Classification of ECG Arrhythmia by Optimizing Multi-layer Perceptron Neural Network Using Genetic Algorithm | Chapter 12 | Contemporary Perspective on Science, Technology and Research Vol. 8

An Electrocardiogram (ECG) graphically records changes in electrical potentials between different sites on the skin due to cardiac activity. ECGs are inexpensive and non-invasive means to observe the heart’s physiology. The heart’s electrical activity is a depolarization and depolarization sequence. ECGs help in identifying cardiac arrhythmia because they have diagnostic information. ECG arrhythmia detection accuracy improves by using machine learning and data mining methods. Genetic Algorithm is an optimization technique trying to replicate natural evolution where individuals with best characteristics adapting to the environment are likely to reproduce and survive. This study proposes multi-layer perceptron neural network optimization using Genetic Algorithm (GA) to classify ECG arrhythmia. Symlet extracts RR intervals from ECG data as features while symmetric uncertainty assures feature reduction. GA optimizes learning rate and momentum. Experimental results show that the proposed optimized neural network achieved classification accuracy and average precision of 96.93% and 96.92% average recall.

Author(s) Details:

V. S. R. Kumari,
Departments of Electronics and Communication, Andra University, Vishakhapatnam, India.

P. Rajesh Kumar,
Departments of Electronics and Communication, Andra University, Vishakhapatnam, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V8/article/view/14107

Lead -aVR of Electrocardiogram (ECG): An Orphan Lead | Chapter 06 | Issues and Developments in Medicine and Medical Research Vol. 4

 This chapter is designed to provide medical students, professors, and clinicians with a comprehensive understanding of -aVR ECG lead interpretation in diverse clinical scenarios. Until recently, among the twelve electrocardiogram leads, Lead –aVR was overlooked for its use (ECG). The augmented and unipolar limb lead -aVR was designed to obtain specific information from the right ventricle outflow tract (RVOT) and the basal portion of the interventricular septum on an electrocardiogram. The reciprocal information from the left lateral side is provided by lead -aVR, which is covered by the leads aVL, II, V5, and V6. This chapter is a collection of the most important facts about the –aVR lead in an ECG. An investigation of the ST-segment in lead -aVR can help predict the coronary angiographic anatomy in acute coronary syndrome (ACS). In an ACS scenario, lead -aVR provides useful information for risk stratification, diagnosis, prognosis prediction, and outcome. The Lead –aVR could be useful in diagnosing, predicting prognosis, and predicting short- and long-term outcomes in a variety of diseases.

Author(S) Details

Virendra C. Patil
Krishna Institute of Medical Sciences Deemed to be University, Karad, Satara, Maharashtra, India.

View Book:- https://stm.bookpi.org/IDMMR-V4/article/view/5543

ECG Classification for Heart Arrhythmia Using Deep Machine Learning | Chapter 4 | New Visions in Science and Technology Vol. 9

 Electrocardiogram (ECG) is a low-cost diagnostic instrument used by healthcare practitioners to examine heart electrical impulses. Arrhythmia, or an aberrant heart signal, can be life-threatening and even fatal. Tachycardia, bradycardia, supraventricular arrhythmias, and ventricular arrhythmias are some of the several types of arrhythmias. Doctors benefit greatly from automated arrhythmia monitoring and classification using ECG. We employ deep machine learning for automated arrhythmia classification in this study, with an emphasis on recent arrhythmia classification trends. We ran intensive and sophisticated simulations on the St. Mary's University Deep Learning Platform to assess the performance of the various arrhythmia classification and detection models. Finally, we show that the proposed deep learning algorithms are more accurate than existing methods in terms of precision and sensitivity.

Author(S) Details

Shalin Savalia
Department of Electrical Engineering, St. Mary’s University, 1 Camino Santa Maria, San Antonio, TX 78228, USA.

Vahid Emamian
School of Science, Engineering and Technology, St. Mary’s University, San Antonio, TX 78228, USA.

View Book:- https://stm.bookpi.org/NVST-V9/article/view/4783

Critical Research on Tracking of Blood Movement in the Heart Using Elongated Horizontal Large Diamond Search Pattern and Optical Flow Technique for Enabling the Detection of Atrial Septal Defect | Chapter 3 | Recent Developments in Engineering Research Vol.4

 

In major congenital heart ailments comes Atrial Septal Defect (ASD). Many techniques viz. ECG, MRI,
Ultrasound etc., are in use to detect ASD. Performance of heart is analyzed by the Doctors through
test results or observations of heart Ultrasound. This paper proposes a new Elongated Horizontal
Large Diamond Search Pattern (EHLDSP) and Optical Flow algorithm for detection of ASD based on
MV. Experimentation was carried out on A4C view of 2D Echo Cardiogram, collected form the
hospitals as well as from open available database. Ground truth image is used by the cardiologist to
compare the results. In this paper, performance of the proposed Elongated Horizontal Large Diamond
Search Pattern (EHLDSP) method is compared with other techniques on the basis of cost functions
like PSNR and computation complexity. EHLDSP algorithm is used to check the pixel movements and
to calculate Motion Vector (MV), followed by the estimation of the displacement of blood cells from
either right to left or vice versa. Proposed algorithm reduces the average computation by providing
speed improvements of about 91% over FS/ES and about 6% over existing DS. Also, EHLDSP has
shown improvements in the PSNR value in comparison with other methods.

Author (s) Details

Prof. Mrunal Ninad Annadate
MIT College of Engineering, Pune, India.

Dr. Manoj Nagmode
Government College of Engineering & Research, Avasari (Khurd), India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/263