Wearable ECG systems with soft biomaterials and AI improve arrhythmia and myocardial infarction detection, though algorithmic bias, data privacy, and regulatory fragmentation remain key challenges.
Cardiovascular diseases (CVDs) remain the leading global cause of mortality, with approximately 18 million deaths annually, underscoring the need for early detection and continuous monitoring. Wearable electrocardiogram (ECG) systems have evolved from bulky instruments, such as Einthoven's galvanometer, to compact, AI-enhanced devices like KardiaMobile and smartwatches, enabling real-time, non-invasive cardiac assessment. This review examines the technological advancements in wearable ECGs, highlighting innovations in soft, stretchable biomaterials that improve comfort and signal fidelity. Advances in signal preprocessing and AI, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) models, have improved arrhythmia classification and myocardial infarction detection. Despite progress, challenges persist in addressing algorithmic bias, ensuring interpretability, and meeting regulatory compliance. The review also compares ECG with complementary modalities, such as photoplethysmography (PPG). It outlines a strategic roadmap for integrating wearables into preventive cardiology, focusing on clinical validation, data security, and harmonized global standards. The Graphical Abstract illustrates the advancements in wearable electrocardiogram (ECG) technology for next-generation cardiac monitoring. This schematic illustrates the transition from traditional ECG devices to contemporary wearable systems, highlighting eight key innovation domains: (i) Physiological foundations of ECG signal acquisition, (ii) Developmental trajectory of wearable ECG sensor technologies, (iii) Advanced biomaterials enabling enhanced wearability and biocompatibility, (iv) Artificial intelligence-driven diagnostic capabilities for cardiovascular diseases, (v) Data privacy, cybersecurity, and ethical frameworks, (vi) Signal preprocessing and feature extraction methodologies, (vii) Challenges and future directions in AI-integrated wearable ECG systems, (viii) Clinical translation and integration into digital health ecosystems. • Evolution of wearable ECGs from early bulky devices to compact, AI-integrated systems enabling real-time cardiac monitoring. • Advances in soft biomaterials (e.g., hydrogels, elastomers) that enhance comfort, skin conformity, and signal stability. • AI-driven approaches (e.g., CNNs, LSTMs) are improving the detection of arrhythmias and myocardial infarction. • Discussion of clinical translation barriers, including algorithmic bias, data privacy, and regulatory fragmentation. • Strategic roadmap for integrating ECG wearables into preventive cardiology and personalized healthcare.
Khan et al. (2025) conducted a review in Cardiovascular diseases. Wearable ECG systems was evaluated. Wearable ECG systems with soft biomaterials and AI improve arrhythmia and myocardial infarction detection, though algorithmic bias, data privacy, and regulatory fragmentation remain key challenges.