Special Issue "Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases"
Deadline for manuscript submissions: 31 January 2024 | Viewed by 1855
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
Interests: biomedical systems design; signal analysis; biomedical signal processing; artificial intelligence; machine learning; deep learning; healthcare assistive techniques; low-power biomedical system design
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
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
Manuscript Submission Information
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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.
- 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