Cardiac Disease Classification Research Articles

Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset. This article presents a methodical exploration of Bi-LSTM (Bi directional Long Short-Term Memory) based DL models for heartbeat classification using various quality metrics. Proposed variants include the Bi-LSTM model, demonstrating remarkable accuracy in classifying the heartbeats into five classes: Normal (N) beat, Supraventricular (S) beat, Ventricular contraction (V), Fusion beats (F), and Unclassifiable Beat (Q). The proposed technique outperforms present classifiers with Accuracy, Sensitivity, Specificity, and F1 score values of 98%, 96.9%, 97.4%, and 97.5% respectively. The simulations are conducted using MATLAB 2020a.

A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings

In this study, we present a novel graph-based methodology for an accurate classification of cardiac arrhythmia diseases using a single-lead electrocardiogram (ECG). The proposed approach employs the visibility graph technique to generate graphs from time signals. Subsequently, informative features are extracted from each graph and then fed into classifiers to match the input ECG signal with the appropriate target arrhythmia class. The six target classes in this study are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), atrial premature contraction (A), and fusion (F) beats. Three classification models were explored, including graph convolutional neural network (GCN), multi-layer perceptron (MLP), and random forest (RF). ECG recordings from the MIT-BIH arrhythmia database were utilized to train and evaluate these classifiers. The results indicate that the multi-layer perceptron model attains the highest performance, showcasing an average accuracy of 99.02%. Following closely, the random forest achieves a strong performance as well, with an accuracy of 98.94% while providing critical intuitions.

Integrated Approach for ECG Signal Classification Using Fused Slantlet Transform and Autoregressive Features

Sudden cardiac death remains a significant concern, emphasizing the critical need for intelligent diagnostic techniques in cardiac disease detection. Cardiologic Vascular Diseases (CVD) pose a significant global health challenge, often associated with unhealthy lifestyle habits. Electrocardiogram (ECG) monitoring plays a crucial role in assessing heart function, but its interpretation can be challenging due to signal complexity. This study proposes a novel approach utilizing fused Slantlet-Spatial features alongside Auto Regression (AR) coefficients to classify electrocardiogram (ECG) signals. Leveraging the Slantlet Transform (SLT) for its superior time localization capabilities and the simplicity of AR coefficients, this methodology aims to enhance the accuracy of cardiac illness classification. Nineteen distinct features were extracted from 300 ECG signals, each comprising 512 samples, enriching the classification scheme's efficacy. The fused feature extraction technique demonstrated success in improving overall accuracy. Employing Binary Particle Swarm Optimization (BPSO), irrelevant features were pruned, resulting in a refined feature vector of only 14 relevant features. This reduction in feature dimensionality significantly influenced the classification accuracy. Subsequently, the refined feature vector was introduced to the classification stage, employing four distinct classifiers: Support Vector Machine (SVM) with various kernel functions, K-Nearest Neighbors (K-NN), Naïve Bayes (NB), and Decision Tree (DT). Simulation results revealed the feasibility of achieving an average classification accuracy of 99.67% across ECG signal categories. Notably, the polynomial kernel function-SVM classifier, with only 2-fold cross-validation, yielded optimal performance. This study emphasizes the potential of fused Slantlet-Spatial and AR features in enhancing the accuracy of cardiac disease classification. Moreover, the efficacy of BPSO in feature selection and the superior performance of the SVM classifier highlight promising avenues for further research in intelligent cardiac diagnosis systems. The proposed approach outperforms existing methods and contributes to the early detection of cardiovascular abnormalities, potentially improving patient outcomes.

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.

Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.

Models of Cardiovascular Surgery Biobanking to Facilitate Translational Research and Precision Medicine

Biobanking in healthcare has evolved over the last few decades from simple biological sample repositories to complex and dynamic units with multi-organisational infrastructure networks and has become an essential tool for modern medical research. Cardiovascular tissue biobanking provides a unique opportunity to utilise cardiac and vascular samples for translational research into heart failure and other related pathologies. Current techniques for diagnosis, classification, and treatment monitoring of cardiac disease primarily rely on interpretation of clinical signs, imaging, and blood biomarkers. Further research at the disease source (i.e., myocardium and blood vessels) has been limited by a relative lack of access to quality human cardiac tissue and the inherent shortcomings of most animal models of heart disease.

Domain randomization using synthetic electrocardiograms for training neural networks

We present a method for training neural networks with synthetic electrocardiograms that mimic signals produced by a wearable single lead electrocardiogram monitor. We use domain randomization where the synthetic signal properties such as the waveform shape, RR-intervals and noise are varied for every training example. Models trained with synthetic data are compared to their counterparts trained with real data. Detection of r-waves in electrocardiograms recorded during different physical activities and in atrial fibrillation is used to assess the performance. By allowing the randomization of the synthetic signals to increase beyond what is typically observed in the real-world data the performance is on par or superseding the performance of networks trained with real data. Experiments show robust model performance using different seeds and on different unseen test sets that were fully separated from the training phase. The ability of the model to generalize well to hidden test sets without any specific tuning provides a simple and explainable alternative to more complex adversarial domain adaptation methods for model generalization. This method opens up the possibility of extending the use of synthetic data towards domain insensitive cardiac disease classification when disease specific a priori information is used in the electrocardiogram generation. Additionally, the method provides training with free-to-collect data with accurate labels, control of the data distribution eliminating class imbalances that are typically observed in health-related data, and the generated data is inherently private.

SUCCESSIVE SUBSPACE LEARNING FOR CARDIAC DISEASE CLASSIFICATION WITH TWO-PHASE DEFORMATION FIELDS FROM CINE MRI.

Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool. While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples. In addition, many deep learning models are deemed a "black box," for which models remain largely elusive in how models yield a prediction and how reliable they are. To alleviate this, this work proposes a lightweight successive subspace learning (SSL) framework for CVD classification, based on an interpretable feedforward design, in conjunction with a cardiac atlas. Specifically, our hierarchical SSL model is based on (i) neighborhood voxel expansion, (ii) unsupervised subspace approximation, (iii) supervised regression, and (iv) multi-level feature integration. In addition, using two-phase 3D deformation fields, including end-diastolic and end-systolic phases, derived between the atlas and individual subjects as input offers objective means of assessing CVD, even with small training samples. We evaluate our framework on the ACDC2017 database, comprising one healthy group and four disease groups. Compared with 3D CNN-based approaches, our framework achieves superior classification performance with 140× fewer parameters, which supports its potential value in clinical use.

Classification of cardiac arrhythmia disease using deep learning auto encoder algorithm

Classification of cardiac arrhythmia disease using deep learning auto encoder algorithm

Valvular heart disease in pregnancy

Key content Pregnancy‐induced changes in haemodynamic physiology can place considerable strain on cardiac function in some women with valvular disease. Regurgitant valve lesions are usually better tolerated in pregnancy than stenotic lesions, although the risk of obstetric complications is increased in both. Pre‐conception counselling is essential for all women with valvular disease. Optimising anticoagulation is a particular challenge in women with mechanical valves. Learning objectives To understand the World Health Organization (WHO) classification of maternal cardiac disease and how this affects counselling regarding decisions around pregnancy for women with valve pathology. To understand how valve disease affects pregnancy and vice versa. To gain insight into the anaesthetic and haematological considerations for managing women with valvular disease. Ethical issues What is the optimum anticoagulation regimen for women with mechanical heart valves in pregnancy that balances both maternal and fetal risks?

Detection of Myocardial Infarction From 12-Lead ECG Trace Images Using Eigendomain Deep Representation Learning

Myocardial infarction (MI) is a life-debilitating emergency in which there is a lack of blood flow in the heart muscle, resulting in permanent damage to the myocardium and sudden cardiac death. The 12-lead electrocardiogram (ECG) is a standardized diagnostic test conducted in hospitals to detect and localize MI-based heart disease. To diagnose MI, the cardiologist visualizes the alternations in the patterns of the 12-lead-based ECG trace image. The automated detection of MI from the 12-lead-based ECG trace image using artificial intelligence (AI) based approaches is important in the clinical study for the accurate diagnosis of MI disease. This paper proposes a novel eigendomain-based deep representation learning approach to automatically detect MI using 12-lead ECG trace images. The singular value decomposition (SVD) and eigendomain grouping are employed to evaluate five modes or components from the 12-lead ECG trace image. The EfficientNetV2B2-based transfer learning model extracts feature maps from the 12-lead ECG trace image and all five modes. The global average pooling (GAP), batch normalization (BN), drop-out, and soft-max layers are used for each feature map to obtain the probability scores. The concatenated probability scores of all feature maps, followed by the dense layer and output layer, are used to detect MI. A public database containing the 12-lead ECG trace images is used to evaluate the performance of the proposed approach. The results show that for the MI class, the proposed approach has achieved the accuracy value of 100%. Similarly, for normal versus MI versus other cardiac arrhythmia-based disease classification schemes, the proposed approach has obtained the overall accuracy, F1-score, specificity, and sensitivity values of 99.03%, 99.01%, 99.49%, and 98.96%, respectively using 5-fold cross-validation (CV). The suggested approach has demonstrated higher overall accuracy than twenty-four existing transfer learning-based models to detect MI using 12-lead ECG trace images.

A Survey on Deep Learning Models Embed Bio-Inspired Algorithms in Cardiac Disease Classification

Deep learning is a sub-field of machine learning that emerged as a noticeable model in the world, specifically for the disease classification field. This work aims to review the state-of-the-art deep learning models in Cardiac Disease prediction by examining several research papers. In this study, popular datasets listed and analyzed in the prediction process of cardiac disease with their performance using various deep learning techniques are presented. This review emphasizes the latest advancement in the six deep learning models, namely, deep neural networks, convolutional neural networks, recurrent neural networks, extreme learning machines, deep belief networks, and transfer learning with its applications. The important features of cardiac disease used by five different countries have been listed that guide researchers to analyze it for future purposes. Freshly, deep learning models have yielded an extended performance in cardiac disease detection that shows its rapid growth. Specifically, deep learning effectiveness concerted with the bio-inspired algorithms is reviewed. This paper also presents what major applications of deep learning techniques have been grasped in the past decade.

Cardiac disease classification from ecg signals using hybrid recurrent neural network method

Medical practitioners and cardiologists frequently employ electrocardiogram (ECG) analysis to assess cardiac health. An automated ECG categorization systems have difficulty detecting and analysing the signals of numerous waves of populations, particularly electrocardiogram (ECG) data. A variety of learning strategies have been researched for the interpretation of ECG categorization. One drawback of machine learning is the use of heuristic features with shallow feature learning architectures. In order to overcome this difficulty, a deep learning approach is used to automatically learn features as opposed to using conventional handmade features. In this work, the use of Long Short-Term Memory (LSTM) and Gated Recurrent Neural Network (GRNN) techniques is proposed to develop an accurate ECG categorization and monitoring system. The purpose of the gated recurrent neural network to processing the sequential data and check the error presented or not in the ECG signals. The long short-term memory is the type of the neural network approaches to identify the complex sequence prediction problems. The GRNN and LSTM models then receive the learnt features that were first captured by the CNN model. There are no custom features required for the ECG categorization in the model. The results section lists various cutting-edge models that the CNN-LSTM and CNN-GRNN models outperformed. The CNN-LSTM and CNN-GRNN designs outperform conventional ECG classification architectures with comparable hyper-parameters, according to the comparison results.

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

IMPROVEMENT OF INCEPTIONV3 MODEL CLASSIFICATION PERFORMANCE USING CHEST X-RAY IMAGES

In this study, we attempted to confirm whether the InceptionV3 model, which shows excellent performance in lung disease classification using chest X-ray images, is suitable for cardiac disease classification. In addition, we proposed a method for improving classification accuracy by improving the structure of the existing InceptionV3 model. The deep learning model used in this study was a modified version of the fully-connected hierarchical structure of InceptionV3. The proposed InceptionV3 model structure was constructed to differentiate between a normal heart and hypertrophic heart. The data used for model training were trained after data augmentation on 1026 chest X-ray images of patients diagnosed with normal heart and cardiac hypertrophy at Kyungpook National University Hospital. The experiment showed a learning classification accuracy of 99.57% and loss of 1.42% for the original InceptionV3 model. The accuracy and loss of the modified InceptionV3 model were 99.81% and 0.92%, respectively. Its classification performance was evaluated based on precision, recall, and F1 score. For a normal heart, precision, recall and F1 score were 78%, 100% and 88%, respectively. For cardiomegaly, classification accuracy, recall and F1 score were 78%, 100% and 88%, respectively. Conversely, the modified model showed 100% precision, 92% recall and 96% F1 score. For cardiomegaly, classification accuracy, recall rate and F1 score were 95, 100 and 97%, respectively. In conclusion, better classification can be achieved if the chest X-ray images for a normal heart and cardiomegaly are classified using the proposed model. Hence, the reliability of the classification performance gradually increases.

A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks

As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiogram (ECG) diagnosis system. Researchers have adopted different methods, such as deep learning, to investigate arrhythmias classification. However, the importance of ECG waveform features is generally ignored when deep learning approaches are applied to classification tasks. P-wave, QRS-wave, and T-wave, containing plenty of physiological information, are three critical waves in the ECG heartbeat. The accurate localization of these critical ECG wave components is a prerequisite for ECG classification and diagnosis. In this study, a novel P-QRS-T wave localization method based on hybrid neural networks is proposed. The raw ECG signal is preprocessed sequentially by filtering, heartbeat extraction, and data standardization. The hybrid neural network is constructed by combining the residual neural network (ResNet) and the Long Short-Term Memory (LSTM). It predicts the relative positions of the P-peak, QRS-peak, and T-peak for each heartbeat. The proposed algorithm was validated on four ECG databases with input noise of different signal-to-noise ratio (SNR) levels. The results show that the proposed method can accurately predict the positions of the three key waves. The proposed P-QRS-T localization approach can improve the efficiency of ECG delineation. Integrated with cardiac disease classification methods, it can contribute to the development of advanced automatic ECG diagnosis systems.

A lightweight hybrid deep learning system for cardiac valvular disease classification

Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical big data and Artificial Intelligence (AI) technology encouraged the effort to develop and deploy deep learning models for distinguishing heart sound abnormalities. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. In this research, a cardiac diagnostic system that combined CNN and LSTM components was developed, it uses phonocardiogram (PCG) signals, and utilizes either augmented or non-augmented datasets. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. For the five classes problem using the open heart sound dataset, accuracy was 98.5%, F1-score was 98.501%, and Area Under the Curve (AUC) was 0.9978 for the non-augmented dataset and accuracy was 99.87%, F1-score was 99.87%, and AUC was 0.9985 for the augmented dataset. Model performance was further evaluated using the PhysioNet/Computing in Cardiology 2016 challenge dataset, for the two classes problem, accuracy was 93.76%, F1-score was 85.59%, and AUC was 0.9505. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.

Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation.

Cardiac cine magnetic resonance imaging (MRI) is a widely used technique for the noninvasive assessment of cardiac functions. Deep neural networks have achieved considerable progress in overcoming various challenges in cine MRI analysis. However, deep learning models cannot be used for classification because limited cine MRI data are available. To overcome this problem, features from cine image settings are derived by handcrafting and addition of other clinical features to the classical machine learning approach for ensuring the model fits the MRI device settings and image parameters required in the analysis. In this study, a novel method was proposed for classifying heart disease (cardiomyopathy patient groups) using only segmented output maps. In the encoder–decoder network, the fully convolutional EfficientNetB5-UNet was modified to perform the semantic segmentation of the MRI image slice. A two-dimensional thickness algorithm was used to combine the segmentation outputs for the 2D representation of images of the end-diastole (ED) and end-systole (ES) cardiac volumes. The thickness images were subsequently used for classification by using a few-shot model with an adaptive subspace classifier. Model performance was verified by applying the model to the 2017 MICCAI Medical Image Computing and Computer-Assisted Intervention dataset. High segmentation performance was achieved as follows: the average Dice coefficients of segmentation were 96.24% (ED) and 89.92% (ES) for the left ventricle (LV); the values for the right ventricle (RV) were 92.90% (ED) and 86.92% (ES). The values for myocardium were 88.90% (ED) and 90.48% (ES). An accuracy score of 92% was achieved in the classification of various cardiomyopathy groups without clinical features. A novel rapid analysis approach was proposed for heart disease diagnosis, especially for cardiomyopathy conditions using cine MRI based on segmented output maps.

Hilbert-envelope features for cardiac disease classification from noisy phonocardiograms

Cardiovascular disease (CVD) is considered a significant public health concern around the world. Automated early diagnostic tools for CVDs can provide substantial benefits, especially in low-resource countries. This study proposes a time-domain Hilbert envelope feature (HEF) extraction scheme that can effectively distinguish among different cardiac anomalies from heart sounds even in highly noisy recordings. The method is motivated by how a cardiologist listens to the heart murmur configurations, e.g., the intensity of the heart sound envelope over a cardiac cycle. The proposed feature is invariant to the heart rate, the position of the first and second heart sounds, and robust in extracting the murmur configuration pattern in the presence of respiratory noise. Experimental evaluations are performed compared to two different state-of-the-art methods in the presence of respiratory noise with signal-to-noise ratio (SNR) values ranging from 0–15 dB. The proposed HEF, fused with standard acoustic and Resnet features, yields an average accuracy, sensitivity, specificity, and F1-score of 94.78% (±2.63), 87.48 %(±6.07), 96.87% (±1.51) and 87.47% (±5.94), respectively, while using a random forest (RF) classifier and applied on a mixture of clean and noise mixed recordings of an open-access dataset. Compared to the best-performing baseline model, this feature-fusion scheme provides a significant performance improvement (p<0.05), notably achieving an absolute improvement of 6.16% in averaged sensitivity. When applied to noisy heart sound recordings collected from a local hospital, this method significantly outperforms the existing systems by achieving an average accuracy of 80.54% (±2.65) and sensitivity of 68.52% (±4.54). The achieved sensitivity yields an absolute improvement of 12.65% compared to the best-performing baseline model in this real-world dataset.

Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology.

Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach.

EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

AbstractCardiac disease is one of the leading causes of death worldwide, and its early detection and diagnosis can considerably increase the lifespan of patients. An automated expert system for early and accurate diagnosis that offsets human error can be designed using machine intelligence and appropriate pre‐processing of data to ensure accuracy. To that end, enhanced binary particle swarm optimization (EBPSO) has been investigated in this paper to enable the definitive classification of cardiac disease with the aid of feature selection. The primary objective of this study is to improve the accuracy of the classification and the convergence speed while employing a lesser number of features. In order to achieve this, a new adaptive inertia weight was introduced to balance the exploitation and exploration capability of binary particle swarm optimization (BPSO). In addition, a novel idea called ‘neighbourhood best’ is introduced in the velocity update equation to improve the convergence speed. The proposed EBPSO approach was tested over well‐known datasets of heart disease, that is, the Cleveland dataset, Hungarian dataset, Switzerland dataset, and Long‐Beach‐Va datasets. For classification purposes, two well‐known classifiers, k‐nearest neighbour, and support vector machine were used with the EBPSO. Furthermore, the performance of EBPSO was compared with the traditional BPSO, as well as with BPSO with proposed inertia weight (Wa‐BPSO) and other PSO variants. It has been observed that the proposed approach EBPSO with KNN shows remarkable efficiency in terms of average classification accuracy and convergence rate. The average classification accuracy for Cleveland dataset is 92.814%, for the Hungarian dataset is 91.97%, for the Switzerland dataset is 91.626% with EBPSO+KNN and for the Long‐Beach‐Va dataset is 85.253% with EBPSO + SVM. Additionally, the performance of Wa‐BPSO was compared with other commonly used inertia weights. The results of this study demonstrated that the proposed approach produces better results in terms of both the accuracy of classification as well as the convergence rate with a reduced feature set.