Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals

A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.

Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach

Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients' short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of life-threatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets.

Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function

Objective.This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data.Approach.We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and theRwave information branch to analyzeRwave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network's learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network's ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification.Main results.The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99%for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92%for Fusion beat, respectively.Significance.This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.

Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach

Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.

Lightweight convolution transformer for cross-patient seizure detection in multi-channel EEG signals

BACKGROUND

Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizures frequency and severity to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies.

METHODS AND MATERIALS

This study proposes a novel deep learning architecture-based Lightweight Convolution Transformer (LCT). The Transformer model is able to learn spatial and temporal correlated information simultaneously from the multi-channel electroencephalogram (EEG) signal to detect seizures at smaller segment lengths. In the proposed work, the lack of translation equivariance and localization of ViT is reduced using convolution tokenization, and rich information from the Transformer encoder is extracted by sequence pooling instead of the learnable class token.

RESULTS

Extensive experimental results demonstrate that the proposed model on cross-patient learning can effectively detect seizures from the raw EEG signals. The accuracy and F1-score of seizure detection in the cross-patient case on the CHB-MIT dataset are 96.31% and 96.32%, respectively, at 0.5 sec segment length. In addition, the performance metrics show that the inclusion of inductive biases and attention-based pooling in the model enhances the performance and reduces the number of Transformer encoder layers, which significantly reduces the computational complexity. In this research, we provide a novel approach to enhance efficiency and simplify the architecture for multi-channel automated seizure detection.

Electrocardiogram signal compression using adaptive tunable-Q wavelet transform and modified dead-zone quantizer

The electrocardiogram (ECG) signals are commonly used to identify heart complications. These recordings generate large data that needed to be stored or transferred in telemedicine applications, which require more storage space and bandwidth. Therefore, a strong motivation is present to develop efficient compression algorithms for ECG signals. In the above context, this work proposes a novel compression algorithm using adaptive tunable-Q wavelet transform (TQWT) and modified dead-zone quantizer (DZQ). The parameters of TQWT and threshold values of DZQ are selected using the proposed Sparse-grey wolf optimization (Sparse-GWO) algorithm. The Sparse-GWO is proposed in this work to reduce the computation time of the original GWO. Moreover, it is also compared with some popular algorithms such as original GWO, particle swarm optimization (PSO), Hybrid PSOGWO, and Sparse-PSO. The DZQ has been utilized to perform thresholding and quantization. Then, run-length encoding (RLE) has been used to encode the quantized coefficients. The proposed work has been performed on the MIT-BIH arrhythmia database. Quality assessment performed on reconstructed signals ensure the minimal impact of compression on the morphology of reconstructed ECG signals. The compression performance of proposed algorithm is measured in terms of the following evaluation matrices: percent root-mean-square difference (PRD1), compression ratio (CR), signal-to-noise ratio (SNR), and quality score (QS1). The obtained average values are 3.21%, 20.56, 30.62 dB, and 7.79, respectively.

Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier

Myocardial infarction (MI), referred to as a heart attack, is a life-threatening condition that happens due to blood clots, typically, blood flow to a portion of the heart muscle is blocked. The cardiac muscle may become permanently damaged if there is insufficient oxygen and blood flow to the affected area. It's crucial to treat MI as soon as possible because even a small delay might have serious effects. The primary diagnostic tool to track and identify the signs of MI is the electrocardiogram (ECG). The complexity of MI signals combined with noise makes it difficult for clinicians to make a precise and prompt diagnosis. It might be laborious and time-consuming to manually analyse an enormous quantity of ECG data. Therefore, techniques for autonomously diagnosing from the ECG data are required. There have been numerous research on the topic of MI espial, but the majority of the algorithms are cognitively intensive when working with empirical data. The current study suggests a unique method for the efficient and reliable identification of MI. We employed circulant singular spectrum analysis (CSSA) for baseline wander removal, a 4-stage Savitzky-Golay (SG) filter to expunge powerline interference from the ECG signal and segmented in the preprocessing stage. Thus segmented ECG has been decomposed using CSSA, entropy based features are extracted. The best features are selected by using binary Harris hawk optimization (BHHO) and to machine learning (ML) classifiers like Naive Bayes, Decision tree, K-nearest neighbor (KNN), Support vector machine (SVM), and Ensemble subspace KNN. Our suggested method has been examined from both class as well as subject oriented perspectives. While the subject-oriented technique uses data from one patient for testing while using data from the other subjects for training, the class-wise strategy divides data as test data as well as training data regardless of subjects. We succeeded in achieving accuracy (A c % ) of 99.8, sensitivity (S e % ) of 99, and 100 specificity (Sp%) under the class-oriented approach. Similarly, for the subject wise strategy we achieved a mean A c % , Se%, and Sp% of 85.2, 83.1, and 84.5, respectively.

Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment

This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia classification from ECG signal inputs. ArrhyMon targets to detect and classify six different types of arrhythmia apart from normal ECG patterns. To the best of our knowledge, ArrhyMon is the first end-to-end classification model that successfully targets the classification of six detailed arrhythmia types and compared to previous work does not require additional preprocessing and/or feature extraction operations separate from the classification model. ArrhyMon's deep learning model is designed to capture and exploit both global and local features embedded in ECG sequences by integrating fully convolutional network (FCN) layers and a self-attention-based long and short-term memory (LSTM) architecture. Moreover, to enhance its practicality, ArrhyMon incorporates a deep ensemble-based uncertainty model that generates a confidence-level measure for each classification result. We evaluate ArrhyMon's effectiveness using three publicly available arrhythmia datasets (i.e., MIT-BIH, Physionet Cardiology Challenge 2017 and 2020/2021) to show that ArrhyMon achieves state-of-the-art classification performance (average accuracy 99.63%), and that confidence measures show close correlation with subjective diagnosis made from practitioners.

NSICA: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis

This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) to solve optimal feature selection problems. The NSICA is a multi-objective and discrete version of the original Imperialist Competitive Algorithm (ICA) that utilizes the competition between colonies and imperialists to solve optimization problems. This study focused on solving challenges such as discretization and elitism by modifying the original operations and using a non-dominated sorting approach. The proposed algorithm is independent of the application, and with customization, it could be employed to solve any feature selection problem. We evaluated the algorithm's efficiency using it as a feature selection system for diagnosing cardiac arrhythmias. The Pareto optimal selected features from NSICA were utilized to classify arrhythmias in binary and multi-class forms based on three essential objectives: accuracy, number of features, and false negativity. We applied NSICA to an ECG-based arrhythmia classification dataset from the UCI machine learning repository. The evaluation results indicate the efficiency of the proposed algorithm compared to other state-of-the-art algorithms.

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

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.

ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems

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.

A Review on the Applications of Time-Frequency Methods in ECG Analysis

The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.

A fully-automated paper ECG digitisation algorithm using deep learning

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60-70% and the average correlation of 3-by-1 ECGs achieved 80-90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.

A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification

Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training. The subband of SWT is input into the multiscale 1D-CNN separately. The output fused with RR interval features are fed to the fully connected layer for classification. To achieve the lightweight network while maintaining the powerful inference capability of the multibranch structure, the redundant branches of the network are removed by reparameterization. Experimental results and analysis show that it outperforms existing methods by many in arrhythmic heartbeat classification.

A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform

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.

Classification of ECG signal using FFT based improved Alexnet classifier

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.

A 746 nW ECG Processor ASIC Based on Ternary Neural Network

This paper presents an ultra-low power electrocardiography (ECG) processor application-specific integrated circuit (ASIC) for the real-time detection of abnormal cardiac rhythms (ACRs). The proposed ECG processor can support wearable or implantable ECG devices for long-term health monitoring. It adopts a derivative-based patient adaptive threshold approach to detect the R peaks in the PQRST complex of ECG signals. Two tiny machine learning classifiers are used for the accurate classification of ACRs. A 3-layer feed-forward ternary neural network (TNN) is designed, which classifies the QRS complex's shape, followed by the adaptive decision logics (DL). The proposed processor requires only 1 KB on-chip memory to store the parameters and ECG data required by the classifiers. The ECG processor has been implemented based on fully-customized near-threshold logic cells using thick-gate transistors in 65-nm CMOS technology. The ASIC core occupies a die area of 1.08 mm². The measured total power consumption is 746 nW, with 0.8 V power supply at 2.5 kHz real-time operating clock. It can detect 13 abnormal cardiac rhythms with a sensitivity and specificity of 99.10% and 99.5%. The number of detectable ACR types far exceeds the other low power designs in the literature.

Data pre-processing for cardiovascular disease classification: A systematic literature review

The important task in the medical field is the early detection of disease. Heart disease is one of the greatest challenging diseases in all other diseases subsequently 17.3 million people died once a year due to heart disease. A minute error in heart disease diagnosis is a risk for an individual lifespan. Precise heart disease diagnosis is consequently critical. Different approaches including data mining have been used for the prediction of heart disease. However, there are some solemn concerns related to the data quality for example inconsistencies, missing values, noise, high dimensionality, and imbalanced statistics. In order to improve the accuracy of Data Mining based prediction systems, techniques for data preparation were applied to increase the quality of the data. The foremost objective of this paper is to highlight and summarize the research work about (i) data preparation techniques mostly used, (ii) the impact of pre-processing procedures on the accuracy of a heart disease prediction system, (iii) classifier enactment with data pre-processing techniques, (4) comparison in terms of accuracy of the different pre-processing model. A systematic literature review on the use of data pre-processing in heart disease diagnosis is carried out from January 2001 to July 2021 by studying the published material. Almost 30 studies were designated and examined related to the above-mentioned benchmarks. The literature review concludes that data reduction and data cleaning pre-processing techniques are mostly used in heart disease prediction systems. Overall this study concludes that data pre-processing has improved the accuracy of models used for heart disease prediction. Some hybrid models including (ANN+CHI), (ANN+PCA), (DNN+CHI) and (SVM+PCA) have shown improved accuracy level. However, due to the lack of clarification, there is a number of limitations and challenges in order to implementing these models in the real world.

Real-time internet of medical things framework for early detection of Covid-19

The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

Electrocardiogram Signal Classification Based on Mix Time-Series Imaging

Arrhythmia is a significant cause of death, and it is essential to analyze the electrocardiogram (ECG) signals as this is usually used to diagnose arrhythmia. However, the traditional time series classification methods based on ECG ignore the nonlinearity, temporality, or other characteristics inside these signals. This paper proposes an electrocardiogram classification method that encodes one-dimensional ECG signals into the three-channel images, named ECG classification based on Mix Time-series Imaging (EC-MTSI). Specifically, this hybrid transformation method combines Gramian angular field (GAF), recurrent plot (RP), and tiling, preserving the original ECG time series’ time dependence and correlation. We use a variety of neural networks to extract features and perform feature fusion and classification. This retains sufficient details while emphasizing local information. To demonstrate the effectiveness of the EC-MTSI, we conduct abundant experiments in a commonly-used dataset. In our experiments, the general accuracy reached 93.23%, and the accuracy of identifying high-risk arrhythmias of ventricular beats and supraventricular beats alone are as high as 97.4% and 96.3%, respectively. The results reveal that the proposed method significantly outperforms the existing approaches.

DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification

In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity.

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.

CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive

(1) Background and objective: Cardiovascular disease is one of the most common causes of death in today’s world. ECG is crucial in the early detection and prevention of cardiovascular disease. In this study, an improved deep learning method is proposed to diagnose abnormal and normal ECG accurately. (2) Methods: This paper proposes a CNN-FWS that combines three convolutional neural networks (CNN) and recursive feature elimination based on feature weights (FW-RFE), which diagnoses abnormal and normal ECG. F1 score and Recall are used to evaluate the performance. (3) Results: A total of 17,259 records were used in this study, which validated the diagnostic performance of CNN-FWS for normal and abnormal ECG signals in 12 leads. The experimental results show that the F1 score of CNN-FWS is 0.902, and the Recall of CNN-FWS is 0.889. (4) Conclusion: CNN-FWS absorbs the advantages of convolutional neural networks (CNN) to obtain three parts of different spatial information and enrich the learned features. CNN-FWS can select the most relevant features while eliminating unrelated and redundant features by FW-RFE, making the residual features more representative and effective. The method is an end-to-end modeling approach that enables an adaptive feature selection process without human intervention.

Lightweight Ensemble Network for Detecting Heart Disease Using ECG Signals

Heart disease should be treated quickly when symptoms appear. Machine-learning methods for detecting heart disease require desktop computers, an obstacle that can have fatal consequences for patients who must check their health periodically. Herein, we propose a MobileNet-based ensemble algorithm for arrhythmia diagnosis that can be easily and quickly operated in a mobile environment. The electrocardiogram (ECG) signal measured over a short period of time was augmented using the matching pursuit algorithm to achieve a high accuracy. The arrhythmia data were classified through an ensemble classifier combining MobileNetV2 and BiLSTM. By classifying the data using this algorithm, an accuracy of 91.7% was achieved. The performance of the algorithm was evaluated using a confusion matrix and a receiver operating characteristic curve. The sensitivity, specificity, precision, and F1 score were 0.92, 0.91, 0.92, and 0.92, respectively. Because the proposed algorithm does not require long-term ECG signal measurement, it facilitates health management for busy people. Moreover, parameters are exchanged when learning data, enhancing the security of the system. In addition, owing to the lightweight deep-learning model, the proposed algorithm can be applied to mobile healthcare, object detection, text recognition, and authentication.

Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.

Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network

BACKGROUND AND OBJECTIVE

In the application of wearable heart-monitors, it is of great significance to analyze electrocardiogram (ECG) signals for anomaly detection. ECG arrhythmia classification remains an open problem in that it cannot easily recognize data from minority classes due to the imbalanced dataset and particular characteristic of the time series signal. In this study, a novel method is presented as a possible solution to imbalanced classification problems.

METHODS

An improved data augmentation method based on variational auto-encoder (VAE) and auxiliary classifier generative adversarial network (ACGAN) is implemented to address the difficulties resulting from the imbalanced dataset. Based on the augmented dataset, convolutional neural network (CNN) classifiers are employed to automatically recognize arrhythmias using two-dimensional ECG images.

RESULTS

In experimental studies conducted with the MIT-BIH arrhythmia database, the proposed method achieves 98.45% accuracy and 97.03% sensitivity. The sensitivities of two minority classes achieve 95.83% and 97.37%, respectively.

CONCLUSION

In imbalanced classification, the sensitivity of minority class is a key evaluation indicator. One of the significant contributions of this study is that the proposed method can obtain higher sensitivity of minority class. The experimental results demonstrate that the proposed method for ECG arrhythmia calssification under imbalanced data has better performance compared with traditional cropping augmentation methods and traditional classifiers.

Meniscal Tear and ACL Injury Detection Model Based on AlexNet and Iterative ReliefF

Magnetic resonance (MR) is one of the special imaging techniques used to diagnose orthopedics and traumatology. In this study, a new method has been proposed to detect highly accurate automatic meniscal tear and anterior cruciate ligament (ACL) injuries. In this study, images in three different slices were collected. These are the sagittal, coronal, and axial slices, respectively. Images taken from each slice were categorized in 3 different ways: sagittal database (sDB), coronal database (cDB), and axial database (aDB). The proposed model in the study uses deep feature extraction. In this context, deep features have been obtained by using fully-connected layers of AlexNet architecture. In the second stage of the study, the most significant features were selected using the iterative RelifF (IRF) algorithm. In the last step of the application, the features are classified by using the k-nearest neighbor (kNN) method. Three datasets were used in the study. These datasets, sDB, and cDB, have four classes and consist of 442 and 457 images, respectively. The aDB used in the study has two class labels and consists of 190 images. The model proposed within the scope of the study was applied in 3 datasets. In this context, 98.42%, 100%, and 100% accuracy values were obtained for sDB, cDB, and aDB datasets, respectively. The study results showed that the proposed method detected meniscal tear and anterior cruciate ligament (ACL) injuries with high accuracy.

Arrhythmia detection and classification using ECG and PPG techniques: a review

Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.

Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction

Background

Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).

Methods

A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git). The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set.

Results

Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI.

Conclusions

We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research.

Trial registration Clinical Trial Registry No.: ChiCTR2100041960.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01667-8.

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.

PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture

Background and objective: Arrhythmia is a widely seen cardiologic ailment worldwide, and is diagnosed using electrocardiogram (ECG) signals. The ECG signals can be translated manually by human experts, but can also be scheduled to be carried out automatically by some agents. To easily diagnose arrhythmia, an intelligent assistant can be used. Machine learning-based automatic arrhythmia detection models have been proposed to create an intelligent assistant. Materials and Methods: In this work, we have used an ECG dataset. This dataset contains 1000 ECG signals with 17 categories. A new hand-modeled learning network is developed on this dataset, and this model uses a 3D shape (prismatoid) to create textural features. Moreover, a tunable Q wavelet transform with low oscillatory parameters and a statistical feature extractor has been applied to extract features at both low and high levels. The suggested prismatoid pattern and statistical feature extractor create features from 53 sub-bands. A neighborhood component analysis has been used to choose the most discriminative features. Two classifiers, k nearest neighbor (kNN) and support vector machine (SVM), were used to classify the selected top features with 10-fold cross-validation. Results: The calculated best accuracy rate of the proposed model is equal to 97.30% using the SVM classifier. Conclusion: The computed results clearly indicate the success of the proposed prismatoid pattern-based model.

ResNet‐Attention model for human authentication using ECG signals

Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end‐to‐end deep neural network models for ECG‐based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet‐Attention is designed for human authentication. We have used 2‐s duration ECG signals obtained from two ECG databases (Physikalisch‐Technische Bundesanstalt [PTB] and Check Your Bio‐signals Here initiative [CYBHi]) for authentication. Our proposed ResNet‐Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real‐time authentication systems after the validation with more diverse ECG data.