Electrocardiogram Database Research Articles

121 year after Einthoven: show me the normal ECG?

Abstract Background The clinical electrocardiogram (ECG) is measured about 120 years. The interpretations of the ECG is an art mastered by only few. Rhythm and Interval analysis is relative reliable and well supported by the available ECG machines. However, the interpretation of the ECG waveform and where it starts to deviate from the normal waveforms is poorly defined for the clinical practice. To support the anatomical relationship of the ECG, the PathECG has been developed as part of the CineECG tool, expanding the view on the ECG. In this study normal ECG distributions were created from a public available database to enable a) visual comparison of the ‘normality’ of the ECG and PathECG and b) build a predictive model to classify an ECG as normal or abnormal. Purpose To create an objective and 3D visual comparison of an ECGs to "normal ECGs". Methods From the (Physionet) PTB-XL database (Germany) all verified ECGs with known gender were selected. From the these ECGs a median ventricular beat was automatically constructed and subsequently resampled to have a standardized number of samples (Figure: Normal ECGs on top of abnormal ECGs). These resampled ECGs were also converted to their equivalent PathECG. From the normal labeled ECGs (3124 female, 2283 male) distributions were created, representing the normal ECG for male, female or the combined group. With these distributions a percentage of ECG segments (QRS, ST, T wave) can be identified as being outside this distribution, and thus as potentially abnormal. A logistic regression model was fitted to determine the optimal outlier removal per segment for the ECG waveforms and the PathECG to classify and ECG as normal or abnormal per gender group. The performance was tested on the CPSC (Hong Kong) public available ECG database. Results The abnormal ECG data in both databases contained approximately 1/3 normal ECG, atrial and ventricular arrhythmias and 1/3 ischemia and conduction disorders. The performance (AUC) of the logistic regression model on the PTB-XL and CPSC data respectively was MALES: 87.5% - 81.5%, FEMALES: 86.7% - 83.7%, and COMBINED: 86.8%-82.9%. The visual overlay of the ECG significantly supports the identification of abnormal segments (Figure: Normal ECG distribution example). Conclusions The overlay of an ECG and PathECG on the normal distribution supports the identification and verification of abnormal ECGs. This method might increase the ability of less experienced ECG readers, like general practitioners, to identify patients that need to be transferred to a cardiologist. This method could also be used for the classification of P-waves, paediatric ECGs or ECGs in specific patient groups.Normal ECGs on top of abnormal ECGsNormal ECG distribution example

ECG data analysis to determine ST-segment elevation myocardial infarction and infarction territory type: an integrative approach of artificial intelligence and clinical guidelines.

Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm. Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI). In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease. These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.

Hybrid LSTM models-based detection of left ventricular hypertrophy in electrocardiogram signals

This research investigates various deep learning techniques to automatically classify Left Ventricular Hypertrophy (LVH) from electrocardiogram (ECG) signals. LVH frequently results from persistently high blood pressure, causing the heart pump harder and thicken the ventricular walls. It is associated with an increased risk of heart attacks, heart failure, stroke, and sudden cardiac death. The significance of this research lies in the early and precise detection of LVH, facilitating timely interventions and ultimately improving patient health. The non-invasive nature of ECG monitoring, integrated with the efficiency of deep learning models, contributes to faster and more accessible to enhance diagnostic accuracy and efficiency in identifying LVH. The objective of this research is to assess and compare the performance of GRU3Net, Double-Bilayer LSTM, and Conv2LSTM, Dual-LSTM models in the classification of Left Ventricular Hypertrophy (LVH) based on electrocardiogram (ECG) signals, utilizing a dataset sourced from the PTB Diagnostic ECG Database. The implemented deep learning models yielded noteworthy results. Specifically, the GRU3Net model achieved a high accuracy of 96.1%, showcasing an optimal configuration for overall accuracy. The Double-Bilayer LSTM model followed with an accuracy of 91.7%. However, a decline in accuracy was observed in both the Dual-LSTM and Conv2LSTM models, with the former registering an accuracy of 90.8% and the latter decreasing further to 87.3%.

A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome.

Wolff-Parkinson-White syndrome is a cardiovascular disease characterized by abnormal atrio-ventricular conduction facilitated by accessory pathways (APs). Invasive catheter ablation of the AP represents the primary treatment modality. Accurate localization of APs is crucial for successful ablation outcomes, but current diagnostic algorithms based on the 12 lead electrocardiogram (ECG) often struggle with precise determination of AP locations. In order to gain insight into the mechanisms underlying localization failures observed in current diagnostic algorithms, we employ a virtual cardiac model to elucidate the relationship between AP location and ECG morphology. We first introduce a cardiac model of electrophysiology that was specifically tailored to represent antegrade APs in the form of a short atrio-ventricular bypass tract. Locations of antegrade APs were then automatically swept across both ventricles in the virtual model to generate a synthetic ECG database consisting of 9271 signals. Regional grouping of antegrade APs revealed overarching morphological patterns originating from diverse cardiac regions. We then applied variance-based sensitivity analysis relying on polynomial chaos expansion on the ECG database to mathematically quantify how variation in AP location and timing relates to morphological variation in the 12 lead ECG. We utilized our mechanistic virtual model to showcase limitations of AP localization using standard ECG-based algorithms and provide mechanistic explanations through exemplary simulations. Our findings highlight the potential of virtual models of cardiac electrophysiology not only to deepen our understanding of the underlying mechanisms of Wolff-Parkinson-White syndrome but also to potentially enhance the diagnostic accuracy of ECG-based algorithms and facilitate personalized treatment planning.

Achieving Real-Time Prediction of Paroxysmal Atrial Fibrillation Onset by Convolutional Neural Network and Sliding Window on R-R Interval Sequences.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset. This integration enables the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, and specifically its effectiveness in making a new prediction with each new heartbeat. Catering to the potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of the automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments were collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with 100 R-R intervals was achieved, with a sensitivity of 97.12%, a specificity of 97.77%, and an accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding window step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 min in advance. The present results suggest that the sliding window technique on raw R-R interval sequences, along with deep learning-based algorithms, may offer the possibility of providing an accurate, real-time, end-to-end clinical tool for mass monitoring of PAF.

A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and synchronize image acquisition with the heart motion. ECG signals are highly distorted during MRI due to the complex electromagnetic environment. Automated ECG analysis is therefore complicated in this context and there is no reference technique in MRI to classify pathological heartbeats. Imaging arrhythmic patients is hence difficult in MRI. Deep Learning based heartbeat classifier have been suggested but require large databases whereas existing annotated sets of ECG in MRI are very small. We proposed a Siamese network to leverage a large database of unannotated ECGs outside MRI. This was used to develop an efficient representation of ECG signals, further used to develop a heartbeat classifier. We extensively tested several data augmentations and self-supervised learning (SSL) techniques and assessed the generalization of the obtained classifier to ECG signals acquired in MRI. These augmentations included random noises and a model simulating MRI specific artefacts. SSL pretraining improved the generalizability of heartbeat classifiers in MRI (F1=0.75) compared to Deep Learning not relying on SSL (F1=0.46) and another classical machine learning approach (F1=0.40). These promising results seem to indicate that the use of SSL techniques can learn efficient ECG signal representation, and are useful for the development of Deep Learning models even when only scarce annotated medical data are available.

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis

Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation. While deep learning excels in ECG classification, its interpretability remains a challenge. Addressing this issue, our research introduces an innovative approach employing image-based ECG representations and cascading deep neural networks (CDNNs) to enhance arrhythmia detection precision. Initiating with the transformation of 12-lead ECG time-series data, particularly focusing on Lead II, into image-based representations using relative positioning matrices (RPM), this novel conversion captures spatial and temporal information concurrently, enriching the depth of our data analysis. Subsequently, CDNNs play a pivotal role in feature extraction, designed to automatically extract impactful features from image-based ECG representations, providing profound insights into cardiac electrical activity. Extracted features serve as inputs for the subsequent deep neural network for classification. Experimental results on the Shaoxing–Chapman ECG database, encompassing over 10,000 recordings with an 80/10/10 split, 80/20 split, 60/40 split, and 10-fold cross-validation, showcase a remarkable 100% accuracy in distinguishing between seven and four different arrhythmia classes. Incorporating SHapley Additive exPlanations (SHAP) values, our methodology offers comprehensive insights into the model’s decision-making, ensuring interpretability. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) aids feature visualization, elucidating key features driving predictions. By amalgamating image-based representation, CDNNs, and interpretability techniques, our research not only attains exceptional classification performance but also boosts model transparency and trustworthiness. These findings hold promise for advancing arrhythmia diagnosis and enhancing interpretability in deep learning-based medical applications.

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs. The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations. A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (F1 score ≥0.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved F1 of 0.78-0.84). The presence of AF had little impact on performance. The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms

In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

Background:The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. Method:The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. Result:The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108–142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. Conclusion:The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

XECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features

Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases (n=9854\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n = 9854$$\\end{document} ECGs) and achieved an F1 score of 95.43% in AF versus non-AF classification on an unseen ECG test dataset. A systematic comparison of xAI methods showed that deep Taylor decomposition provided the most trustworthy explanations (+24%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+24\\%$$\\end{document} compared to the second-best approach). xECGArch can account for short- and long-term features corresponding to clinical features of morphology and rhythm, respectively. Further research will focus on the relationship between xECGArch features and clinical features, which may help in medical applications for diagnosis and therapy.

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

Aim of the study. This research endeavours to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilizing longer electrocardiogram (ECG) signal events based on the Apnea ECG dataset. Apnea ECG is often used to detect sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification. Used method. To extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorizing ECG signals by utilizing several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6). Brief Description of Results. The results emphasize the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognizes 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy. Findings. The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest wavelet families and machine learning can improve cardiac abnormality detection and categorization.

A multi-channel ECG signal deep compressive sensing method using Treeshaped Autoecoder based on multiscale feature fusion

Conventionally, multi-lead Electrocardiogram (ECG) signals are recorded and stored using high sensing rates and high precision, resulting in huge data volumes and placing greater pressure on storage and transmission resources. Compressive sensing (CS) allows efficient encoding and decoding of signals through sparse representation, measurement and reconstruction for compressed storage and transmission of ECG signals. Traditional CS methods generally requires manually selecting the features or dictionaries used in sparse representations, which lacks adaptivity and flexibility, and the complexity of the reconstruction process limits the application of CS in some scenarios with high real-time requirements. In this paper, we propose a deep compressive sensing framework for processing multi-lead ECG signals by combining CS and deep learning, which is based on multi-scale feature fusion to construct a binary tree-shaped autoencoder architecture to achieve efficient compression and reconstruction of ECG signals. Experiments on the PTB diagnostic ECG database show that the proposed method achieves “very good” reconstruction quality at low sensing rates, with PRD and SNR of 1.77 % and 35.67 dB, respectively, when SR = 20 %. In addition, we investigate how the number of quantization bits affects the quality of reconstructed signals. After analysis, we found that 8-bit quantization can be used in practical applications to ensure the quality of reconstructed ECG signals, while decrease the bit rate of the data, improving the transmission efficiency, and reduce energy consumption.

Empirical Wavelet Transform Based ECG Signal Filtering Method

The electrocardiogram (ECG) is a diagnostic tool that provides insights into the heart’s electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, Liouville–Weyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWT’s strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.

Analyzing ECG signals in professional football players using machine learning techniques

BackgroundFootball player's health is important, and preventing sudden cardiac arrest may be a critical issue. Professional football players have different ECG signals than the average population, yet there are considerable gaps in study whereas the general population has been extensively studied. Objectives(a) Generate a reference and innovative resting 12-lead ECG database from 54 UEFA PRO level male football players from La Liga. This is a novel approach to cope the ECG and possible arrythmias in athletes. (b) Manage each XML athlete ECG data and develop a free-use program to visualize, denoise and filter the signal with the capacity to automate the labelling of the waves and save the reports. (c) Study the ECG wave shape and generate models through ML to analyse its utility to automate basic diagnosis. MethodsThe dataset collection is based on a prospective observational cohort and includes 10 s, 12-lead ECGs and rhythm and condition labels for each athlete. Physiological sport arrhythmias, T-Wave shape and other findings were studied and labelled. ECG Visualizer was developed and used for 3 machine learning (ML) methods to automate sinus bradycardia arrhythmia diagnosis. ResultsA dataset with 163 ECGs in XML format was collected comprising the Pro Football 12-lead Resting Electrocardiogram Database (PF12RED). “ECG Visualizer” software was developed, and ML was shown to be useful in detecting sinus bradycardia. ConclusionsThe study demonstrates that AI and machine learning can detect simple arrhythmias with accuracy, also it provides a valuable dataset and a free software application.

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments.

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

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.

Classification of underlying paroxysmal supraventricular tachycardia types using deep learning of sinus rhythm electrocardiograms.

Obtaining tachycardia electrocardiograms (ECGs) in patients with paroxysmal supraventricular tachycardia (PSVT) is often challenging. Sinus rhythm ECGs are of limited predictive value for PSVT types in patients without preexcitation. This study aimed to explore the classification of atrioventricular nodal reentry tachycardia (AVNRT) and concealed atrioventricular reentry tachycardia (AVRT) using sinus rhythm ECGs through deep learning. This retrospective study included patients diagnosed with either AVNRT or concealed AVRT, validated through electrophysiological studies. A modified ResNet-34 deep learning model, pre-trained on a public ECG database, was employed to classify sinus rhythm ECGs with underlying AVNRT or concealed AVRT. Various configurations were compared using ten-fold cross-validation on the training set, and the best-performing configuration was tested on the hold-out test set. The study analyzed 833 patients with AVNRT and 346 with concealed AVRT. Among ECG features, the corrected QT intervals exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.602. The performance of the deep learning model significantly improved after pre-training, showing an AUROC of 0.726 compared to 0.668 without pre-training (p < 0.001). No significant difference was found in AUROC between 12-lead and precordial 6-lead ECGs (p = 0.265). On the test set, deep learning achieved modest performance in differentiating the two types of arrhythmias, with an AUROC of 0.708, an AUPRC of 0.875, an F1-score of 0.750, a sensitivity of 0.670, and a specificity of 0.649. The deep-learning classification of AVNRT and concealed AVRT using sinus rhythm ECGs is feasible, indicating potential for aiding in the non-invasive diagnosis of these arrhythmias.