Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor

Objective.We present a concept for processing 6-lead electrocardiography (ECG) signals which can be applied to various use cases in quantitative electrocardiography.Approach.Our work builds upon the mathematics of the well-known Cabrera sequence which is a re-sorting of the six limb leads (I,II,III,aVR,aVL,aVF) into a clockwise and physiologically-interpretable order. By deriving correction factors for harmonizing lead strengths and choosing an appropriate basis for the leads, we extend this concept towards what we call the 'Cabrera Circle' based on a mathematically sound foundation.Main results.To demonstrate the practical effectiveness and relevance of this concept, we analyze its suitability for deriving interpolated leads between the six limb leads and a 'radial' lead which both can be useful for specific use cases. We focus on the use cases of i) determination of the electrical heart axis by proposing a novel interactive tool for reconstructing the heart's vector loop and ii) improving accuracy in time of automatic R-wave detection and T-wave delineation in 6-lead ECG. For the first use case, we derive an equation which allows projections of the 2-dimensional vector loops to arbitrary angles of the Cabrera Circle. For the second use case, we apply several state-of-the-art algorithms to a freely-available 12-lead dataset (Lobachevsky University Database). Out-of-the-box results show that the derived radial lead outperforms the other limb leads (I,II,III,aVR,aVL,aVF) by improving F1 scores of R-peak and T-peak detection by 0.61 and 2.12, respectively. Results of on- and offset computations are also improved but on a smaller scale.Significance.In summary, the Cabrera Circle offers a methodology that might be useful for quantitative electrocardiography of the 6-lead subsystem-especially in the digital age.

3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads

Objective.The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the PhysioNet/Computing in Cardiology Challenge 2021. The training set is a public database of 88,253 twelve-lead ECG recordings lasting from 6 s to 60 s. Each ECG recording has one or more diagnostic labels. The six-lead, four-lead, three-lead, and two-lead are reduced-lead versions of the original twelve-lead data.Approach.The deep learning method considers images that are built from raw ECG signals. This technique considers innovative 3D display of the entire ECG signal, observing the regional constraints of the leads, obtaining time-spatial images of the 12 leads, where the x-axis is the temporal evolution of ECG signal, the y-axis is the spatial location of the leads, and the z-axis (color) the amplitude. These images are used for training Convolutional Neural Networks with GoogleNet for ECG diagnostic classification.Main results.The official results of the classification accuracy of our team named 'Gio_new_img' received scores of 0.4, 0.4, 0.39, 0.4 and 0.4 (ranked 18th, 18th, 18th,18th, 18th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set with the Challenge evaluation metric.Significance.The results indicated that all these algorithms have similar behaviour in the various lead groups, and the most surprising and interesting point is the fact that the 2-lead scores are similar to those obtained with the analysis of 12 leads. It permitted to test the diagnostic potential of the reduced-lead ECG recordings. These aspects can be related to the pattern recognition capacity and generalizability of the deep learning approach and/or to the fact that the characteristics of the considered cardiac abnormalities can be extracted also from a reduced set of leads.

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Objective. The availability of online electrocardiogram (ECG) repositories can aid researchers in developing automated cardiac abnormality diagnostic systems. Using such ECG repositories, this study aims to develop an algorithm that can assist physicians in diagnosing cardiac abnormalities.Approach. The PhysioNet/CinC 2021 Challenge has opened the venues for creating benchmark algorithms using standard and relatively diverse 12-lead ECG datasets. This work attempts to create a new machine learning approach for identifying common cardiac abnormalities using an ensemble-based classification with two models resulting from two different feature sets. The first feature set extracts RR variability based information by deploying Fourier-Bessel (FB) expansion. The second feature set is composed of time- and frequency-domains-based hand-crafted features. Two long short-term memory (LSTM)-based classifiers are trained using these two feature sets as input to categorize ECG signals. Predictions from these two models are fused to arrive at a final medical decision that improves the multi-label classification of the given ECG signals into twenty-six categories.Main results. We participated in the George B. Moody Physionet Challenge 2021 as team 'Medics', and the proposed methodology was evaluated for all five lead combinations. The challenge scoring metrics obtained on the test data for twelve-, six-, four-, three-, and two-leads combinations are 0.360, 0.368, 0.376, 0.323, and 0.381, respectively. The proposed methodology was ranked 11th among all the follow-up entries of the Challenge.Significance. The obtained results of the proposed method justify the use of an ensemble classifier developed using the extracted feature sets for devising a diagnostic system for detecting and identifying common cardiac problems.

Clinical validation of a 13-lead electrocardiogram derived from a self-applicable 3-lead recording for diagnosis of myocardial supply ischaemia and common non-ischaemic electrocardiogram abnormalities at rest

Aims

In this study, we compare the diagnostic accuracy of a standard 12-lead electrocardiogram (ECG) with a novel 13-lead ECG derived from a self-applicable 3-lead ECG recorded with the right exploratory left foot (RELF) device. The 13th lead is a novel age and sex orthonormalized computed ST (ASO-ST) lead to increase the sensitivity for detecting ischaemia during acute coronary artery occlusion.

Methods and results

A database of simultaneously recorded 12-lead ECGs and RELF recordings from 110 patients undergoing coronary angioplasty and 30 healthy subjects was used. Five cardiologists scored the learning data set and five other cardiologists scored the validation data set. In addition, the presence of non-ischaemic ECG abnormalities was compared. The accuracy for detection of myocardial supply ischaemia with the derived 12 leads was comparable with that of the standard 12-lead ECG (P = 0.126). By adding the ASO-ST lead, the accuracy increased to 77.4% [95% confidence interval (CI): 72.4-82.3; P < 0.001], which was attributed to a higher sensitivity of 81.9% (95% CI: 74.8-89.1) for the RELF 13-lead ECG compared with a sensitivity of 76.8% (95% CI: 71.9-81.7; P < 0.001) for the 12-lead ECG. There was no significant difference in the diagnosis of non-ischaemic ECG abnormalities, except for Q-waves that were more frequently detected on the standard ECG compared with the derived ECG (25.9 vs. 13.8%; P < 0.001).

Conclusion

A self-applicable and easy-to-use 3-lead RELF device can compute a 12-lead ECG plus an ischaemia-specific 13th lead that is, compared with the standard 12-lead ECG, more accurate for the visual diagnosis of myocardial supply ischaemia by cardiologists.

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775–0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830–0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829–0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

Issues in the automated classification of multilead ecgs using heterogeneous labels and populations

Objective.The standard twelve-lead electrocardiogram (ECG) is a widely used tool for monitoring cardiac function and diagnosing cardiac disorders. The development of smaller, lower-cost, and easier-to-use ECG devices may improve access to cardiac care in lower-resource environments, but the diagnostic potential of these devices is unclear. This work explores these issues through a public competition: the 2021 PhysioNet Challenge. In addition, we explore the potential for performance boosting through a meta-learning approach.Approach.We sourced 131,149 twelve-lead ECG recordings from ten international sources. We posted 88,253 annotated recordings as public training data and withheld the remaining recordings as hidden validation and test data. We challenged teams to submit containerized, open-source algorithms for diagnosing cardiac abnormalities using various ECG lead combinations, including the code for training their algorithms. We designed and scored the algorithms using an evaluation metric that captures the risks of different misdiagnoses for 30 conditions. After the Challenge, we implemented a semi-consensus voting model on all working algorithms.Main results.A total of 68 teams submitted 1,056 algorithms during the Challenge, providing a variety of automated approaches from both academia and industry. The performance differences across the different lead combinations were smaller than the performance differences across the different test databases, showing that generalizability posed a larger challenge to the algorithms than the choice of ECG leads. A voting model improved performance by 3.5%.Significance.The use of different ECG lead combinations allowed us to assess the diagnostic potential of reduced-lead ECG recordings, and the use of different data sources allowed us to assess the generalizability of the algorithms to diverse institutions and populations. The submission of working, open-source code for both training and testing and the use of a novel evaluation metric improved the reproducibility, generalizability, and applicability of the research conducted during the Challenge.

Abnormality classification from electrocardiograms with various lead combinations

As cardiovascular diseases have been one of the leading causes of death, early and accurate diagnosis of cardiac abnormalities with less cost becomes particularly important. Given the electrocardiogram (ECG) datasets from multiple sources, there exist many challenges to develop the generalized models that can identify multiple types of cardiac abnormalities from both 12-lead ECG signals and reduced-lead ECG signals. In this study, our objective is to build robust models which can accurately classify 30 types of abnormalities from various lead combinations of ECG signals. Given the challenges of this problem, we proposed a framework for building robust models for ECG signal classification. Firstly, a pre-processing workflow was adopted for each ECG dataset to mitigate the problem of data divergence. Secondly, to capture the lead-wise relations, we used a squeeze-and-excitation deep residual network (SE_ResNet) as our base model. Thirdly, we proposed the cross relabeling strategy and applied the sign-augmented loss function to tackle the corrupted labels in the data. Furthermore, we utilized a pos-if-any-pos ensemble strategy and a dataset-wise cross evaluation strategy to handle the uncertainty of the data distribution in the application. In the Physionet/Computing in Cardiology Challenge 2021, our approach achieved the challenge metric scores of 0.57, 0.59, 0.59, 0.58, 0.57 on 12, 6, 4, 3, 2 lead versions and an averaged challenge metric score of 0.58 over all the lead versions.Using the proposed framework, we developed the models from several large datasets with sufficiently labeled abnormalities. Our models could identify 30 ECG abnormalities accurately based on various lead combinations of ECG signals. The performance on hidden test data demonstrated the effectiveness of the proposed approaches.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

Does T wave inversion in lead aVL predict mid-segment left anterior descending lesions in acute coronary syndrome? A retrospective study

OBJECTIVES

Limited data are available regarding the predictive value of electrocardiographic T wave inversion in lead aVL for mid-segment left anterior descending (MLAD) lesions among patients with acute coronary syndrome (ACS).

SETTING

Retrospective single-centre study, using a prospectively-collected coronary angiography database from January 2012 to December 2013.

PARTICIPANTS

We included consecutive adult patients with ACS who underwent urgent percutaneous coronary intervention (PCI) within 24 h after arriving at the hospital. We excluded patients who did not undergo an ECG before PCI, patients with proximal MLAD occlusion and patients diagnosed with vasospastic angina.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was MLAD lesion >50%. The other outcome of interest was MLAD lesion as a cause of ACS. First, we evaluated the diagnostic values of T wave inversion in lead aVL regardless of other T wave changes for each outcome. Second, we evaluated the diagnostic values of isolated T wave inversion in lead aVL.

RESULTS

Overall, 219 patients were eligible for the analysis. T wave inversion in lead aVL regardless of other T wave changes had a sensitivity of 32.9%, specificity of 48.2%, positive predictive value of 27.6% and negative predictive value of 54.5% for predicting MLAD lesions. Isolated T wave inversion in lead aVL had a sensitivity of 9.8%, specificity of 86.9%, positive predictive value of 30.8% and negative predictive value of 61.7% for predicting MLAD lesions. These diagnostic values did not change materially when focusing on patients with MLAD lesion as the cause.

CONCLUSIONS

While T wave inversion in lead aVL regardless of other T wave changes had low diagnostic values for predicting MLAD lesions, isolated T wave inversion in lead aVL had a high specificity. Our inferences underscore the importance of a cautious interpretation of T wave inversion in lead aVL among patients with ACS.