ARRHYTHMIA CLASSIFICATION WITH THE INTELLIGENCE PARASITISM ALGORITHM-OPTIMIZED FUZZY-ENABLED ENSEMBLED CLASSIFIER

Arrhythmia classification and detection are essential for the early diagnosis of heart diseases, but accurately identifying arrhythmias is challenging due to the inherent noise in electrocardiogram (ECG) data. This study presents a novel method for arrhythmia detection that follows a systematic approach. First, ECG data from the MIT-BIH Arrhythmia Database and the PTB Diagnostic Database are preprocessed using three distinct filters: wavelet transform (WT), finite impulse response (FIR), and an innovative infinite impulse response (IIR) filter to remove noise. The filtered data are then processed through a one-dimensional convolutional neural network (1D-CNN) and graph convolutional networks (GCNs) for feature extraction. These features are initially classified using a support vector machine (SVM) into normal and abnormal categories. For further detection, the classified data undergo additional processing through Short-Time Fourier Transform (STFT) for time-frequency analysis, followed by Principal Component Analysis (PCA) for dimensionality reduction. Finally, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed to detect specific arrhythmias within the classified signals. The proposed method is evaluated using the MIT-BIH and PTB datasets, demonstrating that the combination of STFT, PCA, and BiLSTM significantly enhances the detection of specific arrhythmias. The inclusion of GCNs and the innovative IIR filter further improves classification accuracy. This approach's effectiveness is validated through comparisons with existing arrhythmia identification methods, highlighting its advancements in both classification and detailed arrhythmia detection.

This study introduces a robust and efficient hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BLSTM) networks for the automated detection and classification of cardiac arrhythmias from electrocardiogram (ECG) signals. The proposed architecture leverages the complementary strengths of both components: the CNN layers autonomously learn and extract salient morphological features from raw ECG waveforms, while the BLSTM layers effectively model the sequential and temporal dependencies inherent in ECG signals, thereby improving diagnostic accuracy. To further enhance training stability and non-linear representation capability, the Mish activation function is incorporated throughout the network. The model was trained and evaluated using a combination of the widely recognized MIT-BIH Arrhythmia Database and de-identified clinical ECG recordings sourced from collaborating healthcare institutions, ensuring both diversity and clinical relevance of the dataset. Notably, the framework operates with minimal preprocessing, underscoring its practical viability for real-time implementation. Experimental results demonstrate the model’s exceptional performance, achieving an overall classification accuracy of 99.52%, sensitivity of 99.48%, and specificity of 99.85%. These outcomes highlight the model’s robustness, generalizability, and strong potential for integration into clinical decision support systems, particularly in high-throughput or resource-constrained healthcare environments.

This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre-processing. Feature extraction is done with morphological and dynamic features to detect morphological arrhythmia the shape morphological properties of the ECG signal. Dynamic arrhythmia could be diagnosed by having some feature of the ECG signal such as amplitude and position of the QRS peak. Using the modified pan Tompkins algorithm, arrhythmia was detected. Load the ECG signal after getting QRS complex R and p peak of ECG signal detected. For the deep learning classification modified RNN is used as a classifier. The modified RNN is trained independently for each of the 17 classes using training and validation data, Data from the validation phase is utilized to calculate network parameters tweaking. The acquired results demonstrate the proposed method’s effectiveness. The overall classification accuracy for 17 cardiac arrhythmias was 95.33%. For each 10 s ECG data, the classification time was 0.015 s. The proposed technique is compared in terms of accuracy to that of other existing techniques, revealing that the new method outperforms them.

Automatic detection of arrhythmia using electrocardiogram (ECG) and deep learning (DL) is very important to reduce the global death rate from cardiovascular diseases (CVD). Previous studies on automatic arrhythmia detection relied largely on various ECG features and have achieved considerable classification accuracy using DL-based models. However, most previous research has ignored multi-class imbalanced problems in ECG arrhythmia detection. Therefore, it remains a challenge to improve the classification performance of the DL-based models. This paper proposes a novel parallel cross convolutional recurrent neural network in order to improve the arrhythmia detection performance of imbalanced ECG signals. The proposed model incorporates a recurrent neural network and a two-dimensional (2D) convolutional neural network (CNN) and can effectively learn temporal characteristics and rich spatial information of raw ECG signals. Continuous wavelet transform (CWT) is used to transform the ECG signals into a 2D scalogram composed of time–frequency components, and subsequently, the 2D-CNN can learn spatial information from the 2D scalogram. The proposed model is not only efficient in learning features with imbalanced samples but can also significantly improve model convergence with higher accuracy. The overall performance of our proposed model is evaluated based on the MIT-BIH arrhythmia dataset. Detailed analysis of evaluation metrics reveals that the proposed model is very effective in arrhythmia detection and significantly better than the existing hierarchical network models.

Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.

The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model’s generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.

This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat abnormalities. The physiological detection using electrocardiogram (ECG) signals has been the most popular means and widely accepted automated detection system to monitor heart health. Additionally, arrhythmia beat classification plays a prominent role in electrocardiogram (ECG) analysis dedicated elucidate cardiac health status while analyzing heart rhythm. The authors aim to classify ECG samples into major arrhythmia classes precisely by removing the inherent noise of ECG signals in preprocessing phase using discrete wavelet transformation (DWT). The QRS complex plays a crucial role in ECG signal identification. Therefore, the position and amplitude of R-peaks are determined to detect the QRS complex. The feature vectors of the QRS complex are further optimized with cuckoo search (CS) optimization algorithm in addition to denoising signals using DWT to select the most relevant set of features. The Support vector machine (SVM)-trained support vector contains the best training information used to train feed-forward back-propagation neural network (FFBPNN) to propose the variant DWT + CS + SVM-FFBPNN to classify signals among five classes. MIT-BIH arrhythmia database is utilized for different types of heartbeats. The classification analysis based on a variant with optimized feature vector using cuckoo search algorithm and SVM-FFBPNN determines heart rate with an accuracy of 98.319%. In contrast, the variant FFBPNN without optimization obtains 97.95% accuracy. The improved performance of the novel combination of classifiers resulted in overall classification accuracy of 98.53% with precision and recall of 98.247% and 95.68%, respectively. The simulation analysis comprising 3600 samples and 1160 heartbeats also outperformed the existing arrhythmia classifications performed based on neural networks. This illustrates the success of the proposed ECG classification model in accurately categorizing ECG signals for arrhythmia classification.

This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrations, bumpy road conditions, and the driver’s steering wheel gripping force. The proposed scheme extracts stable ECG signals and transforms them into full 10 s ECG signals to classify arrhythmias using convolutional neural networks (CNN). Before the ECG stitching algorithm is applied, data preprocessing is performed. To extract the cycle from the collected ECG data, the R peaks are found and the TP interval segmentation is applied. An abnormal P peak is very difficult to find. Therefore, this study also introduces a P peak estimation method. Finally, 4 × 2.5 s ECG segments are collected. To classify arrhythmias with stitched ECG data, each time series’ ECG signal is transformed via the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and transfer learning is performed for classification using CNNs. Finally, the parameters of the networks that provide the best performance are investigated. According to the classification accuracy, GoogleNet with the CWT image set shows the best results. The classification accuracy is 82.39% for the stitched ECG data, while it is 88.99% for the original ECG data.

Context: With the widespread availability of portable electrocardiogram (ECG) devices, there is an increasing interest in utilizing artificial intelligence (AI) methods for ECG signal analysis and arrhythmia detection. The potential benefits of AI-assisted arrhythmia prognosis, early screening, and improved accuracy in arrhythmia classification are discussed. Evidence Acquisition: Artificial intelligence methods are a new way to classify different types of arrhythmias. For example, deep learning (DL) algorithms, including long short-term memory (LSTM) networks, convolutional neural networks (CNN), CNN-based autoencoders (AE), and convolutional recurrent neural networks (CRNN), have been extensively utilized for ECG signal analysis and arrhythmia detection. Results: This study explores different DL techniques for classifying arrhythmias. The two-dimensional (2D) CNN model achieved an accuracy of 97.42% in classifying five different arrhythmias. After classifying five types of ECG signals, an accuracy of 99.53% was achieved by the CNN-based AE and transfer learning (TL) models. The CNN-Bi-LSTM model achieved an accuracy of 98.0% in categorizing five categories of ECG signals. The CNN+LSTM model achieved an accuracy of 98.24% in classifying five classes of arrhythmias. The CNN-support vector machine (SVM) classifier model demonstrated an accuracy of 98.64% in detecting seventeen classes of heartbeats. The results indicated that the CNN-based AE and TL models perform exceptionally well with high accuracy in detecting ECG signals. Conclusions: The present study demonstrates the growing interest in utilizing DL for ECG signal detection in medical and healthcare applications over the past decade. Deep learning models have been shown to outperform experienced cardiologists, delivering state-of-the-art and more accurate results.

Electrocardiograms (ECGs) are vital tools for monitoring heart activity and diagnosing a wide range of heart conditions, including arrhythmias, which are characterized by irregular heartbeats. Arrhythmias, if not properly diagnosed and treated, can result in serious complications such as stroke, heart failure, or even sudden cardiac arrest. Given these risks, the accurate detection and classification of arrhythmias is critical for ensuring timely and effective medical interventions. This project aims to tackle this challenge by using the MIT-BIH Arrhythmia Database, a wellestablished resource for arrhythmia classification, which contains data for five types of arrhythmias: Normal beat (N), Supraventricular ectopic beat (S), Ventricular ectopic beat (V), Fusion beat (F), and Unknown beat (Q). These different types of heartbeats present varying levels of difficulty in classification, and one of the key challenges is the imbalance in the dataset, where some arrhythmia types are significantly underrepresented. The project uses a hybrid deep learning model that blends Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs) to overcome these obstacles. CNNs are wellsuited to capture the minute changes in heartbeats that are indicative of various arrhythmia types because they are very good at spotting spatial characteristics and patterns within the ECG signal. On the other hand, LSTMs are designed to handle the temporal relationships in sequential data, which is critical when dealing with ECG signals and other time-series data. The hybrid model can handle the ECG data more efficiently and increase classification accuracy by fusing the advantages of both CNNs and LSTMs. Furthermore, to improve the quality of the input signals and lessen the consequences of dataset imbalance, sophisticated signal processing techniques are applied to the raw ECG data. This all-encompassing strategy not only enhances the model's overall performance but also strengthens its ability to detect uncommon arrhythmias that conventional techniques frequently miss. Ultimately, this project demonstrates the potential of using large, annotated medical databases and cutting-edge computational methods to improve automated arrhythmia diagnosis's precision and dependability, paving the way for better management of cardiovascular health.

IntroductionArrhythmia, characterized by irregular heartbeats, can range from harmless to potentially life-threatening disturbances in heart rhythm. Effective detection and classification of arrhythmias are crucial for timely medical intervention and management.MethodsThis research utilizes the MIT-BIH Arrhythmia Database, a well acknowledged benchmark dataset, to train and validate the proposed EGOLFNet model, Enhanced Gray Wolf Optimization with LSTM Fusion Network. This model integrates advanced optimization techniques with deep learning to enhance diagnostic accuracy and robustness in arrhythmia detection. The methodology includes preprocessing the ECG signals to normalize and filter out noise, followed by feature extraction using statistical methods and wavelet transforms. The distinctive aspect of EGOLF-Net involves using Enhanced Gray Wolf Optimization to select optimal features, which are then processed by LSTM layers to capture temporal dependencies in the ECG data effectively.Results and Discussion The model achieved an accuracy of 99.61%, demonstrating the potential of EGOLF-Net as a highly reliable tool for classifying arrhythmias, significantly advancing the capabilities of cardiology diagnostic systems. Thus the proposed EGOLF-Net model was developed and validated for accurately identifying heart arrhythmias using electrocardiogram (ECG) data.

In this study, we propose an electrocardiogram (ECG) system for the simultaneous and remote monitoring of multiple heart patients. It consists of three main components: patient, sever, and monitoring units. The patient unit uses a wearable miniature sensor that continuously measures ECG signals and sends them to a smart mobile phone via a Bluetooth connection. In the mobile device, the ECG signals can be stored, displayed on screen, and automatically transmitted to a distant server unit over the internet; the server stores ECG data from several patients. Health care stakeholders use a monitoring unit to retrieve the ECG signals of multiple patients at any time from the server for display and real-time automatic analysis. The analysis includes segmentation of the ECG signal into separate heartbeats followed by arrhythmia detection and classification. When compared to existing real-time ECG systems, where the detection of abnormalities is usually performed using simple rules, the proposed system implements a real-time classification module that is based on a support vector machine (SVM) classifier. Extensive experimental results on ECG data obtained from a TechPatientTMsimulator, a real person, and 20 records from the MIT arrhythmia database are reported and discussed.

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.

Detection of arrhythmia in 12-lead varied-length ECG using multi-branch signal fusion network

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.