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Diagnostics
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Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases
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Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 5857

Special Issue Editors

Dr. Manjeet Kumar

E-Mail Website
Guest Editor
Department of Electronics and Communication Engineering, Delhi Technological University (DTU), Delhi 110042, India
Interests: signal processing; biomedical signal processing; artificial intelligence; machine learning; deep learning; fractional system design; fractional delay system; image processing; biomedical imaging; pacemakers; cardiac function; electrocardiogram; stochastic optimization; wearable sensors; mobile health; health monitoring; wave digital filter; LWDFs, 2-D filter design; signal analysis using wavelet transform; healthcare assistive techniques; low-power biomedical circuit design; machine learning in biomedical signal; deep learning in biomedical signal
Dr. Ashish Kumar

E-Mail Website
Guest Editor
School of Electronics Engineering, Vellore Institute of Technology, Vandallur Kelambakkam Road, Chennai 600127, India
Interests: biomedical systems design; signal analysis; biomedical signal processing; artificial intelligence; machine learning; deep learning; healthcare assistive techniques; low-power biomedical system design
Dr. Rama S. Komaragiri

E-Mail Website
Guest Editor
Department of ECE, Bennett University Techzone-II, Near Village Dabra, Greater Noida 201310, India
Interests: biomedical systems; biomedical signal processing; MEMS/NEMS sensors; semiconductor device modeling and simulation; low-power CMOS VLSI circuit design; machine learning; deep learning; healthcare assistive techniques; low-power biomedical system design

Special Issue Information

Dear Colleagues,

Cardiovascular arrhythmia diseases are disorders of the heart's electrical system, resulting in an irregular heartbeat. Some common types of arrhythmias include: atrial fibrillation, ventricular tachycardia, bradycardia, supraventricular tachycardia (SVT), and premature ventricular contractions (PVCs) to name a few. Arrhythmias can be benign or life-threatening, and treatment may range from simple lifestyle modifications to medications or surgical procedures. It is important to diagnose and treat arrhythmias promptly to reduce the risk of complications such as stroke, heart failure, and sudden cardiac death. Several techniques have been developed in terms of technology or technological advancement for diagnosing cardiovascular arrhythmia diseases in the last three decades. Some of the recent technical advancements in the diagnosis and treatment of cardiac arrhythmias include: wearable ECG devices, artificial intelligence, and wireless telemetry, implantable devices to name a few. As healthcare-assistive technologies are continuously growing, there is an increasing need for new computational algorithms for diagnosing cardiovascular arrhythmia diseases because traditional diagnostic techniques have limitations and can be time-consuming. ECG and other cardiac signals generate large amounts of data, which can be challenging to analyze using the traditional methods. New computational algorithms for diagnosing cardiovascular arrhythmia diseases can improve the speed and accuracy of diagnoses, reduce the risk of complications, and improve patient outcomes. The aim of this Special Issue is to provide a platform and opportunity to the researcher fraternity to contribute and share their finding and techniques in the field of ‘Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases’.

The aim of this Special Issue is to collect and present a recent advancement where artificial intelligence algorithms are specifically designed for the diagnosis of cardiovascular arrhythmia diseases, domain-specific solutions, or hybrid algorithms that integrate artificial intelligence with traditional numerical and mathematical methods.

We invite paper submissions in the form of research articles, review articles, etc., in the following research areas:

  • Artificial intelligence and machine learning for ECG analysis;
  • Wearable devices for continuous monitoring of cardiac health;
  • Biomarkers for early detection of cardiovascular diseases;
  • Development of new blood tests for diagnosing cardiovascular diseases;
  • Telemedicine for remote monitoring and diagnosis of cardiovascular diseases;
  • Arrhythmia disease detection and classification using deep learning;
  • Transfer learning for cardiac health analysis;
  • Advancements in cardiac catheterization techniques;
  • Increased use of genetic testing for personalized diagnosis and treatment;
  • Non-invasive imaging techniques such as CT angiography and MRI;
  • Development of implantable devices for continuous monitoring;
  • Holter monitor analysis using artificial intelligence.

Dr. Manjeet Kumar
Dr. Ashish Kumar
Dr. Rama S. Komaragiri
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. Diagnostics 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

  • wearable sensors
  • mobile health
  • health monitoring
  • healthcare assistive techniques
  • low-power biomedical circuit design
  • cardiovascular diseases
  • arrhythmia detection and classification
  • artificial intelligence
  • machine learning for arrhythmia
  • deep learning for arrhythmia
  • transfer learning in arrhythmia
  • atrial fibrillation
  • supraventricular fibrillation
  • ventricular fibrillation

Published Papers (3 papers)

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Research

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18 pages, 10895 KiB  
Article
Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation
by Nitish Katal, Saurav Gupta, Pankaj Verma and Bhisham Sharma
Diagnostics 2023, 13(24), 3605; https://doi.org/10.3390/diagnostics13243605 - 05 Dec 2023
Cited by 1 | Viewed by 1572
Abstract
Heart diseases is the world’s principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning [...] Read more.
Heart diseases is the world’s principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases)
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11 pages, 1455 KiB  
Article
Comparison of the 11-Day Adhesive ECG Patch Monitor and 24-h Holter Tests to Assess the Response to Antiarrhythmic Drug Therapy in Paroxysmal Atrial Fibrillation
by Soohyun Kim, Young Choi, Kichang Lee, Sung-Hwan Kim, Hwajung Kim, Sanghoon Shin, Soyoon Park and Yong-Seog Oh
Diagnostics 2023, 13(19), 3078; https://doi.org/10.3390/diagnostics13193078 - 28 Sep 2023
Viewed by 1429
Abstract
Accurate assessment of the response to the antiarrhythmic drug (AAD) in atrial fibrillation (AF) is crucial to achieve adequate rhythm control. We evaluated the effectiveness of extended cardiac monitoring using an adhesive ECG patch in the detection of drug-refractory paroxysmal AF. Patients diagnosed [...] Read more.
Accurate assessment of the response to the antiarrhythmic drug (AAD) in atrial fibrillation (AF) is crucial to achieve adequate rhythm control. We evaluated the effectiveness of extended cardiac monitoring using an adhesive ECG patch in the detection of drug-refractory paroxysmal AF. Patients diagnosed with paroxysmal AF and receiving AAD therapy were enrolled. The subjects simultaneously underwent 11-day adhesive ECG patch monitoring and a 24-h Holter test. The primary study outcome was a detection rate of drug-refractory AF or atrial tachycardia (AT) lasting ≥30 s. A total of 59 patients were enrolled and completed the study examinations. AF or AT was detected in 28 (47.5%) patients by an 11-day ECG patch monitor and in 8 (13.6%) patients by a 24-h Holter test (p < 0.001). The 11-day ECG patch monitor identified an additional 20 patients (33.8%) with drug-refractory AF not detected by the 24-h Holter, and as a result, the treatment plan was changed in 11 patients (10 catheter ablations, one medication change). In conclusion, extended cardiac rhythm monitoring using an adhesive ECG patch in patients with paroxysmal AF under AAD therapy led to over a threefold higher detection of drug-refractory AF episodes, compared to the 24-h Holter test. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases)
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Review

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20 pages, 5686 KiB  
Review
A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
by Ummay Umama Gronthy, Uzzal Biswas, Salauddin Tapu, Md Abdus Samad and Abdullah-Al Nahid
Diagnostics 2023, 13(10), 1732; https://doi.org/10.3390/diagnostics13101732 - 13 May 2023
Cited by 3 | Viewed by 2042
Abstract
Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 [...] Read more.
Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. “Arrhythmia detection”, “arrhythmia classification” and “arrhythmia detection and classification” are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, “performance analysis” and “science mapping”, were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases)
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