Classification of myocardial infarction with multi-lead ECG signals and deep CNN

A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems

PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号，尤以心电图（ECG）为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

PhysioBank

An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection

Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Genetic algorithms are very effective way of finding a very effective way of quickly finding a reasonable solution to a complex problem. Performance of genetic algorithms mainly depends on type of genetic operators which involve crossover and mutation operators. Different crossover and mutation operators exist to solve the problem that involves large population size. Example of such a problem is travelling sales man problem, which is having a large set of solution. In this paper we will discuss different crossover operators that help in solving the problem.

/pdf/crossover-operators-in-genetic-algorithms-a-review-15kgb4onyk.pdf

Crossover Operators in Genetic Algorithms: A Review

Hybrid Firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block

An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine

The medical practitioners study the electrical activity of the human heart in order to detect heart diseases from the electrocardiogram (ECG) of the heart patients. A myocardial infarction (MI) or heart attack is a heart disease, that occurs when there is a block (blood clot) in the pathway of one or more coronary blood vessels (arteries) that supply blood to the heart muscle. The abnormalities in the heart can be identified by the changes in the ECG signal. The first step in the detection of MI is Preprocessing of ECGs which removes noise by using filters. Feature extraction is the next key process in detecting the changes in the ECG signals. This paper presents a method for extracting key features from each cardiac beat using Improved Bat algorithm. Using this algorithm best features are extracted, then these best (reduced) features are applied to the input of the neural network classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.

/pdf/improved-bat-algorithm-for-the-detection-of-myocardial-21t7xr8bbd.pdf

Padmavathi Kora

Papers

Crossover Operators in Genetic Algorithms: A Review

Hybrid Firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block

An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine

Improved Bat algorithm for the detection of myocardial infarction

ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm