ECG heartbeat classification based on combined features extracted by PCA, KPCA, AKPCA and DWT

Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.

The electrocardiogram (ECG) is a diagnostic procedure that uses a skin electrode to record the heart's electrical activity. Heart diseases are the leading cause of mortality globally, and they have significant monetary cost. In the clinic, automated detection and classification technology of abnormalities in heart beats can assist physicians in making prompt and accurate medical diagnoses. This paper concentrates on two proposed models: Model A: 1-D 8-Layer CNN Model and Model B: 1- D CNN and the LSTM model to characterise ECG data into five categories: normal beat, right bundle branch block beat, left bundle branch block beat, premature ventricular contraction beat, and atrial premature beat. The data used to create and validate the models is obtained from the MIT-BIH Dataset, which comprises of 48 half-hour samples of two-lead continuous ECG signals obtained from 48 individuals. The dataset is divided into two files-.csv and.txt. For each sample, the.csv files include the readings collected from both leads. The experimental results show that Model A has an Accuracy = 99.68%, Precision = 99.23%, and a F1 score = 99.22%. And Model B has an Accuracy= 99.51%, Precision = 98.76%, and a F1 score = 98.76%. Our study aims to assist the medical sector by reducing the diagnoses time by automating the process of detecting arrythmias.

Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA

Artificial Intelligence aims to mimic the human cognitive powers. AI is also into healthcare where it's been used for automating the diagnosis processes powered by increased availability of healthcare data. This research work develops an application, which can be used in diagnostic centers for multiple diseases diagnosis like Arrhythmia classification using ECG Image analysis, Malaria using Cell images, Brain tumor detection using MRI Scans and many more. At Present, this research work attempts to develop an Arrhythmia classification using ECG Image analysis and would add other diagnosis later on. Basically, ECG signals are used for analysis of Arrhythmia. The irregularities of heart beat are commonly called Arrhythmia. Here, a Convolutional Neural Network model is proposed for the classification of signals from ECG images to different classes. There are many different classes which are normal beat, Premature ventricular contraction beat, paced beat, atrial bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat, etc.

This paper presents a novel diagnosis method for the classification of cardiac arrhythmia based on electrocardiogram (ECG). The recognition of abnormal heart beat was performed using the context-independent hidden Markov model (HMM) and context-dependent HMM. Gaussian mixture model (GMM) was used as the state emission probability density function. The input feature of the recognition system was the raw heartbeat waveform. There were no other feature transform steps. Six classes of heartbeats, including normal beat, paced beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and ventricular escape beat, were categorized by the system. The performance was evaluated on the MIT-BIH Arrhythmia Database and the European ST-T Database. The experimental results showed that the context-dependent HMM was more precise than the context-independent HMM. The context-dependent HMM system achieved a mean level of 95.26%, 90.67% and 95.60% in heart beats recognition accuracy, sensibility and specificity respectively. The results confirmed the importance of the generative model and the raw waveform feature strategy in ECG study.

ECG beat classification using particle swarm optimization and radial basis function neural network

Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.

Computer-aided diagnosis (CAD) and artificial intelligence (AI) are hot topics in the field of clinical imaging and neuro-imaging. Recently numerous methods were proposed. In this research work, a new 3D magnetic resonance head images (MRI) classifier based on KPCA and SVM is presented. The proposed algorithm, called the support vector machine with kernel principal component analysis (SVM-KPCA), aims to classify an MR brain image as normal or pathological. The system first employed the Discrete Wavelet Transform (DWT) to extract features from the images. After feature vector normalization the kernel principal component analysis (KPCA) is applied to reduce the dimensionality of features. The reduced features were then submitted to a support vector machine (SVM). The strategy of k-fold cross-validation was used to enhance generalization of the proposed algorithm. Seven common brain diseases have been used (Alzheimer's disease, Alzheimer's disease plus visual agnosia, glioma, meningioma, Huntington's disease, sarcoma and Pick's disease) as pathological brains, and MR brain images have been collected from ‘Harvard Medical School’ website and ‘Open Access Series of Imaging Studies (OASIS)’ website, to validate the proposed algorithm. Simulation results were compared with the existing algorithms and it was observed that the proposed work outperforms other algorithms. Working on the same dataset in term of accuracy, sensitivity and specificity.

Aimed at the non-stationary characteristics of rolling bearing vibration signal,a fault diagnosis method was proposed based on local-wave method and KPCA(kernel principal component analysis)-LSSVM(least squares support vector machine).Firstly,local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function(IMF),whose feature energy,singular values and AR model parameters were computed as initial feature vectors.Secondly,ini tial feature vectors were mapped into a higher-dimensional space with KPCA,and the kemel principal components were extracted as new feature vectors,which used as the input of LSSVM for fault classification.The experimental results show the KPCA-LSSVM method improves LSSVM's classification performance by KPCA obtaining additional discriminative information,and has better generalization than direct LSSVM method,and can identify rolling bearing fault patterns more accurately.

Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing. The study considers five cardiac arrhythmias: normal beats, Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), Right Bundle Branch Block (R-BBB), and Left Bundle Branch Block (L-BBB) for classification. Nine distinct temporal features are extracted from the segmented QRS complex. These features are then applied to six different classifiers for arrhythmia classification. The classifiers' performance is evaluated using the MIT-BIH Arrhythmia Database (MIT-BIH AD). Support Vector Machine (SVM) and Ensemble Tree classifiers demonstrate superior performance in classifying the five different classes. Particularly, the Support Vector Machine classifier achieves high sensitivity (97.44%), specificity (99.36%), positive predictive value (97.44%), and accuracy (98.97%) with a Gaussian kernel. This comprehensive approach, integrating preprocessing, and feature extraction, holds promise for improving automatic cardiac arrhythmia classification in clinical trials.

This paper presents a novel algorithm - robust kernel principal component analysis (robust KPCA), on the basis of the research of kernel principal component analysis (KPCA) and robust principal component analysis (RPCA). First, this algorithm sets the radius of the images of the training samples in the feature space using kernel tricks, then determines whether the samples are outliers or not, and finally analyzes the training samples which have eliminated the outliers using KPCA algorithm. The improved KPCA algorithm not only retains the non-linearity property of KPCA algorithm but also gets better robustness. Because the effects of outliers are eliminated, robust KPCA algorithm gets higher accuracy than KPCA algorithm for data analysis. The simulation experiments show that the robust KPCA algorithm developed is better than the KPCA algorithm.

The identification of between-group differences and changes in gait mechanics are useful for injury prevention. Previous studies suggest the differences in gait biomechanical variables may interact in a complex non-linear fashion rather than a simple linear fashion. A non-linear multivariate analysis technique is therefore necessary to unravel the inherent structure in the gait data. Kernel principal component analysis (KPCA) is an extension of principal component analysis (PCA), the most widely used method in this field, and can provide inside into non-linear relationships of the variables of interest. Despite the growing use of PCA in running gait research, no prior studies have evaluated the utility of KPCA in determining the between-group differences in running gait patterns. Therefore, the objective of this study was to compare the performance of KPCA and PCA in identifying differences between male and female runners and as well between young and older runners in running gait kinematics. Running kinematic data were analysed on a gender group (female, n = 50; male, n = 50) and an age group (young, n = 50; older, n = 50) for discrete variables and waveform data. The following key results were obtained: (1) the performance of the KPCA for the identification of gender and age differences in running gait kinematics increased as compared to using the linear PCA; (2) the Gaussian function often performed better than the polynomial function for these two experiments; (3) there was no consistent optimal value of either the width σ of the Gaussian kernel or the degree d of the polynomial kernel for different data types and experiments. These results suggest that non-linear features extracted by KPCA could be potentially useful in identifying and discriminating between-group differences and changes in running gait patterns, and could provide useful information about the intrinsic non-linear dynamics of running movement.

Electrocardiogram (ECG) is the most common way to diagnose cardiac dysfunctions. Many methods for automatic heartbeats classification have been applied and reported in the literature. However, due to differences in the theory and the structure of the types of classifiers, achieving similar results from these classifiers given a common ECG features and datasets, cannot be expected. In this study, a new fusion classification algorithm consisting of diverse classifiers namely, K-nearest neighbors (KNN), neural networks (NN), Support Vector Machine (SVM), and decision tree, is proposed to increase the robustness of the classification algorithm. The proposed fusion classifier was applied to six types of ventricular and atrial complex - normal (N) heart beats, left bundled branch blocks (LBBB), right bundled branch blocks (RBBB), premature ventricular contractions (PVC), atrial premature beats (APB) and paced beats (PB) from the MIT-BIH Arrhythmia Database and obtained an overall accuracy of 95.99%. The accuracies for the classes N, LBBB, RBBB, PVC, APB, and PB are 97.35, 98.65, 98.40, 94.45, 72.25, and 99.85 respectively. Comparison with peer algorithms also proves a better performance in automatic heart arrhythmia classification technologies.

Machine Learning Based Flow Regime Identification using Ultrasonic Doppler Data and Feature Relevance Determination