Measurement ( IF 3.364 ) Pub Date : 2020-07-26 , DOI: 10.1016/j.measurement.2020.108259 Francesco Picariello; Grazia Iadarola; Eulalia Balestrieri; Ioan Tudosa; Luca De Vito
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.