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Cardiac Monitoring Using ECG and PPG Sensors
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Cardiac Monitoring Using ECG and PPG Sensors

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

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 6836

Special Issue Editor

Dr. Guanghao Sun

E-Mail Website
Guest Editor
Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu 182-8585, Tokyo, Japan
Interests: noncontact bio-measurement; biomedical signal processing; medical radar; remote photoplethysmography; vital signs; mHealth; depth sensor; RGB camera

Special Issue Information

Dear Colleagues,

Electrocardiogram (ECG) and photoplethysmography (PPG) are non-invasive techniques that provide cardiac information for the detection of various cardiac abnormalities. Because cardiac diagnosis requires continuous manual observation, it is sometimes difficult for experts to correctly identify paroxysmal arrhythmia, which is why automated smart devices are so helpful in this. Indeed, as technology has advanced, so have wearables for heart monitoring.

The present Special Issue aims to highlight some of the latest developments in the field of ECG and PPG-based state-of-the-art methods, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias; various wearable sensors used in the literature and public databases available for the evaluation of results; and the limitations of the current techniques and pragmatic solutions to improve the ongoing effort.

Please check that meaning has been retained.

Dr. Guanghao Sun
Guest Editor

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.

Keywords

  • heart monitoring systems
  • heart diseases detection
  • cardiovascular diseases
  • healthcare
  • electrocardiogram (ECG)
  • Photoplethysmography (PPG)

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Published Papers (2 papers)

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15 pages, 2622 KiB  
Article
Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals
by Solmaz Rastegar, Hamid Gholam Hosseini and Andrew Lowe
Sensors 2023, 23(3), 1259; https://doi.org/10.3390/s23031259 - 22 Jan 2023
Cited by 13 | Viewed by 4847
Abstract
Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood [...] Read more.
Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 ± 2.45 mmHg (MAE ± STD) for SBP and 3.08 ± 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard. Full article
(This article belongs to the Special Issue Cardiac Monitoring Using ECG and PPG Sensors)
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Review

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18 pages, 4768 KiB  
Review
Ventricular Arrhythmias in Left Ventricular Assist Device Patients—Current Diagnostic and Therapeutic Considerations
by Laura Załucka, Ewa Świerżyńska, Michał Orczykowski, Krzysztof Dutkowski, Jarosław Szymański, Jarosław Kuriata, Rafał Dąbrowski, Piotr Kołsut, Łukasz Szumowski and Maciej Sterliński
Sensors 2024, 24(4), 1124; https://doi.org/10.3390/s24041124 - 8 Feb 2024
Viewed by 1389
Abstract
Left ventricular assist devices (LVAD) are used in the treatment of advanced left ventricular heart failure. LVAD can serve as a bridge to orthotopic heart transplantation or as a destination therapy in cases where orthotopic heart transplantation is contraindicated. Ventricular arrhythmias are frequently [...] Read more.
Left ventricular assist devices (LVAD) are used in the treatment of advanced left ventricular heart failure. LVAD can serve as a bridge to orthotopic heart transplantation or as a destination therapy in cases where orthotopic heart transplantation is contraindicated. Ventricular arrhythmias are frequently observed in patients with LVAD. This problem is further compounded as a result of diagnostic difficulties arising from presently available electrocardiographic methods. Due to artifacts from LVAD-generated electromagnetic fields, it can be challenging to assess the origin of arrhythmias in standard ECG tracings. In this article, we will review and discuss common mechanisms, diagnostics methods, and therapeutic strategies for ventricular arrhythmia treatment, as well as numerous problems we face in LVAD implant patients. Full article
(This article belongs to the Special Issue Cardiac Monitoring Using ECG and PPG Sensors)
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