Padmavathi Kora

Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block

Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging–Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg–Marquardt Neural Network classifier.

Improved Bat algorithm for the detection of myocardial infarction

AbstractThe 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.

ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm

BACKGROUND AND OBJECTIVE Myocardial Infarction (MI) is one of the most frequent diseases, and can also cause demise, disability and monetary loss in patients who suffer from cardiovascular disorder. Diagnostic methods of this ailment by physicians are typically invasive, even though they do not fulfill the required detection accuracy. METHODS Recent feature extraction methods, for example, Auto Regressive (AR) modelling; Magnitude Squared Coherence (MSC); Wavelet Coherence (WTC) using Physionet database, yielded a collection of huge feature set. A large number of these features may be inconsequential containing some excess and non-discriminative components that present excess burden in computation and loss of execution performance. So Hybrid Firefly and Particle Swarm Optimization (FFPSO) is directly used to optimise the raw ECG signal instead of extracting features using the above feature extraction techniques. RESULTS Provided results in this paper show that, for the detection of MI class, the FFPSO algorithm with ANN gives 99.3% accuracy, sensitivity of 99.97%, and specificity of 98.7% on MIT-BIH database by including NSR database also. CONCLUSIONS The proposed approach has shown that methods that are based on the feature optimization of the ECG signals are the perfect to diagnosis the condition of the heart patients.

ECG Based Myocardial Infarction Detection Using Different Classification Techniques

ECG signal classification is essential for the production of high grade classification results to support diagnostic decisions and develop treatments. Recent methods of feature extraction—for example, autoregressive (AR) modeling; magnitude squared coherence (MSC); wavelet coherence (WTC) using the PhysioNet database—have yielded an extensive set of features. A large number of these features may be inconsequential, as they contain superfluous components that put an excessive burden on computation leading to a loss of performance. For this reason, the hybrid firefly and particle swarm optimization (FFPSO) method is used to optimize the raw ECG signal instead of extracting features using AR, MSC and WTC. This chapter proposes a design for an efficient system for the classification of mocardial infarction (MI) using an artificial neural network (ANN) (Levenberg-Marquardt Neural Network) and two different classifiers. Our experimental results show that an FFPSO algorithm with an ANN give a 99.3% rate of accuracy when combining the MIT-BIH and the NSR databases.

ECG Signal Dimensionality Reduction-Based Atrial Fibrillation Detection

Atrial fibrillation (AF) is a major heart disorder in clinical practice occurring due to irregular heart rates, leading to morbidity and mortality. To cure this heart disorder, the primary constraint is the detection of this problem. The major limitation of AF detection is that it is not possible to detect AF at first due to rapid and overlapping atrial and ventricular waves. A continuous process of detection is required for proper diagnosis and this demands automated methods. This can be accomplished by considering electrocardiography (ECG), which is a principal diagnostic tool useful for examining the function of the human heart. The ECG signal of the AF is primarily preprocessed by means of a Savizky-Golay filter, following which the denoised features obtained are applied to feature extraction techniques such as the discrete wavelet transform (DWT), complex Hadamard transform (CHT) and conjugate symmetric-complex Hadamard transform (CS-CHT) to eliminate redundancy. In DWT, the features extracted contain both time and frequency components. In CHT and CS–CHT, the features of an ECG signal can be obtained only by considering four orders: natural, Paley or dyadic, sequency and Cal–Sal. The features extracted using these techniques are then further applied to feature selection technique methods: principal component analysis (PCA) and methods inspired by nature such as genetic algorithms (GAs) to obtain optimized features. The optimization of these features is evaluated by applying them to a Levenberg-Marquardt neural network (LM-NN) classifier. The performance of the AF detection method is evaluated by calculating sensitivity, specificity and accuracy. Experimental results of the proposed method show that the Cal–Sal order of CS-CHT as feature extraction applied to the feature selection technique of GA yields the best accuracy, with a value of 99.7%, compared with all other techniques.

Bundle Block Detection Using Genetic Neural Network

Abnormal Cardiac beat identification is a key process in the detection of heart ailments. This work proposes a technique for the detection of Bundle Branch Block (BBB) using Genetic Algorithm (GA) technique in combination with Levenberg Marquardt Neural Network (LMNN) classifier. BBB is developed when there is a block along the electrical impulses travel to make heart to beat. The Genetic algorithm can be effectively used to find changes in the ECG by identifying best features (optimized features). For the detection of normal and Bundle block beats, these Genetic features values are given as the input for the LMNN classifier. ECG, Bundle Branch Block, Genetic Algorithm, LMNN classifier.

Detection of Bundle Branch Block Using Bat Algorithm and Levenberg Marquardt Neural Network

Abnormal Cardiac beat identification is a key process in the detection of heart ailments. This work proposes a technique for the detection of Bundle Branch Block (BBB) using Bat Algorithm (BA) technique in combination with Levenberg Marquardt Neural Network (LMNN) classifier. BBB is developed when there is a block along the electrical impulses travel to make heart to beat. The Bat algorithm can be effectively used to find changes in the ECG by identifying best features (optimized features). For the detection of normal and Bundle block beats, these Bat feature values are given as the input for the LMNN classifier.