A novel compressive sampling method for ECG wearable measurement systems. (1st January 2021)

• Record Type: Journal Article
• Authors: Picariello, Francesco; Iadarola, Grazia; Balestrieri, Eulalia; Tudosa, Ioan; De Vito, Luca
• Is Part Of: Measurement. Volume 167(2021)
• Publication Date: 2021-01-01
• Subjects: Internet-of-Medical-Things -- Wearable measurement systems -- ECG signal -- Compressive sampling -- Sampling method; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108259
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Compressive Sampling of electrocardiographic signals. Signal-adapted sensing matrix. Low computational complexity on the acquisition node. Reduced data rate to be transmitted. Accurate reconstruction of the signal waveform. Abstract: The paper presents a novel method for the compressed acquisition of electrocardiographic (ECG) signals. The proposed method is intended to be applied to Internet-of-Medical-Things (IoMT) acquisition nodes (i.e. wearable measurement systems) as they can benefit from a reduction of the signal data rate to be transmitted, and the consequent reduction of energy consumption. Being based on Compressive Sampling (CS), the proposed method presents a very low computational complexity on the acquisition node. Moreover, since the sensing matrix is adapted to the acquired signal, it allows achieving a better reconstruction performance compared with the other CS-based methods available in literature.