Intelligent system based on genetic programming for atrial fibrillation classification

This article focuses on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will study the ECG, and the treatment of the ECG in order to work with it with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers who obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of atrial fibrillation (AF) that they showed, was realized. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction previously mentioned. A new method based on evolutionary algorithms will be used to realize a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology

Atrial fibrillation subtypes classification using the General Fourier-family Transform

Atrial fibrillation patients can be classified into paroxysmal, persistent and permanent attending to the temporal pattern of this arrhythmia. The surface electrocardiogram hides this differentiation. A classification method to discriminate between the different subtypes of atrial fibrillation by using short segments of electrocardiograms recordings is presented. We will process the electrocardiograms (ECGs) using time-frequency techniques with a global accuracy of 80%. Real cases are evaluated showing promising results for an implementation in a semiautomated diagnostic system.This work was supported by grants MTM2010-15200, PrometeoII/2013/013 and UPV-IIS La Fe, 2012/0468.Ortigosa, N.; Cano, O.; Ayala Gallego, G.; Galbis Verdu, A.; Fernandez Rosell, C. (2014). Atrial fibrillation subtypes classification using the General Fourier-family Transform. Medical Engineering and Physics. 36(4):554-560. https://doi.org/10.1016/j.medengphy.2013.12.005S55456036

A novel two-stage heart arrhythmia ensemble classifier

Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives

Evolutionary Optimization of Atrial Fibrillation Diagnostic Algorithms

The goal of this research is to introduce an improved method for detecting atrial fibrillation (AF). The foundation of our algorithm is the irregularity of the RR intervals in the electrocardiogram (ECG) signal, and their correlation with AF. Three statistical techniques, including root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE), are used to detect RR interval irregularity. We use the Massachusetts Institution of Technology / Beth Israel Hospital (MIT-BIH) atrial fibrillation databases and their annotations to tune the parameters of the statistical methods by biogeography-based optimization (BBO), which is an evolutionary optimization algorithm. We trained each statistical method to diagnose AF on each database. Then each trained method was tested on the rest of the databases. We were able to obtain accuracy levels as high as 99 for the detection of AF in the trained databases. We obtained accuracy levels of up to 75 in the tested database

Evolutionary Optimization of Atrial Fibrillation Diagnostic Algorithms

The goal of this research is to introduce an improved method for detecting atrial fibrillation (AF). The foundation of our algorithm is the irregularity of the RR intervals in the electrocardiogram (ECG) signal, and their correlation with AF. Three statistical techniques, including root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE), are used to detect RR interval irregularity. We use the Massachusetts Institution of Technology / Beth Israel Hospital (MIT-BIH) atrial fibrillation databases and their annotations to tune the parameters of the statistical methods by biogeography-based optimization (BBO), which is an evolutionary optimization algorithm. We trained each statistical method to diagnose AF on each database. Then each trained method was tested on the rest of the databases. We were able to obtain accuracy levels as high as 99 for the detection of AF in the trained databases. We obtained accuracy levels of up to 75 in the tested database

1st Symposium of Applied Science for Young Researchers: proceedings

SASYR, the rst Symposium of Applied Science for Young Researchers, welcomes works from young researchers (master students) covering any aspect of all the scienti c areas of the three research centres ADiT-lab (IPVC, Instituto Polit ecnico de Viana do Castelo), 2Ai (IPCA, Instituto Polit ecnico do C avado e do Ave) and CeDRI (IPB, Instituto Polit ecnico de Bragan ca). The main objective of SASYR is to provide a friendly and relaxed environment for young researchers to present their work, to discuss recent results and to develop new ideas. In this way, it will provide an opportunity to the ADiT-lab, 2Ai and CeDRI research communities to gather synergies and indicate possible paths for future joint work. We invite you to join SASYR on 7 July and to share your research!info:eu-repo/semantics/publishedVersio

Dianosing Heart Diseases Using ANN and GA

The heart is complex systems that reveals many clues about its condition in electrocardiogram (ECG), and is one of the most important organs in a human body.The walls of the heart contain myocardial tissues which contract to push the blood through the body. This contract occurs because of passing electrical current in the heart muscle the electrical current can be captured and analyzed to diagnose the heart state. This operation is done by using electrocardiograph (ECG) device; this device captures the electrical signal, filters it from noise signals, and amplifies it. Then it displays the signal on the screen or prints it on the trace paper then the doctor interprets the ECG signal to diagnose the disease.This project discusses using artificial intelligent (AI) to process and analyze the ECG signal to diagnose the heart disease directly and display detailed report about the heart state by using the artificial neural network (ANN) after training it and finding the values of the connection weights using the genetic algorithm (GA) to choose the best values to the weights.The GA is qualified in enhancing the weights of the ANN since the ANN is trained using the classical algorithm (back-propagation), the genetic algorithm is used as a co-training algorithm for enhancing the connection weights values and minimizing the error value

An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (for example, if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily inter- pretable decision statements. We introduce a generative model called the Bayesian List Machine which yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that the Bayesian List Machine has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS2 score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial brillation. Our model is as interpretable as CHADS2, but more accurate