Deep learning and the electrocardiogram: review of the current state-of-the-art

S Somani, AJ Russak, F Richter, S Zhao, A Vaid… - EP …, 2021 - academic.oup.com
In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review

S Hong, Y Zhou, J Shang, C Xiao, J Sun - Computers in biology and …, 2020 - Elsevier
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …

Industry 4.0 and digitalisation in healthcare

VV Popov, EV Kudryavtseva, N Kumar Katiyar… - Materials, 2022 - mdpi.com
Industry 4.0 in healthcare involves use of a wide range of modern technologies including
digitisation, artificial intelligence, user response data (ergonomics), human psychology, the …

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study

H Zhu, C Cheng, H Yin, X Li, P Zuo, J Ding… - The Lancet Digital …, 2020 - thelancet.com
Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple
heart abnormalities that covers a wide range of arrhythmias, with better-than-human …

The emergence of AI-based wearable sensors for digital health technology: a review

S Shajari, K Kuruvinashetti, A Komeili, U Sundararaj - Sensors, 2023 - mdpi.com
Disease diagnosis and monitoring using conventional healthcare services is typically
expensive and has limited accuracy. Wearable health technology based on flexible …

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals

Z Wang, S Stavrakis, B Yao - Computers in Biology and Medicine, 2023 - Elsevier
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …

A foundational vision transformer improves diagnostic performance for electrocardiograms

A Vaid, J Jiang, A Sawant, S Lerakis, E Argulian… - NPJ Digital …, 2023 - nature.com
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …

Artificial intelligence (AI) and cardiovascular diseases: an unexpected alliance

S Romiti, M Vinciguerra, W Saade… - Cardiology …, 2020 - Wiley Online Library
Cardiovascular disease (CVD), despite the significant advances in the diagnosis and
treatments, still represents the leading cause of morbidity and mortality worldwide. In order …

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

N Musa, AY Gital, N Aljojo, H Chiroma… - Journal of ambient …, 2023 - Springer
The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …

Label correlation embedding guided network for multi-label ECG arrhythmia diagnosis

S Ran, X Li, B Zhao, Y Jiang, X Yang… - Knowledge-Based Systems, 2023 - Elsevier
In clinical practice, one patient may suffer from more than one arrhythmia simultaneously,
that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there …