Diagnostic Electrocardiographic Research Articles

Diagnostic accuracy of the 12-lead electrocardiogram in the first 48 hours of life for newborns of a parent with congenital long QT syndrome.

Long QT syndrome (LQTS) is an autosomal dominant disorder characterized by a prolonged QT interval. Electrocardiographic (ECG) screening in the first 48 hours of life may be misleading, even in newborns with a genotype-positive LQTS parent. The purpose of this study was to determine the ECG's diagnostic accuracy in the first 48 hours of life for neonates born to a parent with LQTS. We conducted a retrospective review of all neonates born at Mayo Clinic to a parent with ≥1 pathogenic variant in a LQTS-causative gene who had least 1 ECG in the first 48 hours and genetic test results were available. The sensitivity and specificity of the diagnostic ECG were calculated using Bazett's heart rate-corrected QT (QTc) thresholds of 440, 450, 460, and 470 ms. Overall, 74 newborns (36 females [49%]) were included (mean QTc interval on the first ECG 489 ± 54 ms; 50 [68%] LQTS genotype-positive). The mean QTc interval in the first 48 hours for neonates that ultimately were genotype-positive was greater (506 ± 52 ms) than that for genotype-negative neonates (455 ± 41 ms) (P = .0004). When using a recommended threshold QTc interval of ≥440 ms, 6 of 50 genotype-positive neonates (12%) were missed (underdiagnosed) and 17 of 24 genotype-negative neonates (71%) were overdiagnosed (sensitivity 88%; specificity 29%). The newborn ECG should not be used in isolation to make the diagnosis of LQTS since it will result in many misclassifications. Genetic testing must be initiated before discharge, and proper anticipatory guidance is vital while awaiting test results.

Robust nonlinear aggregation operator for ECG powerline interference reduction

ObjectiveNowadays, the most effective methods for ElectroCardioGraphic (ECG) PowerLine (PL) interference suppression, based on the subtraction operation, require the determination of signal fragments with a low electrical activity of the heart. Any errors in searching for these fragments result in a loss of effectiveness of interference suppression and an increase in signal distortion. This article proposes a method that does not need to determine such signal fragments. MethodsThe robust nonlinear aggregation operator is used for the PL disturbance estimation. This results in an automatic reduction (or elimination) of the influence of the ECG signal fragments with an increased electrical activity of the heart. In other words, the samples with the lowest electrical activity of the heart will be used for the PL interference estimation. ResultsThe method leads to a significant reduction of the ECG signal distortions, unattainable for other methods, leaving the residual noise of the order of single micro-volts, regardless of the PL interference level tackled. The method can be applied using an algorithm that is computationally very efficient, which can help to reduce the total cost of ECG signal processing. The new method proposed is experimentally compared to the traditional ones using signals from the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. ConclusionThe method proposed is an accurate, robust and computationally efficient algorithm for reduction of PL interference disturbing ECG signals. SignificanceNew prospects emerge for systems processing the ECG signal, it is possible to analyze traces with a more unfavorable signal-to-noise ratio. This applies to monitoring and exercise systems as well as to a special advanced analysis of e.g. cardiac micro-potentials.

Electrocardiographic Distinction of Left Circumflexand Right Coronary Artery Occlusion in PatientsWith Inferior Acute Myocardial Infarction

Previously reported electrocardiographic (ECG) criteria to distinguish left circumflex (LCCA) and right coronary artery (RCA) occlusion in patients with acute inferior ST-segment elevation myocardial infarction (STEMI) afford a modest diagnostic accuracy. We aimed to develop a new algorithm overcoming limitations of previous studies. Clinical, ECG, and coronary angiographic data were analyzed in 230 nonselected patients with acute inferior STEMI who underwent primary percutaneous coronary intervention. A decision-tree analysis was used to develop a new ECG algorithm. The diagnostic accuracy of reported ECG criteria was reviewed. LCCA occlusion occurred in 111 cases and RCA in 119. We developed a 3-step algorithm that identified LCCA and RCA occlusion with a sensitivity of 77%, specificity of 86%, accuracy of 82%, and Youden index of 0.63. The area under the ROC curve was 0.85 and resulted 0.82 after a 10-fold cross validation. The key leads for LCCA occlusion were V3 (ST depression in V3/ST elevation in III >1.2) and V6 (ST elevation ≥0.1 mV or greater than III). The key leads for RCA occlusion were I and aVL (ST depression ≥ 0.1 mV). Fifteen of 21 reviewed studies had less than 20 cases of LCCA occlusion, only 48% performed primary percutaneous coronary intervention, and previous infarction or multivessel disease were often excluded. The diagnostic accuracy of reported ECG criteria decreased when applied to our study population. In conclusion, we report a simple and highly discriminative 3-step ECG algorithm to differentiate LCCA and RCA occlusion in an "all comers" population of patients with acute inferior STEMI. The diagnostic key ECG leads were V3 and V6 for LCCA and I and aVL for RCA occlusion.

Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger

Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the single-lead ECG signals of MI.

ECG Authentication Method Based on Parallel Multi-Scale One-Dimensional Residual Network With Center and Margin Loss

To enhance the security level of digital information, the biometric authentication method based on Electrocardiographic (ECG) is gaining increasing attention in a wide range of applications. Compared with other biometric features, e.g., fingerprint and face, the ECG signals have several advantages, such as higher security, simpler acquisition, liveness detection, and health information. Therefore, various methods for ECG-based authentication have been proposed. However, the generalization ability of these methods is limited because the feature extraction for the ECG signals in conventional methods is data dependent. To improve the generalization ability and achieve more stable results on different datasets, a parallel multi-scale one-dimensional residual network is proposed in this paper. This network utilizes three convolutional kernels with different kernel sizes, achieving better classification accuracy than the conventional schemes. Moreover, two loss functions named center loss and margin loss are used during the training of the network. Compared with the conventional softmax loss, these two loss functions can further improve the generalization ability of the extracted embedding features. Furthermore, we evaluate the effectiveness of our proposed method thoroughly on the ECG-ID database, the PTB Diagnostic ECG database, and the MIT-BIH Arrhythmia database, achieving 2.00%, 0.59%, and 4.74% of equal error rate (EER), respectively. Compared with other works, our proposed method improves 1.61% and 4.89% classification accuracy on the ECG-ID database and the MIT-BIH Arrhythmia database, respectively.

Updates to IEC/AAMI ECG standards, a new hybrid standard

The current set of IEC particular standards that pertain to electrocardiograph (ECG) devices, namely, 60601-2-25, 60601-2-27, and 60601-2-47, which define requirements and testing for the essential performance and basic safety of diagnostic ECG, ECG monitoring and ambulatory ECG systems, respectively. These standards have been harmonized with the corresponding AAMI standards, namely EC11, EC13 and EC38. Together these standards have been in existence for decades and have evolved separately even though the technology used in these three clinical applications is very similar. A work proposal was initiated in the ISO/IEC Joint Work Group 22 (JWG22), which over sees the revision of these standards, to update the standards by creating a single new hybrid standard. The goal of this work is a joint endeavor between ISO and IEC to combine the three separate particular standards that cover ECG device and to harmonize the similar requirements that are common across all three types of devices into one set of general requirements. The requirements that separate specific to meeting the intended use and essential performance for each of the 3 types of devices (diagnostic, patient monitoring, and ambulatory ECG recording) will continue to remain as separate requirements.Furthermore, manufactures of ECG devices have also been required to meet standards for disposable electrodes (AAMI EC12), ECG cables and leadwires (AAMI EC53), and arrhythmia analysis performance reporting (AAMI EC57). In addition to the primary goal, a secondary goal for the JW22 work is to also incorporate these three AAMI standards into the ECG hybrid standard. This paper describes the work being done and highlights key updates, and explains the rationale for approaches and changes being made. The Joint Work Group is currently in the process of updating the committee draft of the new hybrid standard before requesting comments from National Standard Committees participating in JWG22.

Deep-ECG: Convolutional Neural Networks for ECG biometric recognition

Electrocardiographic (ECG) signals have been successfully used to perform biometric recognition in a wide range of applications. However, ECG-based biometric systems are usually less accurate than technologies based on other physiological traits. To increase their performance, it is necessary to study novel approaches. Deep learning methods, like Convolutional Neural Networks (CNNs), can automatically extract distinctive features, and have demonstrated their effectiveness for other biometric systems. In this paper, we present Deep-ECG, a CNN-based biometric approach for ECG signals. To the best of our knowledge, this is the first study in the literature that uses a CNN for ECG biometrics. Deep-ECG extracts significant features from one or more leads using a deep CNN and compares biometric templates by computing simple and fast distance functions, obtaining remarkable accuracy for identification, verification and periodic re-authentication. Furthermore, using a simple quantization procedure, Deep-ECG can obtain binary templates that can facilitate the use of ECG-based biometric systems in conjunction with cryptographic applications. We also propose a simple method to enlarge the training dataset of ECG samples, which can increase the performance of deep neural networks. We performed experiments on large sets of samples acquired in uncontrolled conditions, proving the accuracy and robustness ofDeep-ECG in non-ideal scenarios. Furthermore, we evaluated the performance of Deep-ECG for the PTB Diagnostic ECG Database, obtaining identification accuracy better or comparable to the best performing methods in the literature, also for signals with different characteristics with respect to the ones used to train the CNN.

A multichannel decision-level fusion method for T wave alternans detection.

Sudden cardiac death (SCD) is one of the most prominent causes of death among patients with cardiac diseases. Since ventricular arrhythmia is the main cause of SCD and it can be predicted by T wave alternans (TWA), the detection of TWA in the body-surface electrocardiograph (ECG) plays an important role in the prevention of SCD. But due to the multi-source nature of TWA, the nonlinear propagation through thorax, and the effects of the strong noises, the information from different channels is uncertain and competitive with each other. As a result, the single-channel decision is one-sided while the multichannel decision is difficult to reach a consensus on. In this paper, a novel multichannel decision-level fusion method based on the Dezert-Smarandache Theory is proposed to address this issue. Due to the redistribution mechanism for highly competitive information, higher detection accuracy and robustness are achieved. It also shows promise to low-cost instruments and portable applications by reducing demands for the synchronous sampling. Experiments on the real records from the Physikalisch-Technische Bundesanstalt diagnostic ECG database indicate that the performance of the proposed method improves by 12%-20% compared with the one-dimensional decision method based on the periodic component analysis.

Meta-Analysis on Risk Stratification of Asymptomatic Individuals With the Brugada Phenotype

The prognosis of asymptomatic subjects remains the most controversial issue in Brugada syndrome (BS). A meta-analysis on the prognostic role of spontaneous type 1 electrocardiographic (ECG) pattern and programmed ventricular stimulation (PVS) in asymptomatic subjects with Brugada electrocardiogram was performed. Current databases were searched until March 2014. Fourteen prospective observational studies were included in the present meta-analysis, accumulating data on 3,536 asymptomatic subjects (2,820 men) with BS phenotype. The mean follow-up period varied from 20 and 77 months. Data regarding 1,398 asymptomatic subjects with spontaneous type 1 ECG pattern of BS were retrieved from 6 studies. During follow-up, arrhythmic events (sustained ventricular tachycardia/fibrillation, appropriate device therapies, or arrhythmic death) occurred in 42 patients (3%). The meta-analysis of these studies demonstrated that asymptomatic subjects with spontaneous type 1 ECG pattern of BS exhibit an increased risk of future arrhythmic events (odds ratio = 3.56, 95% confidence interval 1.70 to 7.47, Z = 3.37, p = 0.0008); 1,104 asymptomatic subjects with BS ECG pattern from 12 studies underwent PVS and were available for analysis. During follow-up, arrhythmic events occurred in 36 subjects (3.3%). Inducible ventricular arrhythmias at PVS were predictive of future arrhythmic events (odds ratio = 3.51, 95% confidence interval 1.60 to 7.67, Z = 3.14, p = 0.002). In conclusion, this meta-analysis showed that asymptomatic subjects with either spontaneous diagnostic ECG pattern or inducible ventricular arrhythmias at PVS are at increased risk.

Multilead Template‐Derived Residua of Surface ECGS for Quantitative Assessment of Arrhythmia Risk

Contemporary electrocardiographic (ECG) markers including ventricular ectopy and arrhythmias have not proved reliable in risk assessment for life-threatening arrhythmias. We developed the "Multilead ECG Template-Derived Residua" approach to remove intrinsic morphologic differences and allow calculation of pathologic ECG heterogeneities among spatially separated leads. Prediction by R-wave and T-wave heterogeneity (RWH, TWH) analysis was tested in simulated and clinical ECGs. An enabling description of the Residua algorithm is provided. Simulated ECGs with but not without Residua produced a linear relationship (correlation coefficient r(2) = 0.999) between input and output RWH and TWH values. In heart failure patients, Residua disclosed a marked crescendo in RWH from 164.1 ± 33.1 at baseline to 299.8 ± 54.5 μV and TWH from 134.5 ± 20.6 at baseline to 239.2 ± 37.0 μV at 30-45 minutes before the arrhythmia (both, P < 0.05), which remained elevated until arrhythmia onset. Without Residua, mean RWH and TWH were elevated at 1061.0 ± 222.9 and 882.5 ± 375.2 μV, respectively, throughout the recording and were not different prior to ventricular tachycardia onset. Calculation of ECG-template derived Residua provides a highly accurate means for assessing arrhythmia risk from standard ECGs. Potential widespread applications include resting diagnostic 12-lead, ambulatory, and exercise ECGs, electrophysiologic study laboratory recordings, and implantable devices.

A comprehensive electrocardiographic, molecular, and echocardiographic study of Brugada syndrome: Validation of the 2013 diagnostic criteria

The debate on the diagnostic value of high intercostal spaces (ICSs) and of the number of diagnostic leads in Brugada syndrome (BrS) has been settled by a recent expert consensus statement. To test the validity, and the underlying anatomy, of the new electrocardiographic (ECG) diagnostic criteria using echocardiographic, molecular, and clinical evidence in 1 clinical study population with BrS. We analyzed 114 patients with BrS and with a spontaneous or drug-induced type 1 ECG pattern recorded in 1 or more right precordial leads in fourth, third, and second ICSs. The right ventricular outflow tract (RVOT) was localized by using echocardiography. All probands were screened on the SCN5A gene. The percentage of mutation carriers (MCs) and the event rate were similar regardless of the diagnostic ICS (fourth vs high ICSs: MCs 23% vs 19%; event rate 22% vs 28%) and the number of diagnostic leads (1 vs ≥2: MCs 20% vs 22%; event rate 22% vs 27%). The concordance between RVOT anatomical location and the diagnostic ICSs was 86%. The percentage of the diagnostic ECG pattern recorded was significantly increased by the exploration of the ICSs showing RVOT by echocardiography (echocardiography-guided approach vs conventional approach 100% vs 43%; P < .001). The high ICSs are not inferior to the standard fourth ICS for the ECG diagnosis of BrS, and the interindividual variability depends on the anatomical location of the RVOT as assessed by using echocardiography. This approach significantly increases diagnostic sensitivity without decreasing specificity and fully supports the recently published new diagnostic criteria.

Identifying the high-risk Brugada syndrome patient: Let us get personal

There are several reasons why risk stratification in the Brugada syndrome (BrS) continues to pose significant challenges. Prevalence of the syndrome in the general population is low, yet high-risk individuals can manifest with sudden cardiac death at a young age when the implantable cardioverter-defibrillator comes with a heavier burden of lifestyle change and cumulative long-term complications. Many patients will be asymptomatic on presentation and electrocardiographic (ECG) changes can fall in a nondiagnostic category. Despite significant and ongoing advancements in genetic discovery and technology, the yield of genetic testing in overall patients with BrS remains well below 50%. 1 The 2005 Consensus Conference established that conversion of a nondiagnostic ECG (saddle pattern type 2 or 3 ECG) to a diagnostic ECG (type 1 Brugada pattern) with a sodium-channel blocker (SCB) test indicated a positive diagnosis of the BrS. 1 This is an important issue, since increased awareness of this condition has resulted in a referral stream to electrophysiologists and cardiologists of patients with a nondiagnostic Brugada-pattern ECG who have vague or no symptoms. However, more recently, the largest BrS registry (FINGER Registry, 1029 consecutive subjects from 4 European countries) concluded that event rates in asymptomatic patients with BrS were low (0.5% per year) and that inducibility of ventricular arrhythmias and family history of sudden cardiac death were not predictive of sudden cardiac death. 2 Spontaneous type 1 ECG changes and the presence of symptoms were the only predictors of arrhythmic events in this population. In this issue of HeartRhythm, Zorzi el al 3 specifically address the utility of the SCB test among 198 patients with a nondiagnostic Brugada-pattern ECG recruited at 2 medical centers of the Veneto region of Italy. Of the total, 27 who developed spontaneous type 1 ECG changes were excluded and 18 were not available for follow-up. The remaining 153 subjects with type 2 or 3 Brugada ECGs were followed up for a mean period of 59 months. A positive SCB test result was associated with a significantly higher event rate in symptomatic patients but not in asymptomatic patients. These results suggest that the clinical utility of the SCB test in asymptomatic patients with nondiagnostic ECGs is likely to be low. This careful study incorporated placement of the right precordial leads in a superior position in order to increase sensitivity for detecting type 1 ECG changes. 4 What is the clinical message of this study and what are the caveats to keep in mind? The findings of this study provide more evidence to clinicians that SCB testing may not be indicated in asymptomatic patients with a nondiagnostic Brugada-pattern ECG. We should keep in mind that this was a relatively small study with a relatively short follow-up period given the natural history of the BrS. Also, event rates were low and the study did not provide information on pre-2005 (Consensus Conference publication) vs post-2005 clinical management. Most important, if the findings of the FINGER registry remain consistent, our clinical attention is likely to focus largely on symptomatic patients with a spontaneous type 1 BrS ECG.

Brugada-like Electrocardiographic Pattern Induced by Lamotrigine Toxicity

Brugada syndrome, a recognized cause of sudden cardiac death, is due to a defect of cardiac sodium channels. Many pharmotherapeutic agents induce an electrocardiographic (ECG) pattern that can be confused with Brugada syndrome in patients who may not have the disease. A 22-year-old Hispanic female presented for emergency evaluation with ataxia and alterations in consciousness. Her medical history was significant for temporal lobe epilepsy, treated with lamotrigine, a phenyltriazine agent known to block neuronal voltage-gated sodium channels. There was no family history of sudden cardiac death. Initial laboratory data, neuroimaging, and echocardiography were unremarkable. Her ECG on presentation was concerning for Brugada pattern. Because of the nonspecific ECG finding, the patient underwent procainamide challenge, which was initially felt to be positive. Serum lamotrigine level was subsequently reported in the toxic range at 20.4 μg/mL (therapeutic range, 1-4 μg/mL). Repeated procainamide challenge was performed with lamotrigine levels below the therapeutic range (0.2 μg/mL) and failed to show diagnostic ECG changes. Lamotrigine, at toxic levels, may lose specificity and exert an effect on cardiac sodium channels, a phenomenon that has not been previously described. Given previous reports of associations between the drug-induced Brugada ECG pattern and ventricular dysrhythmias, clinicians should be aware of this potential effect.

Significance of QRS prolongation during diagnostic ajmaline test in patients with suspected Brugada syndrome

Current consensus documents on Brugada syndrome recommend the diagnostic intravenous administration of a Na-channel blocker to be stopped when the QRS prolongs to > or =130% of baseline, presumably because of increased arrhythmic risk. This study sought to assess QRS prolongation during ajmaline testing and its relation to arrhythmic risk. We analyzed an electrocardiographic (ECG) database collected during ajmaline testing in 148 patients (92 men, age 36 +/- 15 years). The QRS was measured at baseline and during the 1st to 7th, 10th, and 15th minute after the beginning of ajmaline administration. The average QRS prolongation was 36% +/- 16% (range 9% to 88%), not significantly different between positive (n = 30) and negative (n = 118) tests. QRS prolonged to > or =130% during 16 (55%) positive and 71 (61%) negative tests (P = .50), with no clinical side effects. The incidence of ventricular arrhythmias was not significantly different between patients with and without QRS prolongation. Short runs (3 to 8 complexes) of nonsustained ventricular tachycardia occurred in 3 patients with QRS prolongation > or =130%. In 40% of positive tests, prolongation > or =130% occurred earlier by >1 minute than diagnostic Brugada ECG changes, i.e., early termination of the test could possibly have resulted in false-negative outcomes. QRS prolongation > or =130% occurs in >50% of all tests. In 40% of positive tests it occurs before diagnostic ECG changes. Always terminating the test when QRS prolongs > or =130% could possibly result in loss of important diagnostic information. It is appropriate to adjust the criteria for early termination of the test to the baseline QRS and possibly other factors.

Agreement and coding reliability of the minnesota and mayo electrocardiographic coding systems

Problem: To determine whether diagnoses of myocardial infarction assigned by a system that uses Marquette 12SL electrocardiographic (ECG) codes with manual over-reading agree with diagnoses assigned by Minnesota ECG codes. Studies undertaken: Agreement and recode reliability of Minnesota and Mayo coding systems based on 768 ECGs plus chest pain history and serum enzyme values were analyzed for a stratified random sample of 141 patients with an event in 1990 or 1991 coded as HICDA 410.x, 411, 413 or 796.9. The population was reconstructed from the stratified random sample so that population-based inferences could be made from the analysis. Results: For the stratified random sample, exact agreement on 4 categories (evolving diagnostic, diagnostic, equivocal, or other ECG) between Mayo and Minnesota ECG coding was 53.9% (kappa = 0.37 ± 0.05). Code-recode agreement was higher for Minnesota coding (83.0%; kappa = 0.74 ± 0.05) compared with Mayo coding (73.8%; kappa = 0.64 ± 0.05). The same pattern was present for the reconstructed population. For coding myocardial infarction based on the ECG, serum enzyme levels, and the presence or absence of ischemic chest pain, agreement between Mayo and Minnesota coding was 84.4% (kappa = 0.72 ± 0.05) based on the stratified random sample and 81.7% (kappa = 0.67 ± 0.06) based on the reconstructed population. For the stratified random sample, reliability of diagnosis of myocardial infarction was 93.6% (kappa = 0.88 ± 0.04) for the Minnesota system and 94.3% (kappa = 0.90 ± 0.03) for the Mayo system. Conclusion: ECG interpretation by the Mayo and Minnesota coding systems differs significantly, and Mayo ECG coding is less reliable than Minnesota ECG coding. Coding of myocardial infarction on the basis of ECGs, serum enzymes, and ischemic chest pain, however, is equally reliable for both systems.

Electrocardiographic Findings in a Healthy Biracial Population

It has been well documented that the prevalence of certain electrocardiographic (ECG) findings among individuals free of coronary heart disease (CHD) differs by race. However, it is not known whether these differences exist independently of CHD risk factors (e.g., hypertension). We examined the ECG tracings of 2,686 apparently healthy, middle-aged African-American and white men and women who participated in the Atherosclerosis Risk in Communities Study and were at low risk of CHD. Using the Minnesota Code, among men, 46% of African-Americans, but only 25% of whites, had a minor ECG finding (p <0.001). In women, 32% of African-Americans and 23% of whites had a minor ECG finding (p <0.01). Specifically, the age-adjusted prevalences of high-amplitude R wave, ST elevation, T-wave findings, and prolonged P-R interval were statistically significantly higher in African-Americans. As for continuous ECG measurements, the R wave in leads V 5 and V 6, the S wave in V 1, the J-point amplitude in leads V 2 and V 5, the P-R interval, and the Cornell voltage (|S V 3| + R aVL) for left ventricular hypertrophy were all significantly greater in African-Americans than in whites. However, in both men and women, the heart rate corrected QT interval was shorter in African-Americans than in whites. All of these findings remained statistically significant after further adjustment for traditional CHD risk factors. These results suggest that racial differences in electrocardiograms may not be explained entirely by differences in established CHD risk factors, and because current diagnostic ECG criteria are largely based on data from middle-aged white men and women, race should be considered in the interpretation of ECG findings.

Assessing ECG signal quality on a coronary care unit

Poor electrocardiograph (ECG) signal quality is associated with an increase in the number of false alarms, may degrade diagnostic information, and can increase the workload for coronary care unit (CCU) or other intensive care staff. It is important therefore to establish simple quantitative measures that can be used to demonstrate signal quality problems. In this study, a fixed-gain diagnostic bandwidth ECG from patients in a single CCU bed was monitored continuously for 10 weeks and measures which could relate to quality were calculated. These measures were the number of times the ECG exceeded a preset limit (out-of-range events, ) and the frequency content of the ECG plus superimposed noise in six different bandwidths (0.05 - 0.25, 0.25 - 10, 10 - 20, 20 - 48, 48 - 52, and 52 - 100 Hz). A computer-based data collection system calculated a 10 s average for each of the measures and logged these to memory. Following the data collection phase, good-quality baseline levels for the seven measures were calculated for each of the days studied and compared with levels during the evening - night (6 pm - 6 am), which were in turn compared with levels during the day-time (6 am - 6 pm). All measures were significantly lower (p < 0.001) for the selected good-quality ECGs compared with those recorded during the night, with low frequency, lower ECG bandwidth, and out-of-range events producing the greatest differences. Night-time noise levels were lower than day-time levels, and the largest reductions were found in the rate of out-of-range events (19.8 to ) (p < 0.02), and low-frequency content less than 0.25 Hz (70 to ) (p < 0.01). Significant reductions during the night were also found in the lower ECG bandwidth 0.25 - 10 Hz (111 to ) (p < 0.01). No significant changes were found in the higher frequencies. We conclude that the low-frequency content and rate of out-of-range events are easy to obtain and could be used as measures for evaluating signal quality. Keywords: alarm systems, coronary care unit, electrocardiogram, monitoring, signal quality

The pacing stress test: A reexamination of the relation between coronary artery disease and pacing-induced electrocardiographic changes

Electrocardiographic (ECG) changes during graded pacing-induced tachycardia have been considered unreliable as a test for the presence of coronary artery disease (CAD) because of poor sensitivity and specificity. As a result, atrial pacing has not been widely used as an alternative to exercise testing. However, the limited value of the pacing stress test may be related to technical aspects, such as the duration of pacing and ECG monitoring. To study this problem, 22 patients undergoing coronary cineangiography underwent standard exercise stress testing and graded tachycardia induced by atrial pacing. A 12-lead ECG recorder was used for both tests. Pacing tachycardia was terminated when 85% of maximal predicted heart rate had been achieved or when significant ischemic chest pain accompanied by diagnostic ECG changes occurred. The ECG was considered positive if at least 1 mm of horizontal or downsloping ST-segment depression was present. Six patients with normal or minimally diseased coronary arteries were compared to 16 patients with significant CAD. Of the patients without significant CAD, 5 (83%) had a negative electrocardiogram during both exercise and pacing. Of 16 patients with CAD, the electrocardiogram was positive for ischemia in 10 patients (63%) during exercise, in 15 (94%) during atrial pacing and in 12 (80%) after pacing. When the presence or absence of ECG changes was compared between the exercise and the pacing tests, there was a concordance of 90% (Fisher p < 0.0015). Two patients without significant CAD (33%) had chest pain during both exercise and pacing. Among patients with CAD, 7 (44 %) had chest pain during exercise and 8 (50%) had chest pain during atrial pacing. Thus, the sensitivity and specificity of ECG changes during atrial pacing compare favorably with those produced by exercise testing. ECG changes with pacing are reliable for assessing the presence or absence of CAD. Graded tachycardia induced by atrial pacing may be of particular value in patients unable to complete an exercise tolerance test.

Changing electrocardiographic recording technology and diagnostic accuracy of myocardial infarction criteria. Improved standards for evaluation of ECG measurement precision

An analytical study was performed to estimate the magnitude of the visual Q wave duration bias produced by pressurized ink round stylus electrocardiographic (ECG) recorders. With a paper speed of 25 mm/sec and the ECG tracing width of 0.25 mm, the visually measured Q waves are on the average 8 msec too short. The corresponding error with the older type flat stylus recorder is less than 2 msec. Considerable differences can thus be anticipated in the frequency of observed ECG abnormalities in studies which use different types of electrocardiographs. The effect of the visual Q wave duration measurement bias on the diagnostic ECG classification was investigated in a group of 237 patients with old myocardial infarction and 299 subjects with no clinical evidence of infarction. An 8 msec measurement bias toward too short Q wave duration was observed to result in a potential loss of diagnostic accuracy of about 25% in some ECG coding categories. As a corrective procedure, it is recommended that the baseline width produced by a round recording stylus of uniform thickness irrespective of the vertical deflection velocity should be less.