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Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare
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Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 22878

Special Issue Editors

Prof. Dr. Omar Escalona

E-Mail Website
Guest Editor
School of Engineering, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey BT37 0QB, UK
Interests: atrial fibrillation surveillance and solutions with improved QALY outcome difference; highly efficient cordless energy supply systems for implanted artificial heart pumps; connected-health enabled cardiovascular healthcare services
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dewar Finlay

E-Mail Website
Guest Editor
School of Engineering, Ulster University, Newtownabbey BT37 0QB, UK
Interests: the application of technology in cardiovascular medicine with a particular focus on computerised ECG analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Soares Schlindwein

E-Mail Website
Guest Editor
Department of Engineering, University of Leicester, University Rd, Leicester LE1 7RH, UK
Interests: atrial fibrillation; biomedical engineering; real-time signal processing applied to medical signals

Special Issue Information

Dear Colleagues,

Cardiovascular healthcare is of global concern and the enabling technologies are undergoing rapid unprecedented changes and sophistication which have increased the versatility and reliability of ambulatory, or at home, continuous non-invasive long-term monitoring of the heart’s rhythm through ECG sensing, and level of cardiac pumping through various plethysmographic sensing techniques. Besides the accelerated progress in medical electronics, in hyper-fast wireless connectivity, in advanced medical informatics and in data analytics methods, cardiac biomedical sensors development is offering an important opportunity to capture dynamic body-surface physiological parameters in real-time, in nonintrusive and continuous mode, by integrating flexible electronics packaging and new semiconductor materials and technology, for facilitating advanced cardiovascular healthcare.

This Special Topic is on the science and technology of body-surface biomedical sensors and its applications for cardiovascular healthcare. The topic falls within the following scopes of Sensors: wearable biosensors; non-invasive physical sensors; wireless connected sensors; signal processing, data fusion, deep learning and artificial intelligence in sensor systems; body surface physiological sensor technology and application; advanced materials for body surface sensing; sensor devices and sensing systems.

Scope of this Special Topic issue:

  • Wearable biocompatible dry ECG electrodes for long-term monitoring.
  • Novel bipolar ECG leads positioning, characterisation and reliable interpretation.
  • Smart body-surface cardiac mapping sensing techniques for increased diagnostic accuracy and standardisation.
  • Wearable armbands for long-term ECG and impedance cardiography (ICG) monitoring methods.
  • Supportive body-surface physiological variables sensing techniques for monitoring and for wireless control of cardiovascular therapeutic implanted devices.
  • Effective wireless interconnected ECG sensor systems with connectivity to cardiovascular healthcare network.
  • ECG sensor systems for atrial fibrillation surveillance and treatment decision support using signal processing, data fusion, deep learning and artificial intelligence techniques
  • Novel energy harvesting solutions embedded in ECG sensors for energising wearable, long-term ambulatory cardiovascular monitoring systems.

Prof. Dr. Omar Escalona
Prof. Dr. Dewar Finlay
Dr. Fernando Schlindwein
Guest Editors

Manuscript Submission Information

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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.

Keywords

  • Novel bipolar ECG leads interpretation
  • wireless interconnected ECG sensor systems
  • Wearable biocompatible dry ECG electrodes
  • body-surface physiological variables sensing techniques
  • noninvasive sensors
  • wireless control of cardiovascular therapeutic implanted devices

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Published Papers (5 papers)

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21 pages, 4762 KiB  
Article
Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration
by Krittika Goyal, David A. Borkholder and Steven W. Day
Sensors 2022, 22(21), 8510; https://doi.org/10.3390/s22218510 - 4 Nov 2022
Cited by 17 | Viewed by 8171
Abstract
Dry electrodes offer an accessible continuous acquisition of biopotential signals as part of current in-home monitoring systems but often face challenges of high-contact impedance that results in poor signal quality. The performance of dry electrodes could be affected by electrode material and skin [...] Read more.
Dry electrodes offer an accessible continuous acquisition of biopotential signals as part of current in-home monitoring systems but often face challenges of high-contact impedance that results in poor signal quality. The performance of dry electrodes could be affected by electrode material and skin hydration. Herein, we investigate these dependencies using a circuit skin-electrode interface model, varying material and hydration in controlled benchtop experiments on a biomimetic skin phantom simulating dry and hydrated skin. Results of the model demonstrate the contribution of the individual components in the circuit to total impedance and assist in understanding the role of electrode material in the mechanistic principle of dry electrodes. Validation was performed by conducting in vivo skin-electrode contact impedance measurements across ten normative human subjects. Further, the impact of the electrode on biopotential signal quality was evaluated by demonstrating an ability to capture clinically relevant electrocardiogram signals by using dry electrodes integrated into a toilet seat cardiovascular monitoring system. Titanium electrodes resulted in better signal quality than stainless steel electrodes. Results suggest that relative permittivity of native oxide of electrode material come into contact with the skin contributes to the interface impedance, and can lead to enhancement in the capacitive coupling of biopotential signals, especially in dry skin individuals. Full article
(This article belongs to the Special Issue Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare)
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21 pages, 9698 KiB  
Article
Transcutaneous Pulsed RF Energy Transfer Mitigates Tissue Heating in High Power Demand Implanted Device Applications: In Vivo and In Silico Models Results
by Mohammad L. Karim, Antonio M. Bosnjak, James McLaughlin, Paul Crawford, David McEneaney and Omar J. Escalona
Sensors 2022, 22(20), 7775; https://doi.org/10.3390/s22207775 - 13 Oct 2022
Cited by 4 | Viewed by 2060
Abstract
This article presents the development of a power loss emulation (PLE) system device to study and find ways of mitigating skin tissue heating effects in transcutaneous energy transmission systems (TETS) for existing and next generation left ventricular assist devices (LVADs). Skin thermal profile [...] Read more.
This article presents the development of a power loss emulation (PLE) system device to study and find ways of mitigating skin tissue heating effects in transcutaneous energy transmission systems (TETS) for existing and next generation left ventricular assist devices (LVADs). Skin thermal profile measurements were made using the PLE system prototype and also separately with a TETS in a porcine model. Subsequent data analysis and separate computer modelling studies permit understanding of the contribution of tissue blood perfusion towards cooling of the subcutaneous tissue around the electromagnetic coupling area. A 2-channel PLE system prototype and a 2-channel TETS prototype were implemented for this study. The heating effects resulting from power transmission inefficiency were investigated under varying conditions of power delivery levels for an implanted device. In the part of the study using the PLE setup, the implanted heating element was placed subcutaneously 6–8 mm below the body surface of in vivo porcine model skin. Two operating modes of transmission coupling power losses were emulated: (a) conventional continuous transmission, and (b) using our proposed pulsed transmission waveform protocols. Experimental skin tissue thermal profiles were studied for various levels of LVAD power. The heating coefficient was estimated from the porcine model measurements (an in vivo living model and a euthanised cadaver model without blood circulation at the end of the experiment). An in silico model to support data interpretation provided reliable experimental and numerical methods for effective wireless transdermal LVAD energization advanced solutions. In the separate second part of the study conducted with a separate set of pigs, a two-channel inductively coupled RF driving system implemented wireless power transfer (WPT) to a resistive LVAD model (50 Ω) to explore continuous versus pulsed RF transmission modes. The RF-transmission pulse duration ranged from 30 ms to 480 ms, and the idle time (no-transmission) from 5 s to 120 s. The results revealed that blood perfusion plays an important cooling role in reducing thermal tissue damage from TETS applications. In addition, the results analysis of the in vivo, cadaver (R1Sp2) model, and in silico studies confirmed that the tissue heating effect was significantly lower in the living model versus the cadaver model due to the presence of blood perfusion cooling effects. Full article
(This article belongs to the Special Issue Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare)
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22 pages, 3134 KiB  
Article
Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line
by Omar Escalona, Sephorah Mukhtar, David McEneaney and Dewar Finlay
Sensors 2022, 22(19), 7240; https://doi.org/10.3390/s22197240 - 24 Sep 2022
Cited by 3 | Viewed by 2198
Abstract
Sudden cardiac death (SCD) risk can be reduced by early detection of short-lived and transient cardiac arrhythmias using long-term electrocardiographic (ECG) monitoring. Early detection of ventricular arrhythmias can reduce the risk of SCD by allowing appropriate interventions. Long-term continuous ECG monitoring, using a [...] Read more.
Sudden cardiac death (SCD) risk can be reduced by early detection of short-lived and transient cardiac arrhythmias using long-term electrocardiographic (ECG) monitoring. Early detection of ventricular arrhythmias can reduce the risk of SCD by allowing appropriate interventions. Long-term continuous ECG monitoring, using a non-invasive armband-based wearable device is an appealing solution for detecting early heart rhythm abnormalities. However, there is a paucity of understanding on the number and best bipolar ECG electrode pairs axial orientation around the left mid-upper arm circumference (MUAC) for such devices. This study addresses the question on the best axial orientation of ECG bipolar electrode pairs around the left MUAC in non-invasive armband-based wearable devices, for the early detection of heart rhythm abnormalities. A total of 18 subjects with almost same BMI values in the WASTCArD arm-ECG database were selected to assess arm-ECG bipolar leads quality using proposed metrics of relative (normalized) signal strength measurement, arm-ECG detection performance of the main ECG waveform event component (QRS) and heart-rate variability (HRV) in six derived bipolar arm ECG-lead sensor pairs around the armband circumference, having regularly spaced axis angles (at 30° steps) orientation. The analysis revealed that the angular range from −30° to +30°of arm-lead sensors pair axis orientation around the arm, including the 0° axis (which is co-planar to chest plane), provided the best orientation on the arm for reasonably good QRS detection; presenting the highest sensitivity (Se) median value of 93.3%, precision PPV median value at 99.6%; HRV RMS correlation (p) of 0.97 and coefficient of determination (R2) of 0.95 with HRV gold standard values measured in the standard Lead-I ECG. Full article
(This article belongs to the Special Issue Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare)
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18 pages, 6830 KiB  
Article
Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks
by Ke-Wei Chen, Laura Bear and Che-Wei Lin
Sensors 2022, 22(6), 2331; https://doi.org/10.3390/s22062331 - 17 Mar 2022
Cited by 11 | Viewed by 3418
Abstract
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms [...] Read more.
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs’ ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods. Full article
(This article belongs to the Special Issue Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare)
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27 pages, 2090 KiB  
Systematic Review
A Systematic Review of the Transthoracic Impedance during Cardiac Defibrillation
by Yasmine Heyer, Daniela Baumgartner and Christian Baumgartner
Sensors 2022, 22(7), 2808; https://doi.org/10.3390/s22072808 - 6 Apr 2022
Cited by 13 | Viewed by 6046
Abstract
For cardiac defibrillator testing and design purposes, the range and limits of the human TTI is of high interest. Potential influencing factors regarding the electronic configurations, the electrode/tissue interface and patient characteristics were identified and analyzed. A literature survey based on 71 selected [...] Read more.
For cardiac defibrillator testing and design purposes, the range and limits of the human TTI is of high interest. Potential influencing factors regarding the electronic configurations, the electrode/tissue interface and patient characteristics were identified and analyzed. A literature survey based on 71 selected articles was used to review and assess human TTI and the influencing factors found. The human TTI extended from 12 to 212 Ω in the literature selected. Excluding outliers and pediatric measurements, the mean TTI recordings ranged from 51 to 112 Ω with an average TTI of 76.7 Ω under normal distribution. The wide range of human impedance can be attributed to 12 different influencing factors, including shock waveforms and protocols, coupling devices, electrode size and pressure, electrode position, patient age, gender, body dimensions, respiration and lung volume, blood hemoglobin saturation and different pathologies. The coupling device, electrode size and electrode pressure have the greatest influence on TTI. Full article
(This article belongs to the Special Issue Body Surface Physiological Sensing for Advanced Cardiovascular Healthcare)
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