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 3.6milliseconds for the PR interval, 0.1 to 8.1milliseconds for QRS duration, and 0.1 to 9.3milliseconds 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 13.3milliseconds in the LQT1 subjects and from 0.2 to 11.0milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5milliseconds in the LQT1 subjects and from 0.9 to 12.8milliseconds 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.

Professor Herman Burger (1893-1965), eminent teacher and scientist, who laid the theoretical foundations of vectorcardiography--and electrocardiography.

Einthoven not only designed a high quality instrument, the string galvanometer, for recording the ECG, he also shaped the conceptual framework to understand it. He reduced the body to an equilateral triangle and the cardiac electric activity to a dipole, represented by an arrow (i.e. a vector) in the triangle's center. Up to the present day the interpretation of the ECG is based on the model of a dipole vector being projected on the various leads. The model is practical but intuitive, not physically founded. Burger analysed the relation between heart vector and leads according to the principles of physics. It then follows that an ECG lead must be treated as a vector (lead vector) and that the lead voltage is not simply proportional to the projection of the vector on the lead, but must be multiplied by the value (length) of the lead vector, the lead strength. Anatomical lead axis and electrical lead axis are different entities and the anatomical body space must be distinguished from electrical space. Appreciation of these underlying physical principles should contribute to a better understanding of the ECG. The development of these principles by Burger is described, together with some personal notes and a sketch of the personality of this pioneer of medical physics.

Normal values of the electrocardiogram for ages 16-90 years.

INTRODUCTION: To establish an up-to-date and comprehensive set of normal values for the clinically current measurements in the adult ECG, covering all ages for both sexes. METHODS: The study population included 13,354 individuals, taken from four population studies in The Netherlands, ranging in age from 16 to 90 years (55% men) and cardiologically healthy by commonly accepted criteria. Standard 12-lead ECGs were available for all participants. The ECGs were processed by a well-validated computer program. Normal limits were taken as the 2nd and 98th percentiles of the measurement distribution per age group. RESULTS: Our study corroborates many findings of previous studies, but also provides more differentiated results, in particular for the older age groups. Age trends were apparent for the QTc interval, QRS axis, and indices of left ventricular hypertrophy. Amplitudes in the left precordial leads showed a substantial increase in the older age groups for women, but not for men. Sex-dependent differences were apparent for most ECG parameters. All results are available on the Website www.normalecg.org, both in tabular and in graphical format. CONCLUSIONS: We determined age- and sex-dependent normal values of the adult ECG. Our study distinguishes itself from other studies by the large size of the study population, comprising both sexes, the broad range of ages, and the exhaustive set of measurements. Our results emphasize that most diagnostic ECG criteria should be age- and sex-specific.

Methodology of QT-interval measurement in the modular ECG analysis system (MEANS).

BACKGROUND: QT prolongation as can be induced by drugs, signals the risk of life-threatening arrhythmias. The methodology of QT measurement in the modular ECG analysis system (MEANS) is described. METHODS: In the simultaneously recorded leads of the standard 12-lead electrocardiogram (ECG), the QRS complexes are detected by a spatial velocity function. They are typed as dominant or nondominant, and a representative complex per lead is obtained by averaging over the dominant complexes. QRS onset and T end are determined by a template technique, and QT is measured. MEANS performance was evaluated on the 125 ECGs of the common standards for quantitative electrocardiography (CSE) multilead database, of which the waveform boundaries have been released. RESULTS: MEANS detected correctly all 1445 complexes of the CSE library, with one false-positive detection due to a sudden baseline jump. All dominant complexes were correctly typed. The average of the differences between MEANS and reference was less than 2 ms (=1 sample) for both QRS onset and T end, and 2.1 ms for QT duration. The standard deviation of the differences was 3.8, 8.4, and 10.4 ms, respectively. CONCLUSIONS: A standard deviation of 10.4 ms for QT measurement seems large when related to the regulatory requirement that a prolongation as small as 5 ms should be detected. However, QT variabilities as encountered in different individuals will be larger than when measured in one individual during pharmacological intervention. Finally, if the U wave is part of the total repolarization, then T and U form a continuum and the end of T becomes questionable.

Normal Values of QT Variability in 10-s Electrocardiograms for all Ages.

Aims: QT variability is a promising electrocardiographic marker. It has been studied as a screening tool for coronary artery disease and left ventricular hypertrophy, and increased QT variability is a known risk factor for sudden cardiac death. Considering that comprehensive normal values for QT variability were lacking, we set out to establish these in standard 10-s electrocardiograms (ECGs) covering both sexes and all ages. Methods: Ten-second, 12-lead ECGs were provided by five Dutch population studies (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study). ECGs were recorded digitally and processed by well-validated analysis software. We selected cardiologically healthy participants, 46% being women. Ages ranged from 11 days to 91 years. After quality control, 13,828 ECGs were available. We assessed three markers: standard deviation of QT intervals (SDqt), short-term QT variability (STVqt), and QT variability index (QTVI). Results: For SDqt and STVqt, the median and the lower limit of normal remained stable with age. The upper limit of normal declined until around age 45, and increased strongly in the elderly, notably so in women. This implies that a subset of the population, small enough not to have appreciable effect on the median, shows a high degree of QT variability with a possible risk of arrhythmias or worse, especially in women. Otherwise, sex differences were negligible in all three measurements. For QTVI, median, and normal limits decreased until age 20, and steadily went up afterwards except for the lower limit of normal, which flattens off after age 65. Conclusion: We report the first set of normal values for QT variability based on 10-s ECGs, for all ages and both sexes.

Corrigendum: Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

[This corrects the article DOI: 10.3389/fphys.2018.00424.].

Recommendations for the standardization and interpretation of the electrocardiogram: part II: Electrocardiography diagnostic statement list: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology.

This statement provides a concise list of diagnostic terms for ECG interpretation that can be shared by students, teachers, and readers of electrocardiography. This effort was motivated by the existence of multiple automated diagnostic code sets containing imprecise and overlapping terms. An intended outcome of this statement list is greater uniformity of ECG diagnosis and a resultant improvement in patient care. The lexicon includes primary diagnostic statements, secondary diagnostic statements, modifiers, and statements for the comparison of ECGs. This diagnostic lexicon should be reviewed and updated periodically.

Recommendations for the standardization and interpretation of the electrocardiogram: part I: The electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology.

This statement examines the relation of the resting ECG to its technology. Its purpose is to foster understanding of how the modern ECG is derived and displayed and to establish standards that will improve the accuracy and usefulness of the ECG in practice. Derivation of representative waveforms and measurements based on global intervals are described. Special emphasis is placed on digital signal acquisition and computer-based signal processing, which provide automated measurements that lead to computer-generated diagnostic statements. Lead placement, recording methods, and waveform presentation are reviewed. Throughout the statement, recommendations for ECG standards are placed in context of the clinical implications of evolving ECG technology.

Mirror image electrocardiograms and additional electrocardiographic leads: new wine in old wineskins?

BACKGROUND: Mirror image electrocardiograms (ECGs), obtained by inverting the original signals, and additional precordial leads have been proposed as means to improve ECG diagnosis. The theoretical backgrounds of these proposals are discussed. METHODS: In 746 body surface potential maps, the mirror areas of the 6 precordial leads, V(3)R, and 2 more leads higher up and 1 lower down the thorax have been determined. The similarity between the original signal and its mirror image was expressed by a similarity index. This was done separately for QRS and ST-T; for the first and second parts of QRS; and for the categories normal, left ventricular hypertrophy, and infarct. RESULTS: In general, high similarity scores were obtained. The mirror images of V(1) and V(2) are almost diametrically located on the back. Inverting these leads could render the V(8) and V(9) leads. The other mirror areas may deviate considerably from where generally expected. CONCLUSION: Mirror images can be obtained consistently from all locations, supporting the dipole representation of cardiac electrical activity. Neither mirror image ECGs nor additional chest leads contribute essentially to ECG diagnosis.

Intraindividual variability in electrocardiograms.

The electrocardiogram (ECG) can be affected by intraindividual variations from various sources that may confuse the diagnosis of the underlying cardiac condition and impair the accuracy of ECG interpretation. Intraindividual variability is a hindrance in serial ECG analysis, where ECGs of the same individual, but taken at different times, are compared. Two sources of intraindividual variability can be distinguished as follows: variability related to the technical circumstances during ECG recording (technical sources) and nonpathologic biologic variability (biological sources). Among the technical sources, variation in electrode positioning between recordings is the most confusing. Of the biological sources, respiratory variations are effective at any time scale, but the most important are age and weight that work on prolonged time scales. Technical problems are best prevented by rigorously sticking to a standard acquisition protocol. Criteria can be adapted to changing circumstances (age, weight), and by computer modeling, it may be possible to correct the ECG diagnosis for some sources of intraindividual variability.

The meaning of the Tp-Te interval and its diagnostic value.

BACKGROUND: The interval between T peak (Tp) and T end (Te) has been proposed as a measure of transmural dispersion of repolarization, but experimental and clinical studies to validate Tp-Te have given conflicting results. We have investigated the meaning of Tp-Te and its diagnostic potential. METHODS: We used a digital model of the left ventricular wall to simulate the effect of varying action potential durations on the timing of Tp and Te. Furthermore, we used the vectorcardiogram to explain the relationships between Tp locations in the precordial electrocardiogram leads. RESULTS: Prolongation or ischemic shortening of action potentials in our model did not result in substantial Tp shifts. The phase relationships revealed by the vectorcardiogram showed that Tp-Te in the precordial leads is a derivative of T loop morphology. CONCLUSION: Tp-Te is the resultant of the global distribution of the repolarization process and is a surrogate diagnostic parameter.

Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

Purpose: Heart-rate variability (HRV) measured on standard 10-s electrocardiograms (ECGs) has been associated with increased risk of cardiac and all-cause mortality, but age- and sex-dependent normal values have not been established. Since heart rate strongly affects HRV, its effect should be taken into account. We determined a comprehensive set of normal values of heart-rate corrected HRV derived from 10-s ECGs for both children and adults, covering both sexes. Methods: Five population studies in the Netherlands (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study) provided 10-s, 12-lead ECGs. ECGs were stored digitally and analyzed by well-validated analysis software. We included cardiologically healthy participants, 42% being men. Their ages ranged from 11 days to 91 years. After quality control, 13,943 ECGs were available. Heart-rate correction formulas were derived using an exponential model. Two time-domain HRV markers were analyzed: the corrected standard deviation of the normal-to-normal RR intervals (SDNNc) and corrected root mean square of successive RR-interval differences (RMSSDc). Results: There was a considerable age effect. For both SDNNc and RMSSDc, the median and the lower limit of normal decreased steadily from birth until old age. The upper limit of normal decreased until the age of 60, but increased markedly after that age. Differences of the median were minimal between men and women. Conclusion: We report the first comprehensive set of normal values for heart-rate corrected 10-s HRV, which can be of value in clinical practice and in further research.

Recommendations for the standardization and interpretation of the electrocardiogram. Part I: The electrocardiogram and its technology. A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society.

This statement examines the relation of the resting ECG to its technology. Its purpose is to foster understanding of how the modern ECG is derived and displayed and to establish standards that will improve the accuracy and usefulness of the ECG in practice. Derivation of representative waveforms and measurements based on global intervals are described. Special emphasis is placed on digital signal acquisition and computer-based signal processing, which provide automated measurements that lead to computer-generated diagnostic statements. Lead placement, recording methods, and waveform presentation are reviewed. Throughout the statement, recommendations for ECG standards are placed in context of the clinical implications of evolving ECG technology.

Optimizing the 12-lead electrocardiogram: a data driven approach to locating alternative recording sites.

BACKGROUND: Despite its widespread use, the limitations of the 12-lead electrocardiogram (ECG) are undisputed. The main deficiency is that just a small area of the precordium is interrogated and for some abnormalities information may be transmitted to a region of the body surface where information is not recorded. In this study, we attempted to optimize the 12-lead ECG by using a data-driven approach to suggest alternate recording sites. METHODS: A sequential lead selection algorithm was applied to a set of 744 body surface potential maps (BSPMs), consisting of recordings from subjects with myocardial infarction, left ventricular hypertrophy, and no apparent disease. A number of scenarios were investigated in which pairs of precordial leads were repositioned; these pairs were V3 and V5, V4 and V5, and V4 and V6. The algorithm was also used to find optimal positions for all 6 precordial leads. RESULT: Through estimation of entire surface potential distributions it was found that each of the scenarios, with 2 leads repositioned, captured more information than the standard 12-lead ECG. The scenario with V4 and V6 repositioned performed best with a root mean square error of 22.3 microvolts and a correlation coefficient of 0.967. This configuration also fared favorably when compared to the scenario where all 6 precordial leads were repositioned as optimizing all 6 leads offered no significant improvement. CONCLUSION: This study demonstrated the use of a lead selection algorithm in enhancing the 12-lead ECG. The results also indicated that repositioning just 2 precordial leads can provide the same level of information capture as that observed when all precordial leads are optimally placed.

Validation of automatic measurement of QT interval variability.

BACKGROUND: Increased variability of beat-to-beat QT-interval durations on the electrocardiogram (ECG) has been associated with increased risk for fatal and non-fatal cardiac events. However, techniques for the measurement of QT variability (QTV) have not been validated since a gold standard is not available. In this study, we propose a validation method and illustrate its use for the validation of two automatic QTV measurement techniques. METHODS: Our method generates artificial standard 12-lead ECGs based on the averaged P-QRS-T complexes from a variety of existing ECG signals, with simulated intrinsic (QT interval) and extrinsic (noise, baseline wander, signal length) variations. We quantified QTV by a commonly used measure, short-term QT variability (STV). Using 28,800 simulated ECGs, we assessed the performance of a conventional QTV measurement algorithm, resembling a manual QTV measurement approach, and a more advanced algorithm based on fiducial segment averaging (FSA). RESULTS: The results for the conventional algorithm show considerable median absolute differences between the simulated and estimated STV. For the highest noise level, median differences were 4-6 ms in the absence of QTV. Increasing signal length generally yields more accurate STV estimates, but the difference in performance between 30 or 60 beats is small. The FSA algorithm proved to be very accurate, with most median absolute differences less than 0.5 ms, even for the highest levels of disturbance. CONCLUSIONS: Artificially constructed ECGs with a variety of disturbances allow validation of QTV measurement procedures. The FSA algorithm provides highly accurate STV estimates under varying signal conditions, and performs much better than traditional beat-by-beat analysis. The fully automatic operation of the FSA algorithm enables STV measurement in large sets of ECGs.

Dispersion of repolarization, myocardial iso-source maps, and the electrocardiographic T and U waves.

The surface potential at any given electrode location is the net result of simultaneously acting and variously directed electrical forces in the myocardium. The degree to which the electrical forces in the heart are thus opposing each other has been defined as cancellation, and this mechanism plays a major role in the formation of the electrocardiogram (ECG). However, previous studies did not take into account the locations of the electrical sources. In this study, we used a computer model of the left ventricle to study the effect of source locations on cancellation during the T and U waves. The model represents an anatomically stylized cross-sectional slice of the left ventricle, containing 1961 hexagonal cells in a single layer. An action potential (AP) is assigned to each cell. The timing of the APs follows a simulated excitation sequence. The potential differences between the APs of adjacent cells produce time-varying electrical sources, each of which contributes to the potential in an arbitrary point P on the body proportionally to its own, location-dependent, transfer function (lead vector). The ECG at P is the sum of all potential contributions. For each time point in the ECG at P, the contribution of each cell is mapped back onto the slice. Adjacent cells with equal contributions form iso-source strings, together forming iso-source maps. The T-U wave as observed in P will be the sum of positive and negative contributions from the iso-source distributions as they change with time. The iso-source maps for an anteriorly located observation point P at 4.2 cm from the epicardial surface show a continuous interplay of positive and negative contributions. During the near-zero ST segment, cancellation varies between 80% and 100%. In the ascending limb of the T wave, positive contributions substantially increase, giving a decrease in cancellation to about 40%. At the end of the T wave (with almost zero amplitude), the positive contributions are only slightly reduced as compared with those at peak T, but greatly increased negative contributions cancel them out. This is contrary to the generally held view that the end of T signifies the end of the repolarization process. The manifest shape of the T and U waves is the result of complex interactions of varying and often largely canceling contributions. The iso-source maps are helpful to understand the genesis of the T and U waves.

The U wave in the electrocardiogram: a solution for a 100-year-old riddle.

OBJECTIVE: In the electrocardiogram (ECG) the U wave follows the T, which is considered to reflect the repolarization of the cardiac ventricles. Despite the U wave's well-known clinical relevance, a satisfactory explanation of its origin is still outstanding. We have undertaken to explain the formation of the U wave by means of a simple digital model of the left ventricle. METHODS: The model employs a multi-layered segment of the myocardium. To each layer an action potential (AP) is assigned with shape and duration according to published data. The potential differences between the APs produce time-varying electrical sources. Each source contributes to the potentials in an arbitrary point P of the body. The strength of this contribution is determined by a specific coefficient, the "lead vector", linking P to the source. The ECG recorded at P is calculated as the sum of all potential contributions. RESULTS: The repolarization waves constructed in this way reproduce the natural aspects of a T wave followed by a U wave. The creation of a U wave is conditional on small voltage differences between the tail ends of the APs. No fundamental demarcation exists between U wave and preceding T wave. The morphology of the T-U wave is dependent on the geometrical position of P with respect to the myocardium. CONCLUSION: T and U form a continuum. Together they are the resultant of one and the same process of repolarization of the ventricular myocardium. This has implications for the measurement of QT duration and for safety testing of drug-induced QT prolongation.