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The electrocardiogram (ECG) signal describes the heart's electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient's clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application., Methods and Procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients' demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared., Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86-0.99 and 0.79-0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively., Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

The Fourth Universal Definition of Myocardial Infarction (FUDMI) focuses on the distinction between nonischemic myocardial injury and myocardial infarction (MI), along with the role of cardiovascular magnetic resonance, in order to define the etiology of myocardial injury. As a consequence, there is less emphasis on updating the parts of the definition concerning the electrocardiographic (ECG) changes related to MI. Evidence of myocardial ischemia is a prerequisite for the diagnosis of MI, and the ECG is the main available tool for (a) detecting acute ischemia, (b) triage, and (c) risk stratification upon presentation. This review focuses on multiple aspects of ECG interpretation that we firmly believe should be considered for incorporation in any future update to the Universal Definition of MI., (© 2020 The Authors. Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Accessory pathways are present in 1 in 300 young individuals. They are often asymptomatic and potentially lethal arrhythmias may be the first presentation. During long-term follow-up, up to 20% of asymptomatic individuals with pre-excitation go on to develop an arrhythmia and the absence of traditional clinical and electrophysiological high-risk markers does not guarantee the "safe" nature of an accessory pathway. The widespread availability of permanent cure for the condition at low risk by catheter ablation, creates an incentive to screen for accessory pathways with a 12-lead ECG, particularly in individuals who are perceived to be at increased risk, such as athletes and high-risk professions. We review the existing literature on the assessment and management of accessory pathways (Wolff-Parkinson-White [WPW] syndrome) and discuss its implications for the young athletic population., (© 2020 The Authors. Clinical Cardiology published by Wiley Periodicals, Inc.)

Timely evaluation and reperfusion have improved the myocardial salvage and the subsequent recovery rate of the patients hospitalized with acute myocardial infarction (MI). Long waiting time and time-consuming procedures of in-hospital diagnostic testing severely affect the timeliness. We present a Poincare pattern ensemble-based method with the consideration of multi-correlated non-stationary stochastic system dynamics to localize the infarct-related artery (IRA) in acute MI by fully harnessing information from paper-based Electrocardiogram (ECG). The vectorcardiogram (VCG) diagnostic features extracted from only 2.5-s long paper ECG recordings were used to hierarchically localize the IRA-not mere localization of the infarcted cardiac tissues-in acute MI. Paper ECG records and angiograms of 106 acute MI patients collected at the Heart Artery and Vein Center at Fresno California and the 12-lead ECG signals from the Physionet PTB online database were employed to validate the proposed approach. We reported the overall accuracies of 97.41% for healthy control (HC) vs. MI, 89.41 ± 9.89 for left and right culprit arteries vs. others, 88.2 ± 11.6 for left main arteries vs. right-coronary-ascending (RCA) and 93.67 ± 4.89 for left-anterior-descending (LAD) vs. left-circumflex (LCX). The IRA localization from paper ECG can be used to timely triage the patients with acute coronary syndromes to the percutaneous coronary intervention facilities.

Cardiac thromboembolism attributed to atrial fibrillation (AF) is responsible for up to one-third of ischemic strokes. Stroke may be the first manifestation of previously undetected AF. Given the efficacy of oral anticoagulants in preventing AF-related ischemic strokes, strategies of searching for AF after a stroke using ECG monitoring followed by oral anticoagulation (OAC) treatment have been proposed to prevent recurrent cardioembolic strokes. This white paper by experts from the AF-SCREEN International Collaboration summarizes existing evidence and knowledge gaps on searching for AF after a stroke by using ECG monitoring. New AF can be detected by routine plus intensive ECG monitoring in approximately one-quarter of patients with ischemic stroke. It may be causal, a bystander, or neurogenically induced by the stroke. AF after a stroke is a risk factor for thromboembolism and a strong marker for atrial myopathy. After acute ischemic stroke, patients should undergo 72 hours of electrocardiographic monitoring to detect AF. The diagnosis requires an ECG of sufficient quality for confirmation by a health professional with ECG rhythm expertise. AF detection rate is a function of monitoring duration and quality of analysis, AF episode definition, interval from stroke to monitoring commencement, and patient characteristics including old age, certain ECG alterations, and stroke type. Markers of atrial myopathy (eg, imaging, atrial ectopy, natriuretic peptides) may increase AF yield from monitoring and could be used to guide patient selection for more intensive/prolonged poststroke ECG monitoring. Atrial myopathy without detected AF is not currently sufficient to initiate OAC. The concept of embolic stroke of unknown source is not proven to identify patients who have had a stroke benefitting from empiric OAC treatment. However, some embolic stroke of unknown source subgroups (eg, advanced age, atrial enlargement) might benefit more from non-vitamin K-dependent OAC therapy than aspirin. Fulfilling embolic stroke of unknown source criteria is an indication neither for empiric non-vitamin K-dependent OAC treatment nor for withholding prolonged ECG monitoring for AF. Clinically diagnosed AF after a stroke or a transient ischemic attack is associated with significantly increased risk of recurrent stroke or systemic embolism, in particular, with additional stroke risk factors, and requires OAC rather than antiplatelet therapy. The minimum subclinical AF duration required on ECG monitoring poststroke/transient ischemic attack to recommend OAC therapy is debated.

Background: A large number of nondigitized electrocardiograph (ECG) strips are routinely collected in larger cohort studies such as the ADDITION study (Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care). These ECG strips are routinely read manually but may contain overlooked information revealing cardiac autonomic dysfunction. The aim of this study was to investigate whether clinical information may be lost using manual R wave to R wave (RR) interval measurements in the calculation of heart rate variability (HRV) in patients with type 2 diabetes mellitus (T2DM)., Method: From the Danish part of the ADDITION study, we randomly selected 120 T2DM patients at baseline of the ADDITION study. Analysis of the ECG strips was performed using two different methods: (1) by experienced technicians using rulers and (2) by experienced technicians using a high-resolution computer-assisted method. Calculation of heart rate and time domain HRV [standard deviation of normal-to-normal RR intervals (SDNN) and root mean square of successive differences (RMSSD)] were performed with the same software., Results: When comparing results from the two methods, the following values of Pearson's r are obtained: 0.98 for heart rate, 0.76 for SDNN, and 0.68 for RMSSD. These results indicate that heart rate and HRV measurements by the computer-assisted and manually based methods correlate. However, Bland-Altman plots and Pitman's test of difference in variance revealed poor agreements (p < .01) for both HRV measurements (SDNN and RMSSD); only heart rate showed substantiated agreement (p = .54) between the two methods. Low HRV was statistically significantly associated to high heart rate, systolic blood pressure, and diastolic blood pressure in these screen-detected T2DM patients., Conclusions: Paper ECG strips may contain overlooked clinical information on the status of autonomic function in patients with T2DM. In our study, manual measurements of RR intervals were inferior to the computer-assisted method. Based on this study, we recommend cautiousness in the clinical use and interpretation of HRV based on manual or low resolution measurements of RR intervals from ECG strips. High resolution measurements of RR intervals reveal significant associations between low HRV and high heart rate, systolic blood pressure, and diastolic blood pressure among patients with screen-detected T2DM. It is feasible to use a computer-assisted method to determine RR intervals in patients with T2DM., (© 2012 Diabetes Technology Society.)

The term early repolarization has been in use for more than 50 years. This electrocardiographic pattern was considered benign until 2008, when it was linked to sudden cardiac arrest due to idiopathic ventricular fibrillation. Much confusion over the definition of early repolarization followed. Thus, the objective of this paper was to prepare an agreed definition to facilitate future research in this area. The different definitions of the early repolarization pattern were reviewed to delineate the electrocardiographic measures to be used when defining this pattern. An agreed definition has been established, which requires the peak of an end-QRS notch and/or the onset of an end-QRS slur as a measure, denoted Jp, to be determined when an interpretation of early repolarization is being considered. One condition for early repolarization to be present is Jp ≥0.1 mV, while ST-segment elevation is not a required criterion., (Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Electrocardiographic (ECG) abnormalities in the setting of acute pulmonary embolism (PE) are being increasingly characterized and mounting evidence suggests that ECG plays a valuable role in prognostication for PE. We review the historical 21-point ECG prognostic score for the severity of PE and examine the updated evidence surrounding the utility of ECG abnormalities in prognostication for severity of acute PE. We performed a literature search of MEDLINE, EMBASE, and PubMed up to February 2015. Article titles and abstracts were screened, and articles were included if they were observational studies that used a surface 12-lead ECG as the instrument for measurement, a diagnosis of PE was confirmed by imaging, arteriography or autopsy, and analysis of prognostic outcomes was performed. Thirty-six articles met our inclusion criteria. We review the prognostic value of ECG abnormalities included in the 21-point ECG score, including new evidence that has arisen since the time of its publication. We also discuss the potential prognostic value of several ECG abnormalities with newly identified prognostic value in the setting of acute PE., (© 2015 Wiley Periodicals, Inc.)

Background: We will focus our attention in this article in the ECG changes of classical Prinzmetal angina that occur during occlusive proximal coronary spasm usually in patients with normal or noncritical coronary stenosis., Results: The most important ECG change during a focal proximal coronary spasm is in around 50% of cases the appearance of peaked and symmetrical T wave that is followed, if the spasm persist, by progressive ST-segment elevation that last for a few minutes, and later progressively resolve. The most frequent ECG changes associated with ST-segment elevation are: (a) increased height of the R wave, (b) coincident S-wave diminution, (c) upsloping TQ in many cases, and (d) alternans of the elevated ST-segment and negative T wave deepness in 20% of cases. The presence of arrhythmias is very frequent during Prinzmetal angina crises, especially ventricular arrhythmias. The prevalence and importance of ventricular arrhythmias were related to: (a) duration of episodes, (b) degree of ST-segment elevation, (c) presence of ST-T wave alternans, and (d) the presence of >25% increase of the R wave., Conclusions: The incidence of Prinzmetal angina is much lower then 50 years ago for many reasons including treatment with calcium channel blocks to treat hypertension and ischemia heart disease and the decrease of smoking habits., (© 2014 Wiley Periodicals, Inc.)

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. [ABSTRACT FROM AUTHOR]

Background: Many morphological parameters of the electrocardiogram (ECG) can be calculated from a digital ECG and paper prints of ECG after digitizing. However, the digitizing process, including printing, scanning, ECG contour extraction, and alignment, can produce changes to the signals, reducing the reliability of some sensitive parameters of QRS complex and T wave., Methods: The influence of the digitizing process on the parameters of T wave and QRS complex morphology was studied by comparing systematically the values of the nine ECG morphology parameters, computed from the digital ECG and the corresponding paper ECG. The robustness of the parameters to the digitizing process and their discrimination ability between healthy subjects and postinfarction patients were investigated., Results: The standard T wave parameters and all selected dipolar loop-parameters retained their robustness and discrimination ability during the digitizing process of the paper ECGs. The non-dipolar parameters distorted strongly, especially those of the QRS complex. The T wave-based non-dipolar parameters retained their discrimination ability during the digitizing process., Conclusions: The selected standard T wave parameters and the dipolar loop-parameters calculated from properly digitized ECG paper prints can be utilized in patient studies. Non-dipolar parameters distort strongly but T wave-based parameters retain discriminatory information.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Aims: To evaluate relationships between plasma concentrations of belantamab mafodotin, total monoclonal antibody, and its payload and changes in electrocardiogram (ECG) parameters in patients with relapsed or refractory multiple myeloma from the DREAMM-1 and DREAMM-2 studies., Methods: Hysteresis plots and linear regression analyses of pharmacokinetic (PK) analyte (belantamab mafodotin, total monoclonal antibody, and cytotoxic cysteine-maleimidocaproyl monomethyl auristatin F payload) concentrations vs. time-matched ECG parameters (absolute/change from baseline in QT interval corrected for RR interval [QTc/ΔQTc] and QT interval corrected for heart rate by Fridericia's formula [QTcF/ΔQTcF]) were performed. Concentrations of PK analyte required for a 10-ms increase in QTc in DREAMM-2 were calculated via simulation, as was the probability of ΔQTc/ΔQTcF exceeding 10 ms for the expected C max of PK analyte concentrations associated with the doses (2.5 and 3.4 mg/kg) administered in DREAMM-2., Results: Time-matched PK and ECG data from 290 patients (DREAMM-1, n = 73; DREAMM-2, n = 217) were analysed. Hysteresis plots did not clearly indicate any concentration-related prolongation in QTc or QTcF; regression analyses indicated a very small rate of increase in ΔQTc and ΔQTcF with increasing concentrations of PK analytes. Calculated concentrations of PK analyte required for a 10-ms prolongation in QTc were higher than the maximum analyte concentrations observed following treatment with belantamab mafodotin in DREAMM-2; the probability that each dose would prolong ΔQTc and ΔQTcF by >10 ms was 0 and <0.25%, respectively., Conclusion: This study of belantamab mafodotin and its payload did not provide evidence of QT prolongation in patients with relapsed or refractory multiple myeloma at clinically relevant doses., (© 2024 GSK Plc. and The Author(s). British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Abstract The Fourth Universal Definition of Myocardial Infarction (FUDMI) focuses on the distinction between nonischemic myocardial injury and myocardial infarction (MI), along with the role of cardiovascular magnetic resonance, in order to define the etiology of myocardial injury. As a consequence, there is less emphasis on updating the parts of the definition concerning the electrocardiographic (ECG) changes related to MI. Evidence of myocardial ischemia is a prerequisite for the diagnosis of MI, and the ECG is the main available tool for (a) detecting acute ischemia, (b) triage, and (c) risk stratification upon presentation. This review focuses on multiple aspects of ECG interpretation that we firmly believe should be considered for incorporation in any future update to the Universal Definition of MI.

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data. [ABSTRACT FROM AUTHOR]