Electrocardiogram Classification Research Articles

Improving deep-learning electrocardiogram classification with an effective coloring method

Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis and model training, integrating additional clinical information, especially demographic data, remains challenging. In this study, we present an innovative approach to ECG classification by incorporating demographic information from patients’ medical histories through a colorization technique. Our proposed method maps demographic features onto the (R, G, B) color space through normalized scaling. Each demographic feature corresponds to a distinct color, allowing for different ECG leads to be colored. This approach preserves the relationships between data by maintaining the color correlations in the statistical features, enhancing ECG analytics and supporting precision medicine. We conducted experiments with PTB-XL dataset and achieved 1%–6% improvements in the area under the receiving operator characteristic curve performance compared with other methods for various classification problems. Notably, our method excelled in multiclass and challenging classification tasks. The combined use of color features and the original waveform shape features enhanced prediction accuracy for various deep learning models. Our findings suggest that colorization is a promising avenue for advancing ECG classification and diagnosis, contributing to improved prediction and diagnosis of cardiovascular diseases and ultimately enhancing clinical outcomes.

ECG data enhancement method using generate adversarial networks based on Bi-LSTM and CBAM

Objective. The classification performance of electrocardiogram (ECG) classification algorithms is easily affected by data imbalance, which often leads to poor model prediction performance for a few classes and a consequent decrease in the overall performance of the model. Approach. To address this problem, this paper proposed an ECG data augmentation method based on a generative adversarial network (GAN) that combines bidirectional long short-term memory (Bi-LSTM) networks and convolutional block attention mechanism (CBAM) to improve the overall performance of ECG classification models. In this paper, we used two ECG databases, namely the MIT-BIH arrhythmia (MIT-BIH-AR) database and the Chinese cardiovascular disease database (CCDD). The quality of the ECG signals produced by the generated models was assessed using the percent relative difference, root mean square error, Frechet distance, dynamic time warping (DTW), and Pearson correlation metrics. In addition, we also validated the impact of our proposed data augmentation method on ECG classification performance on MIT-BIH-AR database and CCDD. Main results. On the MIT-BIH-AR database, the overall accuracy of the data-enhanced balanced dataset was improved to 99.46% for 15 types of heartbeat classification task. On the CCDD, which focuses on the detection of ventricular precession (PVC), the overall accuracy of PVC detection improved to 99.15% after performing data enhancement. Significance. The experimental results indicate that the data augmentation method proposed in this paper can further improve the ECG classification performance.

Comparative study of time-frequency transformation methods for ECG signal classification

In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to ECG signals is gaining significant attention owing to their exceptional image-classification capabilities. However, we addressed the lack of standardized methods for converting 1D ECG signals into 2D-CNN-compatible input images by using time-frequency methods and selecting hyperparameters associated with these methods, particularly the choice of function. Furthermore, we investigated the effects of fine-tuned training, a technique where pre-trained weights are adapted to a specific dataset, on 2D-CNNs for ECG classification. We conducted the experiments using the MIT-BIH Arrhythmia Database, focusing on classifying premature ventricular contractions (PVCs) and abnormal heartbeats originating from ventricles. We employed several CNN architectures pre-trained on ImageNet and fine-tuned using the proposed ECG datasets. We found that using the Ricker Wavelet function outperformed other feature extraction methods with an accuracy of 96.17%. We provided crucial insights into CNNs for ECG classification, underscoring the significance of fine-tuning and hyperparameter selection in image transformation methods. The findings provide valuable guidance for researchers and practitioners, improving the accuracy and efficiency of ECG analysis using 2D-CNNs. Future research avenues may include advanced visualization techniques and extending CNNs to multiclass classification, expanding their utility in medical diagnosis.

An Electrocardiogram Classification Using a Multiscale Convolutional Causal Attention Network

Electrocardiograms (ECGs) play a pivotal role in the diagnosis and prediction of cardiovascular diseases (CVDs). However, traditional methods for ECG classification involve intricate signal processing steps, leading to high design costs. Addressing this concern, this study introduces the Multiscale Convolutional Causal Attention network (MSCANet), which utilizes a multiscale convolutional neural network combined with causal convolutional attention mechanisms for ECG signal classification from the PhysioNet MIT-BIH Arrhythmia database. Simultaneously, the dataset is balanced by downsampling the majority class and oversampling the minority class using the Synthetic Minority Oversampling Technique (SMOTE), effectively categorizing the five heartbeat types in the test dataset. The experimental results showcase the classifier’s performance, evaluated through accuracy, precision, sensitivity, and F1-score and culminating in an overall accuracy of 99.35%, precision of 96.55%, sensitivity of 96.73%, and an F1-recall of 96.63%, surpassing existing methods. Simultaneously, the application of this innovative data balancing technique significantly addresses the issue of data imbalance. Compared to the data before balancing, there was a significant improvement in accuracy for the S-class and the F-class, with increases of approximately 8% and 13%, respectively.

CardioDefense: Defending against adversarial attack in ECG classification with adversarial distillation training

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adversarial perturbation (SAP) which targets ECG classification; however, to the best of our knowledge, no study has completely explored the defense against adversarial attacks targeting ECG classification. Thus, we did different experiments to explore the effects of defense methods against white-box adversarial attack and black-box adversarial attack targeting ECG classification, and we found that some common defense methods performed well against these attacks. Besides, we proposed a new defense method based on adversarial distillation training (named CardioDefense) which comes from defensive distillation and can effectively improve the generalization performance of DNNs. The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization. Furthermore, we found that our method performed better against PGD attacks with low noise levels, which means that our method has stronger robustness.

COMPUTATIONAL FRAMEWORK FOR PREDICTION OF CARDIAC DISORDERS BY ANALYZING ECG SIGNALS USING MACHINE LEARNING TECHNIQUE

The clinical diagnosis of heart disorders relies heavily on electrocardiograms (ECGs). Numerous abnormalities in heart are being identified with a record of heart signal throughout intervals. Monitoring and diagnosing ECGs signals in daily life are appearing recently due to an increase in healthcare equipment. This paper presents a novel computational framework for detecting heart disorders by analyzing the ECG signals using machine learning technology. The manual interpretation of ECGs by medical experts is time-consuming and subject to human error. This study presents a comprehensive review and implementation of an automated ECG classification system utilizing artificial neural networks (ANNs) for efficient and accurate cardiac condition identification. The proposed ECG classifier employs a multilayered ANN architecture, trained and validated using a diverse dataset of annotated ECG recordings. Preprocessing techniques, including signal denoising, feature extraction, and data augmentation, enhance the quality and relevance of input data for the ANN. The selected features encompass morphological and temporal attributes, essential for distinguishing normal and abnormal ECG patterns. In this paper, noise removal from input ECG dataset is performed by adaptive filter technique and baseline wander is also removed. Machine learning in ECG classification is done by ANN that allows to use less energy while still providing accurate classification. MATLAB software is employed to carry out this work and corresponding outputs are obtained for ECG classification.

A Fuzzy-Operated Convolutional Autoencoder for Classification of Wearable Device-Collected Electrocardiogram

A Fuzzy-Operated Convolutional Autoencoder for Classification of Wearable Device-Collected Electrocardiogram

A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data. This study aims to reliably detect atrial fibrillation (AF) from short-term single-lead (STSL) electrocardiogram (ECG) recordings obtained in unsupervised telehealth environments. A novel deep ensemble-based method was developed for detecting AF from STSL ECG recordings. Following this, a postprocessing algorithm was created to assess uncertainty in classified STSL ECGs and to refrain from interpretation when confidence is low. The proposed method was validated through a 5-fold cross-validation on the Cardiology Challenge 2017 (CinC2017) dataset. The deep ensemble method achieved 83.5 ± 1.5% sensitivity, 98.4 ± 0.2% specificity, and an F 1-score of 0.847 ± 0.016in AF detection. Implementing the selective classification algorithm resulted in significant improvements, with sensitivity increasing to 92.8 ± 2.2%, specificity to 99.7 ± 0.0%, and an F 1-score of 0.919 ± 0.016. The proposed method demonstrates the feasibility of accurately detecting AF from STSL ECG recordings. The selective classification approach offers a substantial enhancement to automated ECG interpretation algorithms in telehealth solutions. These findings highlight the potential for improving the utility of telehealth systems by integrating advanced CDSSs capable of managing uncertainty and ensuring higher accuracy, thereby improving patient outcomes in remote healthcare settings.

Efficient ECG classification based on the probabilistic Kullback-Leibler divergence

Diagnostic systems of cardiac arrhythmias face early and accurate detection challenges due to the overlap of electrocardiogram (ECG) patterns. Additionally, these systems must manage a huge number of features. This paper proposes a new classifier Kullback-Leibler classifier (KLC) that combines feature optimization and probabilistic Kullback-Leibler (KL) divergence. Particle swarm optimization (PSO) is used for optimizing the features of ECG data, while KL divergence counts the variance between training and testing probability distributions. The proposed framework led the new classifier to distinguish normal and abnormal rhythms accurately. MIT-BIH Standard Arrhythmia Dataset (MIT-BIH) is used to test the validity of the proposed model. The experimental results show the proposed classifier achieves results in precision (86.67%), recall (86.67%), and F1_Score (86.5%).

MD-CardioNet: A Multi-Dimensional Deep Neural Network for Cardiovascular Disease Diagnosis from Electrocardiogram.

Automated classification of cardiovascular diseases from electrocardiogram (ECG) signals using deep learning has gained significant interest due to its wide range of applications. However, existing deep learning approaches often overlook inter-channel shared information or lose time-sequence dependent information when considering 1D and 2D ECG representations, respectively. Moreover, besides considering spatial dimension, it is necessary to understand the context of the signals from a global feature space. We propose MD-CardioNet, an efficient deep learning architecture that captures temporal, spatial, and volumetric features from multi-lead ECG signals using multidimensional (1D, 2D, and 3D) convolutions to address these challenges. Sequential feature extractors capture time-dependent information, while a 2D convolution is applied to form an image representation from the multi-channel ECG signal, extracting inter-channel features. Additionally, a volumetric feature extraction network is designed to incorporate intra-channel, inter-channel, and inter-filter global space information. To reduce computational complexity, we introduce a practical knowledge distillation framework that reduces the number of trainable parameters by up to eight times ( from 4,304,910 parameters to 94,842 parameters) while maintaining satisfactory performance compatible with the other existing approaches. The proposed architecture is evaluated on a large publicly available dataset containing ECG signals from over 10,000 patients, achieving an accuracy of 97.3% in classifying six heartbeat rhythms. Our results surpass the performance of some state-of-the-art approaches. This paper presents a novel deep-learning approach for ECG classification that addresses the limitations of existing methods. The experimental results highlight the robustness and accuracy of MD-CardioNet in cardiovascular disease classification, offering valuable insights for future research in this field.

Classification of Cardiac Arrhythmias

Cardiovascular diseases (CVD) are one of the leading causes of mortality worldwide. In this context, the design of accurate and efficient methods for the automated classification of electrocardiogram (ECG) signals is essential for clinical diagnosis, particularly in the detection of cardiac arrhythmias. The main objective of this project is to develop an automated classification system for cardiac arrhythmias using electrocardiogram signals. To carry out this project, the iterative cascade methodology was used, which allows modifications and improvements to be made in each development iteration. The interview technique was used to collect the project requirements, which allowed obtaining detailed and precise information on the needs and expectations of the client.

Chaotic neural networks with complex-valued weight and its applications in electrocardiogram classification

As neural networks are easy to converge to local minimum, the ergodicity of chaotic system is helpful to tackle this problem. Besides, the real parts and imaginary parts in complex-valued chaotic systems are independent, which increase the ergodic property and unpredictability of the chaotic signals. Therefore, we propose a new chaotic neural network with complex-valued weight for electrocardiogram classification. Firstly, a complex Logistic chaotic map is put forward, and its bifurcation diagram, Lyapunov index, and chaotic attractors are analyzed. Secondly, based on the ergodicity of complex Logistic chaotic map and a novel neuron function, the learning algorithm including complex-valued weight iteration for the chaotic neural network is proposed. Finally, the MIT-BIH data-base is used to verify the proposed method. The chaotic neural network with real Logistic map and other classification methods are also adopted for comparison. The results show that our chaotic neural network has a certain improvement in the accuracy of electrocardiogram classification.

Cross-database and cross-channel electrocardiogram arrhythmia heartbeat classification based on unsupervised domain adaptation

The classification of electrocardiogram (ECG) plays a crucial role in the development of an automatic cardiovascular diagnostic system. However, the considerable variances in ECG signals between individuals pose a significant challenge. Changes in data distribution limit cross-domain utilization of a model. In this study, we propose a solution to classify ECG in an unlabeled dataset by leveraging knowledge obtained from labeled source domain. We present a domain-adaptive deep network based on cross-domain feature discrepancy optimization. Our method comprises three stages: pre-training, cluster-centroid computing, and adaptation. In pre-training, we employ a Distributionally Robust Optimization (DRO) technique to deal with the vanishing worst-case training loss. To enhance the richness of the features, we concatenate three temporal features with the deep learning features. The cluster computing stage involves computing centroids of distinctly separable clusters for the source using true labels, and for the target using confident predictions. We propose a novel technique for selecting confident predictions in the target domain. In the adaptation stage, we minimize compacting loss within the same cluster, separating loss across different clusters, inter-domain cluster discrepancy loss, and running combined loss to produce a domain-robust model. Experiments conducted in both cross-domain and cross-channel paradigms show the efficacy of the proposed method. Our method achieves superior performance compared to other state-of-the-art approaches in detecting ventricular ectopic beats (V), supraventricular ectopic beats (S), and fusion beats (F). Our method achieves an average improvement of 11.78% in overall accuracy over the non-domain-adaptive baseline method on the three test datasets.

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models

Automatic detection and localisation of myocardial infarction using multi-channel dense attention neural network

Myocardial infarction (MI) is a life-threatening cardiac disease. Electrocardiogram (ECG) is crucial in the clinical diagnosis of MI, facilitating early localisation and diagnosis, guiding timely intervention, and improving patient outcomes. However, most previous studies required additional denoising operations and did not propose an effective method to reduce differences between patients. Therefore, this paper presents a multi-channel dense attention neural network (MCDANN) for MI detection and localisation using 12-lead ECG signals without denoising preprocessing. MCDANN is composed of 12 parallel channels dedicated to the automatic extraction of heartbeat features. The extracted features from all channels are then aggregated for MI detection and localisation. Each channel consists primarily of the dense layers and the squeeze-and-excitation (SE) module. The dense layers enhance the reuse of feature information within the leads, whereas the SE module weakens less critical information in the ECG classification task. We conduct inter-patient and intra-patient experiments to evaluate the MCDANN model based on the PTB diagnostic database. For MI detection, MCDANN achieved 99.94% accuracy and 99.92% F1 score in intra-patient experiments and 98.27% accuracy and 96.84% F1 score in inter-patient experiments. For MI localisation, MCDANN achieved 99.85% accuracy and 99.84% F1 score in intra-patient experiments, and 81.70% accuracy and 80.73% F1 score in inter-patient experiments. Based on the experimental findings, it is clear that the MCDANN model exhibits remarkable performance in MI detection and localisation, both for intra-patient and inter-patient cases. Notably, it substantially enhances the performance of inter-patient MI localisation compared to other existing methods.

A deep learning framework optimised by Harris Hawks algorithm for intelligent ECG classification in WSN-IoT environment

Nowadays, WSN-IoT may be used to remotely and in real-time monitor patients’ vital signs, enabling medical practitioners to follow their status and deliver prompt treatments. This equipment can evaluate the gathered data on-site thanks to the integration of edge computing, enabling quicker diagnostic and medical options with the need for massive data transmission to a centralized server. Making the most of the resources accessible without sacrificing monitoring efficiency is critical due to the constrained lifespan and resource availability that these intelligent devices still encounter. To make the most of the assets at hand and achieve excellent categorization performance, intelligence must be applied through a learning model. Making the most of the resources that are available without sacrificing performance monitoring is essential given the restricted lifespan and resource availability that these intelligent devices still suffer. A learning model must incorporate intelligence in order to maximize the utilization of resources while maintaining excellent classification performance. In this study, a unique Harris Hawks Optimized Long Short-Term Memory (HHO-LSTM) that categorizes Electrocardiogram (ECG) data without compromising optimum utilization of resources is proposed for Edge enabled WSN devices. We will train the model to correctly categorize various kinds of ECG readings by employing cutting-edge techniques and neural networks. Significant testing is carried out on fifty individuals utilizing real-time test chips with integrated controllers coupled to ECG sensors and NVIDIA Jetson Nano Boards as edge computing devices. To show the benefits of the suggested model, performance comparisons with various deep-learning techniques for peripheral equipment are conducted. Experiments show that in terms of classification results (98% accuracy) and processing expenses, the suggested model, which is based on Edge-enabled WSN devices, beat existing state-of-the-art learning algorithms. The ability of this technology to help medical personnel diagnose a range of heart issues would eventually enhance customer management.

A HYBRID MODEL FOR THE CLASSIFICATION OF PHYSIOLOGICAL AND NEURAL SIGNAL USING CNN-LSTM TECHNIQUE

Physiological and neural signal classification has many important applications in healthcare, including medical diagnosis and monitoring. For example, electrocardiogram (ECG) classification can be used to detect arrhythmias and other cardiac abnormalities; while EEG classification can be used to diagnose neurological disorders such as epilepsy and sleep apnea. This paper presents an LSTM model for the decoding of physiological and neural signals. In this paper, an electroencephalography brain signal data which was gotten from Kaggle.com was used. The dataset was pre-processed so as to remove noise from the data. The pre-processed data was used in training the LSTM model. The LSTM model was trained on fourteen (14) steps. The result of the LSTM model showed an accuracy of 85% at the first step and a validation (testing) accuracy of 90%. For the fourteenth step, the model achieved an accuracy result of 98% for training and 94% for validation (testing). We also evaluated the performance of the model using a classification report and confusion matrix. The result of the classification report showed an accuracy of 95%, which is implication that the performance of the model on the test data is efficient. The confusion matrix was used to specify how well the proposed model classified the electroencephalography signal. The result of the confusion matrix showed that the model predicted the result correctly to be neutral 151 out of 153, positive to be 127 out of 142, and negative to be 128 out of 132. The result showed that the level of false positive and negative values is minimal (0.02% and 0.05%).

Разработка искусственной нейронной сети для классификации ЭКГ

The paper presents a solution to the problem of developing an artificial neural network for classifying electrocardiograms in order to identify a patient's cardiovascular diseases. A literary review of the use of artificial neural networks for use in medicine in the determination of various cardiac pathologies in the condition of patients has been carried out. The most significant six parameters of the heart rhythm have been determined, which can be used in neural network modeling of the state of the human cardiovascular system. An analysis of the capabilities of well-known commercial programs for the classification of electrocardiograms was carried out, their advantages and disadvantages were identified. The capabilities of modern devices for taking cardiograms are described with an indication of the accuracy of their measurements. A study of three different structures of the desired neural network was carried out and the best option was chosen. The software package is made in Python in the Pycharm 2023.1 environment. The network was trained on the basis of the error backpropagation algorithm, the Adam method served as an optimizer. The standard deviation of the network output signal from the standard was used as a criterion. The Softmax function used in the output layer is used to convert the output of neurons into probabilities. Thus, a neural mathematical model for detecting heart diseases has been developed, which has low system requirements for a computer, which can be used to develop software for wearable electronics. An artificial neural network has the following metric: accuracy - 90%, sensitivity - 90%, specificity - 10%.

Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG features. Altogether, 10745 ECGs were recorded for students aged 6 to 18. Manual and automatic ECG features were extracted for each participant. Features were normalized using Z-score normalization and went through the student's t-test and chi-squared test to measure their relevance. We applied the Boruta algorithm for feature selection and then implemented eight classifier algorithms. The dataset was split into training (80%) and test (20%) partitions. The performance of the classifiers was evaluated on the test data (unseen data) by 1000 bootstrap, and sensitivity (SEN), specificity (SPE), AUC, and accuracy (ACC) were reported. In univariate analysis, the highest performance was heart rate and RR interval in the manual dataset and heart rate in an automated dataset with AUC of 0.72 and 0.71, respectively. The best classifiers in the manual dataset were random forest (RF) and quadratic-discriminant-analysis (QDA) with AUC, ACC, SEN, and SPE equal to 0.93, 0.98, 0.69, 0.99, and 0.90, 0.95, 0.75, 0.96, respectively. In the automated dataset, QDA (AUC: 0.89, ACC:0.92, SEN:0.71, SPE:0.93) and stack learning (SL) (AUC:0.89, ACC:0.96, SEN:0.61, SPE:0.99) reached best performances. This study demonstrated that the manual measurement of 12-Lead ECG features had better performance than the automated measurement (MEANS algorithm), but some classifiers had promising results in discriminating between normal and abnormal cases. Further studies can help us evaluate the applicability and efficacy of machine-learning approaches for distinguishing abnormal ECGs in community-based investigations in both adults and children.