Automated Electrocardiogram Analysis Research Articles

Evaluating the feasibility of upper arm ECG for cardiac monitoring

Abstract Background Ambulatory electrocardiogram (ECG) monitoring is essential for detecting paroxysmal cardiac events and tracking long-term physiological parameters. However, traditional Holter devices are cumbersome, restrict movement, and can cause skin irritation, resulting in sub-optimal patient compliance during extended monitoring periods. Wearable devices utilizing non-conventional form factors are emerging as promising non-intrusive alternatives for ECG monitoring. Purpose To determine the optimal location for single-lead ECG acquisition on the upper arm and assess the feasibility of using dry electrode armbands for cardiac monitoring when coupled with robust ECG processing software. Methods Stationary ECGs from 20 healthy adult participants were recorded across a two-phase data collection protocol and processed with clinically validated ECG software. In the first phase, the average signal amplitudes of wet-electrode ECGs obtained from 30 different configurations across the mid-brachial and proximal-brachial regions of the upper arm were assessed. In the second, the performance of two proprietary dry-electrode armbands, featuring either a single-point or three-point electrode strap, were evaluated on the optimal upper arm configuration using QRS detection and heart rate (HR) metrics. Comparative analysis was performed throughout using data simultaneously acquired on a gold standard Holter device in modified lead III (MLIII) configuration. Performance metrics were calculated relative to manually generated signal annotations. Results Wet-electrode-acquired ECGs from the proximal-brachial region exhibited significantly higher average signal amplitudes (0.12 ± 0.06 mV) compared to those from the mid-brachial region (0.05 ± 0.04 mV) of the upper arm (p < 0.05). The optimal configuration (A-F), positioned at the lateral and medial extents of the proximal-brachial region, demonstrated comparable average signal amplitudes (0.16 ± 0.07 mV, 0.16 ± 0.06 mV, 0.19 ± 0.08 mV) for single-point dry, three-point dry, and wet-electrode setups, respectively. Upon processing with ECG software, data from optimal dry-electrode configurations showed excellent average QRS sensitivity and specificity of 98.2% and 98.9% for the one-point strap, and 98.9% and 99.8% for the three-point strap, respectively. HR metrics from the optimized dry-electrode setups were comparable in accuracy to those derived from the reference Holter device, with the average HR error being similar across the three-point dry electrode strap (0.57% ± 0.28%), one-point dry electrode strap (1.16% ± 1.46%), and Holter device (0.74% ± 1.50%). Conclusion(s) Optimized upper arm single-lead ECG with dry electrodes can yield signals with sufficient amplitude and quality for automated ECG analysis. Coupled with robust ECG signal processing software, the cardiac metrics obtained from the optimized upper arm setups exhibited comparable performance to those derived from medical Holter data.

Bootstrap each lead’s latent: A novel method for self-supervised learning of multilead electrocardiograms

Background and Objective:Electrocardiogram (ECG) is one of the most important diagnostic tools for cardiovascular diseases (CVDs). Recent studies show that deep learning models can be trained using labeled ECGs to achieve automatic detection of CVDs, assisting cardiologists in diagnosis. However, the deep learning models heavily rely on labels in training, while manual labeling is costly and time-consuming. This paper proposes a new self-supervised learning (SSL) method for multilead ECGs: bootstrap each lead’s latent (BELL) to reduce the reliance and boost model performance in various tasks, especially when training data are insufficient. Method:BELL is a variant of the well-known bootstrap your own latent (BYOL). The BELL aims to learn prior knowledge from unlabeled ECGs by pretraining, benefitting downstream tasks. It leverages the characteristics of multilead ECGs. First, BELL uses the multiple-branch skeleton, which is more effective in processing multilead ECGs. Moreover, it proposes intra-lead and inter-lead mean square error (MSE) to guide pretraining, and their fusion can result in better performances. Additionally, BELL inherits the main advantage of the BYOL: No negative pair is used in pretraining, making it more efficient. Results:In most cases, BELL surpasses previous works in the experiments. More importantly, the pretraining improves model performances by 0.69% ∼ 8.89% in downstream tasks when only 10% of training data are available. Furthermore, BELL shows excellent adaptability to uncurated ECG data from a real-world hospital. Only slight performance degradation occurs (<1% in most cases) when using these data. Conclusion:The results suggest that the BELL can alleviate the reliance on manual ECG labels from cardiologists, a critical bottleneck of the current deep learning-based models. In this way, the BELL can also help deep learning extend its application on automatic ECG analysis, reducing the cardiologists’ burden in real-world diagnosis.

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.

Automated Myocardial Infarction Screening Using Morphology-Based Electrocardiogram Biomarkers.

Ischemic heart disease (IHD), a critical and dreadful cardiovascular disease, is a leading cause of death globally. The steady progress of IHD leads to an irreversible condition called myocardial infarction (MI). The detection of MI can be done by observing the altered electrocardiogram (ECG) characteristics. Often, automated ECG analysis is preferred in place of visual inspection to reduce time and ensure reliable detection even when the recording quality is not very good. This paper presents an automated approach to classify recent MI, past MI, and normal sinus rhythm (NSR) classes based on the morphological features of the ECG. In clinical practice, a standard 12-lead ECG setup is typically employed to identify MI. However, acquiring a 12-lead ECG is not always convenient. Hence, in this study, we have explored the possibility of using a minimal number of ECG leads by deriving the augmented limb leads using leads I and II. A well-known and widely used ensemble machine learning tool, the random forest (RF) classifier is trained using features extracted from the derived augmented limb leads and their combinations. An RF classifier built using features extracted from all limb leads has outperformed classifiers built on combinations of them with five-fold cross-validation training accuracy of 97.9 (±0.008) % and testing accuracy of 98 %.Clinical relevance- As high sensitivity is reported in identifying recent MI and past MI classes, the proposed approach is suitable for preventative healthcare applications since it is less likely that subjects with recent or past MI will be misclassified. Due to its low computational complexity, better interpretability, and comparable performance to the state-of-the-art results, the proposed approach can be employed in clinical and cardiac health screening applications. It also has the potential to be employed in remote monitoring with mobile and wearable devices because it is built on features extracted from only lead I and II ECG recordings.

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG.

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances in wearable ECG technology, however, raise the possibility of automated out-of-hospital QT tracking. Applications of Deep Learning (DL) - a subfield within Machine Learning - in ECG analysis holds the promise of automation for a variety of classification and regression tasks. In this work, we propose a residual neural network, QTNet, for the regression of QT intervals from a single lead (Lead-I) ECG. QTNet is trained in a supervised manner on a large ECG dataset from a U.S. hospital. We demonstrate the robustness and generalizability of QTNet on four test-sets; one from the same hospital, one from another U.S. hospital, and two public datasets. Over all four datasets, the mean absolute error (MAE) in the estimated QT interval ranges between 9ms and 15.8ms. Pearson correlation coefficients vary between 0.899 and 0.914. By contrast, QT interval estimation on these datasets with a standard method for automated ECG analysis (NeuroKit2) yields MAEs between 22.29ms and 90.79ms, and Pearson correlation coefficients 0.345 and 0.620. These results demonstrate the utility of QTNet across distinct datasets and patient populations, thereby highlighting the potential utility of DL models for ubiquitous QT tracking.Clinical Relevance- QTNet can be applied to inpatient or ambulatory Lead-I ECG signals to track QT intervals. The method facilitates ambulatory monitoring of patients at risk of QT prolongation.

A Comprehensive Comparison of Six Publicly Available Algorithms for Localization of QRS Complex on Electrocardiograph.

The QRS complex is the most prominent feature of the electrocardiogram (ECG) that is used as a marker to identify the cardiac cycles. Identification of QRS complex locations enables arrhythmia detection and heart rate variability estimation. Therefore, accurate and consistent localization of the QRS complex is an important component of automated ECG analysis which is necessary for the early detection of cardiovascular diseases. This study evaluates the performance of six popular publicly available QRS complex detection methods on a large dataset of over half a million ECGs in a diverse population of patients. We found that a deep-learning method that won first place in the 2019 Chinese physiological challenge (CPSC-1) outperforms the remaining five QRS complex detection methods with an F1 score of 98.8% and an absolute sdRR error of 5.5 ms. We also examined the stratified performance of the studied methods on various cardiac conditions. All six methods had a lower performance in the detection of QRS complexes in ECG signals of patients with pacemakers, complete atrioventricular block, or indeterminate cardiac axis. We also concluded that, in the presence of different cardiac conditions, CPSC-1 is more robust than Pan-Tompkins which is the most popular model for QRS complex detection. We expect that this study can potentially serve as a guide for researchers on the appropriate QRS detection method for their target applications.Clinical Relevance-This study highlights the overall performance of publicly available QRS detection algorithms in a large dataset of diverse patients. We showed that there are specific cardiac conditions that are associated with the poor performance of QRS detection algorithms and may adversely influence the performance of algorithms that rely on accurate and reliable QRS detection.

Ensemble classifier fostered detection of arrhythmia using ECG data.

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).

An automated patient-specific ECG beat classification using LSTM-based recurrent neural networks

Customized electrocardiogram analysis is inevitable in the cardiac functionality assessment of every single individual patient. In this paper, we firmly introduce a novel framework for patient-specific electrocardiogram (ECG) beat classification characterized by the long-short term memory (LSTM) based recurrent neural networks (RNNs). A novel architecture is developed by governing two LSTM models connected in parallel, and each of them operates on the single-channel ECG input. The proposed model customizes the classification process and imbibes the trait of patient-specific analysis by training the model with the first 5-min segment of heartbeats of the respective patient’s ECG record and tests the same for the rest of the ECG record. The proposed framework undergoes five stages viz; pre-processing, QRS complex detection, segmentation, feature extraction, and LSTM-based RNNs for classification. The efficiency of the proposed framework is deeply examined by the support of various metrics viz; accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictivity (Ppr), F1 score, and G score and the performance of the proposed method is compared to that of some popular state-of-the-art techniques. The proposed framework is exposed to a highly popular and reliable MIT-BIH arrhythmia database for training and testing purposes. The obtained results are found promising and yield superior classification performance in recognizing ventricular ectopic beats (VEB) and supra-ventricular ectopic beats (SVEB). In addition, the proposed framework is recognized as simple, lightweight yet efficient which in turn makes it suitable for continuous monitoring applications in clinical settings for automated ECG analysis to assist the cardiologist.

Spatiotemporal self-supervised representation learning from multi-lead ECG signals

Automatic analysis of electrocardiogram(ECG) signals is one of the applications in the medical domain where deep learning methods demonstrate impressive performance. However, label scarcity remains a critical obstacle limiting the development and clinical application of deep learning methods. Self-supervised pretraining exhibits its potential to alleviate this issue. Motivated by the idea that multi-lead ECG signals gathered simultaneously represent similar underlying cardiac status, we propose ECG-MAE, a novel generative self-supervised pretraining approach. Specifically, an efficient encoder is designed to learn spatiotemporal representations by reconstructing 12-lead ECG signals that have been randomly masked in both time and lead dimensions. Experiments on a multi-label classification problem reveal that our pretrained model obtains a macro-averaged AUC of 0.9474, exceeding prior studies. We observe enhanced downstream performance and label efficiency compared with purely supervised models, allowing us to train more powerful models with the same amount of data and diagnose rare diseases beyond the reach of conventional methods.

Corrected QT interval in electrocardiogram recordings in patients treated with calcium channel blockers.

One of the most popular tests of the heart is the electrocardiogram (ECG). The physical basis of this study has been known for over 200 years. However, the way an ECG is performed and the interpretation of the obtained results have undergone considerable evolution over time. To analyze the ECG recordings regarding the QTc interval in patients taking calcium channel blockers. The publicly available PhysioNet signals database was used to analyze the effect of selected drugs on the heart rhythm and QTc interval. To automate the QTc assessment, the database was processed in MATLAB. First, QTc was assessed using four formulas; then the results were compared in terms of methodology and medications taken. In the group of patients taking dihydropyridine blockers, the QTc interval assessed by the Bazett formula (mean: 478.87 ms; SD: 73.10) is longer than in patients taking non-dihydropyridine blockers (mean: 446.54 ms, SD: 88.07, p = 0.2123) and in patients not taking calcium channel blockers (mean: 436.33 ms, SD: 49.94, p = 0.0319). The same significant differences were obtained regardless of the QTc assessment method used. However, the QTc result obtained with the Bazett formula is significantly lower than in the case of using the other three formulas used for the presented analysis. Automatic ECG analysis is possible; however, it should be used carefully, considering the possibility of obtaining incorrect results. Taking calcium channel blockers may affect the QTc interval. QTc results significantly depend on the formula based on which we evaluate this parameter.

Normal and Abnormal Classification of Electrocardiogram: A Primary Screening Tool Kit.

Cardiovascular diseases (CVDs) are one of the principal causes of death. Cardiac arrhythmia, a critical CVD, can be easily detected from an electrocardiogram (ECG) recording. Automated ECG analysis can help clinicians to identify arrhythmia and prevent untimely death. This paper presents a simple model to classify the ECG recordings into two classes: Normal and Abnormal based on morphological and heart rate variability (HRV) features. Before feature extraction, Signal quality analysis (SQA) is performed to abandon poor quality ECG signals. Several machine-learning classifiers such as Support Vector Machine (SVM), Adaboost (AB), Random Forest (RF), Extra-Tree Classifier (ET), Decision Tree (DT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Gradient Boosting (GB) are explored on the extracted feature space. To enhance the study, few feature selection algorithms such as F test, Least Absolute Shrinkage and Selection Operator (LASSO), and Minimal Redundancy Maximal Relevance (mRMR) algorithms are also applied and the outcomes of each algorithm along with the considered classifiers are analyzed and compared. The proposed algorithm is validated on 2648 Normal and 2518 Abnormal ECG recordings. The accuracy of our best classifier is found to be 95.25 %. It is anticipated that the proposed model will be helpful as a primary and mass screening tool kit in clinical settings.

Automatic ECG analysis system with hybrid optimization algorithm based feature selection and classifier

Cardio vascular disease threatens human life with higher mortality rate. Therefore it is quite important to monitor. An arrhythmia is an abnormal heart beat and rhythm which causes the disease. The best tool to find the heart rhythm of heart is Electro Cardiogram (ECG) which provides information about the different types of arrhythmias. This paper aims at proposing an automatic framework by employing multi-domain features to classify ECG signals. Proposed work uses optimum method of feature selection to improvise the efficiency of the classification process. A hybrid optimization algorithm is used for feature selection and proposed to optimize the parameters of the existing Support Vector Machine (SVM) classifier. Proposed hybrid optimization algorithm was developed using Particle Swarm Optimization (PSO) and Migration Modified Biogeography Based Optimization (MMBBO) algorithm. Algorithm provides an improved solution to the optimizing the parameters of ECG signals. Results are evaluated by implementing in MATLAB software and the performance is justified with comparative analysis. The proposed framework enhances the process of automatic prediction of various arrhythmias or rhythm abnormalities which performs in gaining better accuracy. For data sets, the average classification accuracy of this method is 97.89%. This result is an improvement of 4–5% over the comparison of other methods.

Beat-Level Interpretation of Intra-Patient Paradigm Based on Object Detection.

Electrocardiogram (ECG), as a product that can most directly reflect the electrical activity of the heart, has become the most common clinical technique used for the analysis of cardiac abnormalities. However, it is a heavy and tedious burden for doctors to analyze a large amount of ECG data from the long-term monitoring system. The realization of automatic ECG analysis is of great significance. This work proposes a beat-level interpretation method based on the automatic annotation algorithm and object detector, which abandons the previous mode of separate R peak detection and heartbeat classification. The ground truth of the QRS complex is automatically annotated and also regarded as the object the model can learn like category information. The object detector unifies the localization and classification tasks, achieving an end-to-end optimization as well as decoupling the high dependence on the R peak. Compared with most advanced methods, this work shows superior performance. For the interpretation of 12 heartbeat types in the MIT-BIH dataset, the average accuracy is 99.60%, the average sensitivity is 97.56%, and the average specificity is 99.78%. This method can be used as a clinical auxiliary tool to help doctors diagnose arrhythmia after receiving large-scale database training.

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, K-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.

Remote Analysis and Transmission System of Electrocardiogram in Prehospital Setting; a Diagnostic Accuracy Study.

Introduction:One of the trends in the development of medical technologies is considered to be telemedicine. This study aimed to evaluate the accuracy of a remote electrocardiogram (ECG) analysis and transmission system in prehospital setting.Methods:In this cross-sectional study, the data of 19,265 ECGs was gathered from emergency medical service (EMS) database of Almaty city, Kazakhstan, from 2015 to 2019. All ECGs were recorded in the prehospital setting by a paramedic, using "Poly-Spectrum" ECG recording device. Subsequently, all ECGs were sent to the cardiologist for interpretation and the findings were compared between software and cardiologist.Results:19,265 ECGs were registered. The average time from taking ECGs to receiving an expert’s conclusion was 9.2 ± 2.5 minutes. The medical teams were called in 17.9% of cases after paramedic ECG recording; however, in the rest of the cases there was no need to call those teams. Using the device reduced the number of visits of specialist teams. The overall sensitivity, specificity, and accuracy of ECG analysis device in diagnosis of ECG abnormalities were 83.8% (95%CI: 82.6 – 84.9), 95.5% (95%CI: 95.1 – 95.8), and 93.3% (95%CI: 92.9 – 93.7), respectively.Conclusion:The findings of this study showed the 93.3% accuracy of automatic ECG analysis device in interpretation of ECG abnormalities in prehospital setting compared with the cardiologist interpretations. Using the device causes a decrease in the number of cardiologist visits needed as well as reduction in cost and elapsed time.

Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization

Electrocardiogram (ECG) signals contain a significant amount of subtle information that can be used to detect some types of heart dysfunction. The widespread availability of digital ECG and the algorithmic paradigm of the long short-term memory (LSTM) network present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, the number of hidden units and initial learning rate of an LSTM neural network for ECG classification are currently preset based on prior knowledge, which causes the model to reach a sub-optimal state. In this study, an automated ECG detection and classification method using a bidirectional LSTM (BiLSTM) network modified by Bayesian optimization is developed. Bayesian optimization is used to optimize the two hyperparameters of the BiLSTM network: the initial learning rate and the number of hidden layers. By classifying five ECG signals in the MIT-BIH arrhythmia database, the accuracy of the modified network reaches 99.00%, which is 0.86% higher than that before optimization. The results demonstrate that Bayesian optimization can be an effective approach to improving the quality of classifiers based on deep learning. The presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies, which may have practical applications.

Symmetric Projection Attractor Reconstruction: Sex Differences in the ECG.

Background: The electrocardiogram (ECG) is a key tool in patient management. Automated ECG analysis supports clinical decision-making, but traditional fiducial point identification discards much of the time-series data that captures the morphology of the whole waveform. Our Symmetric Projection Attractor Reconstruction (SPAR) method uses all the available data to provide a new visualization and quantification of the morphology and variability of any approximately periodic signal. We therefore applied SPAR to ECG signals to ascertain whether this more detailed investigation of ECG morphology adds clinical value.Methods: Our aim was to demonstrate the accuracy of the SPAR method in discriminating between two biologically distinct groups. As sex has been shown to influence the waveform appearance, we investigated sex differences in normal sinus rhythm ECGs. We applied the SPAR method to 9,007 10 second 12-lead ECG recordings from Physionet, which comprised; Dataset 1: 104 subjects (40% female), Dataset 2: 8,903 subjects (54% female).Results: SPAR showed clear visual differences between female and male ECGs (Dataset 1). A stacked machine learning model achieved a cross-validation sex classification accuracy of 86.3% (Dataset 2) and an unseen test accuracy of 91.3% (Dataset 1). The mid-precordial leads performed best in classification individually, but the highest overall accuracy was achieved with all 12 leads. Classification accuracy was highest for young adults and declined with older age.Conclusions: SPAR allows quantification of the morphology of the ECG without the need to identify conventional fiducial points, whilst utilizing of all the data reduces inadvertent bias. By intuitively re-visualizing signal morphology as two-dimensional images, SPAR accurately discriminated ECG sex differences in a small dataset. We extended the approach to a machine learning classification of sex for a larger dataset, and showed that the SPAR method provided a means of visualizing the similarities of subjects given the same classification. This proof-of-concept study therefore provided an implementation of SPAR using existing data and showed that subtle differences in the ECG can be amplified by the attractor. SPAR's supplementary analysis of ECG morphology may enhance conventional automated analysis in clinically important datasets, and improve patient stratification and risk management.

Application of Pre-Trained Deep Learning Models for Clinical ECGs.

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting.

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

The cardiac surgeon's guide to artificial intelligence.

Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.