ArticleSensors (Basel, Switzerland)2021
ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- ECG Signal Compression and Reconstruction Based on CNN-LSTM-Attention Model.Sensors (Basel, Switzerland) · 2026Article
- FHGNet: A Feature-Centric Hierarchical Network with Graph Attention Layer for Supraventricular Tachycardia Classification.Interdisciplinary sciences, computational life sciences · 2026Article
- Internet of Nano-Things (IoNT): A Comprehensive Review from Architecture to Security and Privacy Challenges.Sensors (Basel, Switzerland) · 2023Review
- Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation.Sensors (Basel, Switzerland) · 2023Article
- A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs.Sensors (Basel, Switzerland) · 2022Article
- A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification.Biosensors · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio (CR) up to 10, without compromising signal quality. In particular, for CR= 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.