Derivation of the correct waveform of the human electrocardiogram by Willem Einthoven, 1890-1895

In the period 1890 to 1895, Willem Einthoven greatly improved the quality of tracings that could be directly obtained with the capillary electrometer. He then introduced an ingenious correction for the poor frequency response of these instruments, using differential equations. This method allowed him to predict the correct form of the human electrocardiogram, as subsequently revealed by the new string galvanometer that he introduced in 1902. For Einthoven, who won the Nobel Prize for the development of the electrocardiogram in 1924, one of the most rewarding aspects of the high fidelity recording of the human electrocardiogram was its validation of his earlier theoretical predictions regarding the electrical activity of the heart. (Cardiol J 2010; 17, 1: 109-113

Principles of simple heart rate adjustment of ST segment depression during exercise electrocardiography

Compared with standard test criteria, simple heart rate (HR) adjustment of ST depression during exercise electrocardiography can improve the identification and assessment of underlying coronary artery disease. Since heart rate during exercise drives progressive ST segment depression in the presence of coronary obstruction that limits flow reserve, the ST/HR index controls for the increasing metabolic severity of ischemia that accompanies exercise. Improvement of exercise test sensitivity with the ST/HR index results from reclassification of otherwise "equivocal" and even "negative" test responses, including increased identification of one and two-vessel disease in men and in women. In addition, in population studies of low and moderate risk subjects, the ST/HR index can increase the prognostic value of the exercise electrocardiogram for prediction of cardiac risk and mortality. (Cardiol J 2008; 15: 194-200

Heart rate adjustment of the time-voltage ST segment integral: Identification of coronary disease and relation to standard and heart rate-adjusted ST segment depression criteria

AbstractTo assess the effect of heart rate adjustment of the magnitude of the ST integral (ST-HR integral) on exercise test performance, the exercise electrocardiogram (ECG) of 50 clinically normal subjects and 100 patients with known or suspected coronary artery disease was analyzed. At matched specificity of 96% with standard ECG criteria (≥0.1 mV of additional horizontal or downsloping ST segment depression), ar unadjusted ST integral partition of 16 μV-s identified coronary disease in the 100 patients with known or suspected disease with a sensitivity of only 41%, a value significantly lower than the 59% sensitivity of standard ECG criteria (p < 0.01) and the 65% sensitivity of an ST depression partition of 130 μV (p < 0.001).However, test performance of the ST integral was greatly improved by simple heart rate adjustment: at a matched specificity of 96%, an ST-HR integral partition of 0.154 μV-s/beat per min identified coronary disease in the 100 patients with a sensitivity of 90%, a value significantly greater than the 59% sensitivity of standard criteria and 65% sensitivity of ST depression criteria (each p < 0.001) and similar to the 91% sensitivity of the ST-HR index and 93% sensitivity of the ST-HR slope (each p = NS). Comparison of receiver-operating characteristic curves confirmed the superior overall test performance of the ST-HR integral relative to the ST integral and ST segment depression, and demonstrated improved performance that was comparable with that of the ST-HR index and the ST-HR slope.These findings support the value of heart rate adjustment of end-exercise repolarization changes during exercise electrocardiography and demonstrate that this approach significantly improves the performance of the ST integral in identifying coronary artery disease

Debatable issues in automated ECG reporting

Although automated ECG analysis has been available for many years, there are some aspects which require to be re-assessed with respect to their value while newer techniques which are worthy of review are beginning to find their way into routine use. At the annual International Society of Computerized Electrocardiology conference held in April 2017, four areas in particular were debated. These were a) automated 12 lead resting ECG analysis; b) real time out of hospital ECG monitoring; c) ECG imaging; and d) single channel ECG rhythm interpretation. One speaker presented the positive aspects of each technique and another outlined the more negative aspects. Debate ensued. There were many positives set out for each technique but equally, more negative features were not in short supply, particularly for out of hospital ECG monitoring

Electrocardiographic detection of left ventricular hypertrophy using echocardiographic determination of left ventricular mass as the reference standard Comparison of standard criteria, computer diagnosis and physician interpretation

Electrocardiographic findings of left ventricular hypertrophy were compared with echocardiographic left ventricular mass in 148 patients to assess performance of standard electrocardiographic criteria, the IBM Bonner program and physician interpretation. On echocardiography, 43% of the patients had left ventricular hypertrophy (left ventricular mass > 215 g). Sokolow-Lyon voltage (S in V1+ R in V5or V6) and Romhilt-Estes point score correlated modestly with left ventricular mass (r = 0.40, p < 0.001 and r = 0.55, p < 0.001, respectively). Sensitivity of Sokolow-Lyon voltage greater than 3.5 mV for left ventricular hypertrophy was only 22%, but specificity was 93%. Point score for probable left ventricular hypertrophy (≥ 4 points) had 48% sensitivity and 85% specificity, whereas definite hypertrophy (≥ 5 points) had 34% sensitivity and 98% specificity. Computer analysis resulted in 45% sensitivity and 83% specificity. Overall diagnostic accuracy of the IBM Bonner program (67%) was better than that of Sokolow-Lyon voltage (62%), but worse than the Romhilt-Estes point score (69% for ≥ 4 points or 70% for ≥ 5 points). Three cardiologists interpreted electrocardiograms independently and in a blinded fashion. Physician sensitivity was 56%, specificity 92% and accuracy 76%. Correlation with left ventricular hypertrophy was good (r = 0.70, p < 0.001).It is concluded that: 1) computer diagnosis of left ventricular hypertrophy by the IBM Bonner program is no more accurate than diagnosis by Sokolow-Lyon or Romhilt-Estes criteria, and 2) physician recognition of left ventricular hypertrophy is more accurate. This suggests that additional information about left ventricular hypertrophy is present in the electrocardiogram that is not detectable by standard criteria or the IBM computer program

Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs

Background: Automated measurements of electrocardiographic (ECG) intervals by current-generation digital electrocardiographs are critical to computer-based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison. Methods: Seven widely used automated algorithms for computer-based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2). Results: For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6 milliseconds for the PR interval, 0.1 to 8.1 milliseconds for QRS duration, and 0.1 to 9.3 milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3 milliseconds in the LQT1 subjects and from 0.2 to 11.0 milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5 milliseconds in the LQT1 subjects and from 0.9 to 12.8 milliseconds in the LQT2 subjects. Conclusions: Among current-generation computer-based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within-individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011