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

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 44290

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Special Issue Editors

Prof. Laura Burattini

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Guest Editor

Università Politecnica delle Marche, Ancona, Italy
Interests: cardiovascualar signal processing; clinical ECG interpretation; fetal ECG; newborn ECG; sport applications
Special Issues, Collections and Topics in MDPI journals

Prof. Jean Philippe Couderc

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Guest Editor

University of Rochester, NY, USA
Interests: ECG signal processing; wearable sensors; computerized technologies for cardiac safety

Dr. Danilo Pani

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Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, CA 09124, Italy
Interests: biomedical signal processing; fetal electrocardiography; wearable electronics; cardiac electrophysiology; neural engineering; real-time processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Paola Pierleoni

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Department of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: network protocols; wireless sensor networks; Internet of Things; signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Micaela Morettini

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Guest Editor

Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: biological systems modeling; sport applications; biomedical signal processing; health monitoring

Special Issue Information

Dear Colleagues,

The electrocardiogram (ECG) represents a simple, cheap, and non-invasive diagnostic examination for assessing the functionality of the electrical system of the heart. It allows discovering pathological conditions even before structural changes in the heart can be diagnosed by other methods. The reduced data size of the recordings, due to the typical sampling frequency and number of channels, makes its adoption possible in any scenario, including telehealth and telecare, where unobtrusiveness of the measuring system is crucial. These characteristics, along with the possibilities offered by the miniaturization of the recording systems, fostered the development of diagnostic and monitoring devices for long-term ECG recording, which opened the possibility of wearing ECG devices for personal or clinical use. Then, the technological challenge moved from the realization of lightweight ECG devices to the realization of unobtrusive electrodes. Each wave of the ECG tracing reflects a specific phase of the cardiac cycle; thus, analysis of the ECG morphological and temporal features provides important clinical information on the health status of the heart at any age and condition. ECG interpretation may be jeopardized by artefact and noise affecting the ECG tracing. Consequently, ECG sensors have to be designed in order to minimize noise during acquisition, specific hardware and software filters have to be implemented for ECG cleaning, and signal processing procedures have to be implemented for clinical information extraction. Moreover, some specialistic clinical uses (such as multimodal recordings) find severe limitation in the current electrode technologies, asking for further research on this fundamental aspect, designing electrodes with specific physical characteristics, or including some stages of the signal acquisition chain close to the acquisition point on the body.

This Special Issue aims to collect original research papers or review papers on advances in the technologies for the design of ECG electrodes. Moreover, readout electronic circuitry for ECG electrodes, and novel techniques for processing of the ECG signals gathered with such tools are also welcome. Topics include but are not limited to:

ECG electrodes materials and technologies;
ECG electrodes characterization;
Textile electrodes;
ECG electrodes on unconventional substrates;
Loop recorders and invasive devices;
Cardiac catheters for intracardiac recording and stimulation;
Embedded systems for ECG sensing;
Hardware front-end for ECG electrodes;
Active ECG electrodes;
Hardware and software filtering for ECG denoising and enhancement;
Fetal electrocardiography electrodes and systems;
Newborn and pediatric electrocardiography electrodes and systems;
ECG applications during physical activity;
Long-term ECG recording systems;
Cardiac home telemonitoring systems;
Sensor-dependent signal processing for computer-aided diagnosis.

Prof. Laura Burattini
Prof. Jean Philippe Couderc
Prof. Danilo Pani
Prof. Paola Pierleoni
Dr. Micaela Morettini
Guest Editors

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Keywords

ECG electrodes
electrodes technology
electrodes materials
ECG acquisition
ECG front-end
active electrodes
ECG processing

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Published Papers (8 papers)

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Research

19 pages, 10620 KiB

Open AccessArticle

Characterisation of Textile Embedded Electrodes for Use in a Neonatal Smart Mattress Electrocardiography System

by Henry Dore, Rodrigo Aviles-Espinosa, Zhenhua Luo, Oana Anton, Heike Rabe and Elizabeth Rendon-Morales

Sensors 2021, 21(3), 999; https://doi.org/10.3390/s21030999 - 2 Feb 2021

Cited by 7 | Viewed by 4672

Abstract

Heart rate monitoring is the predominant quantitative health indicator of a newborn in the delivery room. A rapid and accurate heart rate measurement is vital during the first minutes after birth. Clinical recommendations suggest that electrocardiogram (ECG) monitoring should be widely adopted in the neonatal intensive care unit to reduce infant mortality and improve long term health outcomes in births that require intervention. Novel non-contact electrocardiogram sensors can reduce the time from birth to heart rate reading as well as providing unobtrusive and continuous monitoring during intervention. In this work we report the design and development of a solution to provide high resolution, real time electrocardiogram data to the clinicians within the delivery room using non-contact electric potential sensors embedded in a neonatal intensive care unit mattress. A real-time high-resolution electrocardiogram acquisition solution based on a low power embedded system was developed and textile embedded electrodes were fabricated and characterised. Proof of concept tests were carried out on simulated and human cardiac signals, producing electrocardiograms suitable for the calculation of heart rate having an accuracy within ±1 beat per minute using a test ECG signal, ECG recordings from a human volunteer with a correlation coefficient of ~ 87% proved accurate beat to beat morphology reproduction of the waveform without morphological alterations and a time from application to heart rate display below 6 s. This provides evidence that flexible non-contact textile-based electrodes can be embedded in wearable devices for assisting births through heart rate monitoring and serves as a proof of concept for a complete neonate electrocardiogram monitoring system. Full article

(This article belongs to the Special Issue ECG Sensors)

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18 pages, 5673 KiB

Open AccessArticle

A Dynamic Systems Approach for Detecting and Localizing of Infarct-Related Artery in Acute Myocardial Infarction Using Compressed Paper-Based Electrocardiogram (ECG)

by Trung Q. Le, Vibhuthi Chandra, Kahkashan Afrin, Sanjay Srivatsa and Satish Bukkapatnam

Sensors 2020, 20(14), 3975; https://doi.org/10.3390/s20143975 - 17 Jul 2020

Cited by 8 | Viewed by 4920

Abstract

Timely evaluation and reperfusion have improved the myocardial salvage and the subsequent recovery rate of the patients hospitalized with acute myocardial infarction (MI). Long waiting time and time-consuming procedures of in-hospital diagnostic testing severely affect the timeliness. We present a Poincare pattern ensemble-based method with the consideration of multi-correlated non-stationary stochastic system dynamics to localize the infarct-related artery (IRA) in acute MI by fully harnessing information from paper-based Electrocardiogram (ECG). The vectorcardiogram (VCG) diagnostic features extracted from only 2.5-s long paper ECG recordings were used to hierarchically localize the IRA—not mere localization of the infarcted cardiac tissues—in acute MI. Paper ECG records and angiograms of 106 acute MI patients collected at the Heart Artery and Vein Center at Fresno California and the 12-lead ECG signals from the Physionet PTB online database were employed to validate the proposed approach. We reported the overall accuracies of 97.41% for healthy control (HC) vs. MI, 89.41 ± 9.89 for left and right culprit arteries vs. others, 88.2 ± 11.6 for left main arteries vs. right-coronary-ascending (RCA) and 93.67 ± 4.89 for left-anterior-descending (LAD) vs. left-circumflex (LCX). The IRA localization from paper ECG can be used to timely triage the patients with acute coronary syndromes to the percutaneous coronary intervention facilities. Full article

(This article belongs to the Special Issue ECG Sensors)

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14 pages, 3263 KiB

Open AccessArticle

Ambulatory Electrocardiographic Monitoring and Ectopic Beat Detection in Conscious Mice

by Felke Steijns, Máté I. Tóth, Anthony Demolder, Lars E. Larsen, Jana Desloovere, Marjolijn Renard, Robrecht Raedt, Patrick Segers, Julie De Backer and Patrick Sips

Sensors 2020, 20(14), 3867; https://doi.org/10.3390/s20143867 - 10 Jul 2020

Cited by 7 | Viewed by 5707

Abstract

Ambulatory electrocardiography (AECG) is a primary diagnostic tool in patients with potential arrhythmic disorders. To study the pathophysiological mechanisms of arrhythmic disorders, mouse models are widely implemented. The use of a technique similar to AECG for mice is thus of great relevance. We have optimized a protocol which allows qualitative, long-term ECG data recording in conscious, freely moving mice. Automated algorithms were developed to efficiently process the large amount of data and calculate the average heart rate (HR), the mean peak-to-peak interval and heart rate variability (HRV) based on peak detection. Ectopic beats are automatically detected based on aberrant peak intervals. As we have incorporated a multiple lead configuration in our ECG set-up, the nature and origin of the suggested ectopic beats can be analyzed in detail. The protocol and analysis tools presented here are promising tools for studies which require detailed, long-term ECG characterization in mouse models with potential arrhythmic disorders. Full article

(This article belongs to the Special Issue ECG Sensors)

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16 pages, 1377 KiB

Open AccessArticle

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

by Daniele Marinucci, Agnese Sbrollini, Ilaria Marcantoni, Micaela Morettini, Cees A. Swenne and Laura Burattini

Sensors 2020, 20(12), 3570; https://doi.org/10.3390/s20123570 - 24 Jun 2020

Cited by 47 | Viewed by 6245

Abstract

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices. Full article

(This article belongs to the Special Issue ECG Sensors)

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18 pages, 3664 KiB

Open AccessArticle

A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements

by Hajime Nakamura, Yuichiro Sakajiri, Hiroshi Ishigami and Akinori Ueno

Sensors 2020, 20(9), 2476; https://doi.org/10.3390/s20092476 - 27 Apr 2020

Cited by 12 | Viewed by 5050

Abstract

This paper proposes a novel analogue front end (AFE) that has three features: voltage-dependent input impedance, bandpass amplification, and stray capacitance reduction. With a view to applying the AFE to capacitive biopotential measurements (CBMs), the three features were investigated separately in a schematic and mathematical manner. Capacitive electrocardiogram (cECG) or capacitive electromyogram (cEMG) measurements using the AFE were performed in low-humidity conditions (below 35% relative humidity) for a total of seven human subjects. Performance evaluation of the AFE revealed the following: (1) the proposed AFE in cECG measurement with 1.70-mm thick clothing reduced the baseline recovery time and root mean square voltage of respiratory interference in subjects with healthy-weight body mass index (BMI), and increased R-wave amplitude for overweight-BMI subjects; and (2) the proposed AFE in cEMG measurement of biceps brachii muscle yielded stable electromyographic waveforms without the marked DC component for all subjects and a significant (p < 0.01) increase in the signal-to-noise ratio. These results indicate that the proposed AFE can provide a feasible balance between sensitivity and stability in CBMs, and it could be a versatile replacement for the conventional voltage follower used in CBMs. Full article

(This article belongs to the Special Issue ECG Sensors)

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16 pages, 9200 KiB

Open AccessArticle

Understanding the Washing Damage to Textile ECG Dry Skin Electrodes, Embroidered and Fabric-Based; set up of Equivalent Laboratory Tests

by Shahood uz Zaman, Xuyuan Tao, Cédric Cochrane and Vladan Koncar

Sensors 2020, 20(5), 1272; https://doi.org/10.3390/s20051272 - 26 Feb 2020

Cited by 41 | Viewed by 5578

Abstract

Reliability and washability are major hurdles facing the e-textile industry nowadays. The main fear behind the product’s rejection is the inability to ensure its projected life span. The durability of e-textiles is based on an approximate lifetime of both the electronics and textiles integrated into the product. A detailed analysis of the wash process and the possibility of predicting product behavior are key factors for new standards implementation. This manuscript is focused on the washability issues of different types of woven, knitted, and embroidered, textile-based ECG electrodes. These electrodes are used without the addition of any ionic gel to the skin to reduce impedance. They were subjected to up to 50 wash cycles with two different types of wash processes, and changes in surface resistance, as well as the quality of ECG waves, were observed To investigate the wash damages in detail, the proposed mechanical (Martindale and Pilling box) and chemical test methods were investigated. The electrodes which increased resistance after washing showed the same trend in the proposed test methods. Copper-based electrodes suffered the most severe damage and increased resistance, as was also visible in an SEM analysis. These proposed test methods can be used to predict robustness behavior without washing. Full article

(This article belongs to the Special Issue ECG Sensors)

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24 pages, 5296 KiB

Open AccessArticle

Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals

by Lidan Fu, Binchun Lu, Bo Nie, Zhiyun Peng, Hongying Liu and Xitian Pi

Sensors 2020, 20(4), 1020; https://doi.org/10.3390/s20041020 - 14 Feb 2020

Cited by 72 | Viewed by 5807

Abstract

The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance. Full article

(This article belongs to the Special Issue ECG Sensors)

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Graphical abstract

18 pages, 1783 KiB

Open AccessArticle

A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

by Olivia Vargas-Lopez, Juan P. Amezquita-Sanchez, J. Jesus De-Santiago-Perez, Jesus R. Rivera-Guillen, Martin Valtierra-Rodriguez, Manuel Toledano-Ayala and Carlos A. Perez-Ramirez

Sensors 2020, 20(1), 9; https://doi.org/10.3390/s20010009 - 18 Dec 2019

Cited by 24 | Viewed by 3790

Abstract

Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works. Full article

(This article belongs to the Special Issue ECG Sensors)

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