persons have cardiovascular diseases around the world. Our proposed work based …
Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers
A method for the automatic classification of electrocardiograms (ECG) based on the
combination of multiple Support Vector Machines (SVMs) is presented in this work. The …
combination of multiple Support Vector Machines (SVMs) is presented in this work. The …
The role of the Internet of Things in healthcare: Future trends and challenges
Abstract Background and Objective With the recent advances in the Internet of Things (IoT),
the field has become more and more developed in healthcare. The Internet of things will …
the field has become more and more developed in healthcare. The Internet of things will …