A Dynamic Approach for Compressed Sensing of Multi–lead ECG Signals
Grazia Iadarola, Pasquale Daponte, Francesco Picariello, Luca De Vito
Abstract
This paper proposes a dynamic method based on Compressed Sensing (CS) to reconstruct multi-lead electrocardiography (ECG) signals in support of Internet-of-Medical-Things. Specifically, the sensing matrix is dynamically evaluated through the signal samples acquired by the first lead. The experimental evaluation demonstrates that, compared to the traditional CS multi-lead method adopting a random sensing matrix, the proposed dynamic method exhibits a lower difference from the original ECG signal.
Topics & Concepts
Compressed sensingComputer scienceLead (geology)SIGNAL (programming language)Matrix (chemical analysis)Signal reconstructionArtificial intelligencePattern recognition (psychology)Signal processingMaterials scienceComputer hardwareGeologyDigital signal processingProgramming languageComposite materialGeomorphologyAnalog and Mixed-Signal Circuit DesignECG Monitoring and AnalysisSparse and Compressive Sensing Techniques