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Compressed Sensing for ECG Data Acquisition and Processing
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Compressed Sensing for ECG Data Acquisition and Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 5559

Special Issue Editors

Prof. Dr. Luca De Vito

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi del Sannio, Benevento, Italy
Interests: instrumentation and measurement; data converters; data acquisition systems; Internet of Things (IoT); biomedical instrumentation
Dr. Francesco Picariello

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: electrical and electronic instrumentation; data acquisition systems (DAQs) based on compressive sampling (CS); biomedical instrumentation; distributed measurement systems, including wireless sensor networks (WSNs); Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals
Dr. Ioan Tudosa

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, Piazza Roma, 21, 82100 Benevento, Italy
Interests: time and frequency measurements; measurement methods for signal/instrument characterization/calibration; data acquisition systems (DAQ); hardware design for instrumentation; applied Compressed Sensing (CS) and compressive sampling techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Compressed sensing (CS) has recently been applied to ECG monitoring systems with the aim of either compressing the acquired data rate, reducing the noise, or even processing the ECG signal to discover anomalies.

This Special Issue seeks innovative contributions on the application of recent CS results to the acquisition and processing of ECG signals, related but not restricted to the following topics:

  • Signal acquisition schemes based on CS;
  • Signal dictionaries and methods for dictionary optimization, learning, and adaptation;
  • Reconstruction algorithms;
  • Characterization and assessment of CS ECG monitoring systems;
  • Hardware implementations of ECG monitoring systems based on CS;
  • Analog-to-information converters for ECG monitoring;
  • Processing of ECG samples acquired by CS;
  • CS-based ECG signal denoising;
  • Anomaly detection from compressed samples;
  • CS-based heartrate and heartrate variation measurements;
  • CS-based Internet of Things and Internet of Medical Things systems;
  • Machine learning for CS;
  • Energy-efficient CS systems;
  • CS-based ECG segmentation and feature extraction.

Prof. Dr. Luca De Vito
Dr. Francesco Picariello
Dr. Ioan Tudosa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

17 pages, 3266 KiB  
Article
ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
by Pasquale Daponte, Luca De Vito, Grazia Iadarola and Francesco Picariello
Sensors 2021, 21(21), 7003; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217003 - 22 Oct 2021
Cited by 16 | Viewed by 2358
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 [...] Read more.
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%. Full article
(This article belongs to the Special Issue Compressed Sensing for ECG Data Acquisition and Processing)
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22 pages, 481 KiB  
Article
A Dictionary Optimization Method for Reconstruction of ECG Signals after Compressed Sensing
by Luca De Vito, Enrico Picariello, Francesco Picariello, Sergio Rapuano and Ioan Tudosa
Sensors 2021, 21(16), 5282; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165282 - 05 Aug 2021
Cited by 9 | Viewed by 2078
Abstract
This paper presents a new approach for the optimization of a dictionary used in ECG signal compression and reconstruction systems, based on Compressed Sensing (CS). Alternatively to fully data driven methods, which learn the dictionary from the training data, the proposed approach uses [...] Read more.
This paper presents a new approach for the optimization of a dictionary used in ECG signal compression and reconstruction systems, based on Compressed Sensing (CS). Alternatively to fully data driven methods, which learn the dictionary from the training data, the proposed approach uses an over complete wavelet dictionary, which is then reduced by means of a training phase. Moreover, the alignment of the frames according to the position of the R-peak is proposed, such that the dictionary optimization can exploit the different scaling features of the ECG waves. Therefore, at first, a training phase is performed in order to optimize the overcomplete dictionary matrix by reducing its number of columns. Then, the optimized matrix is used in combination with a dynamic sensing matrix to compress and reconstruct the ECG waveform. In this paper, the mathematical formulation of the patient-specific optimization is presented and three optimization algorithms have been evaluated. For each of them, an experimental tuning of the convergence parameter is carried out, in order to ensure that the algorithm can work in its most suitable conditions. The performance of each considered algorithm is evaluated by assessing the Percentage of Root-mean-squared Difference (PRD) and compared with the state of the art techniques. The obtained experimental results demonstrate that: (i) the utilization of an optimized dictionary matrix allows a better performance to be reached in the reconstruction quality of the ECG signals when compared with other methods, (ii) the regularization parameters of the optimization algorithms should be properly tuned to achieve the best reconstruction results, and (iii) the Multiple Orthogonal Matching Pursuit (M-OMP) algorithm is the better suited algorithm among those examined. Full article
(This article belongs to the Special Issue Compressed Sensing for ECG Data Acquisition and Processing)
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