Key result
Compressed sensing for ECG and PPG signals reduces data volume while preserving critical clinical features.
Why the study?
High volumes of biomedical signals in healthcare monitoring require optimized compression techniques such as compressed sensing to achieve efficient data management without losing critical information.
This review highlights recent advancements in compressed sensing techniques for efficient compression and management of ECG and PPG signals in healthcare monitoring systems.
May guide efficient ECG/PPG monitoring development; leaves open prospective clinical validation of AI frameworks.
The increasing integration of information and communication technologies in healthcare has enabled advanced patient monitoring and personalized treatment. Efficient data management is crucial in such systems, as the high volume of biomedical signals requires optimized compression techniques. Compressed sensing has emerged as an approach within lossy compression methods, leveraging signal sparsity to achieve high compression ratios (CR) without significant loss of critical information. This paper provides a concise review of the most recent developments in CS for electrocardiogram (ECG) and photoplethysmogram (PPG) signals, summarizing key advances in standard CS methods, dictionary-based approaches, and emerging frameworks incorporating artificial intelligence. The review aims to highlight current trends and outline directions for future research in biomedical signal compression.
No takes yet. Share an insight, caveat, or question.
Kovacova et al. (2026) conducted a review in ECG and PPG signal compression. Compressed sensing was evaluated. Compressed sensing methods for ECG and PPG signals substantially reduce data volume while retaining critical clinical features, utilizing various dictionaries, sensing matrices, and reconstruction algorithms.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: