Automated detection of ventricular pre-excitation in pediatric 12-lead ECG.

BACKGROUND: With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff-Parkinson-White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence. METHODS: Digital ECGs with 12 leads or 15 leads (12-lead plus V3R, V4R and V7) were selected from multiple hospitals and separated into a testing and training database. Inclusion criterion was age less than 16 years. The reference for algorithm detection of VPE was cardiologist annotation of VPE for each ECG. The training database (n=772) consisted of VPE ECGs (n=37), normal ECGs (n=492) and a high concentration of conduction defects, RBBB (n=232) and LBBB (n=11). The testing database was a random sample (n=763). All ECGs were analyzed with the Philips DXL ECG Analysis algorithm for basic waveform measurements. Additional ECG features specific to VPE, mainly delta wave scoring, were calculated from the basic measurements and the average beat. A classifier based on decision tree bootstrap aggregation (tree bagger) was trained in multiple steps to select the number of decision trees and the 10 best features. The classifier accuracy was measured on the test database. RESULTS: The new algorithm detected pediatric VPE with a sensitivity of 78%, a specificity of 99.9%, a positive predictive value of 88% and negative predictive value of 99.7%. CONCLUSION: This new algorithm for detection of pediatric VPE performs well with a reasonable positive and negative predictive value despite the low prevalence in the general population.

Agreement between cardiologists and fellows in interpretation of ischemic electrocardiographic changes in acute myocardial infarction.

BACKGROUND: Time from symptom onset may not be the best indicator for choosing reperfusion therapy for patients presenting with acute ST-elevation myocardial infarction (STEMI); consequently ECG-based methods have been developed. METHODS: This study evaluated the inter-observer agreement between experienced cardiologists and junior doctors in identifying the ECG findings of the pre-infarction syndrome (PIS) and evolving myocardial infarction (EMI). The ECGs of 353 STEMI patients were independently analyzed by two cardiologists, one fellow in cardiology, one fellow in internal medicine and a medical student. The last two were given a half-hour introduction of the PIS/EMI-algorithm. RESULTS: The inter-observer reliability between all the investigators was found to be good according to kappa statistics (κ 0.632-0.790) for the whole study population. When divided into different subgroups, the inter-observer agreements were from good to very good between the cardiologists and the fellow in cardiology (κ 0.652 -0.813) and from moderate to good (κ 0.464-0.784) between the fellow in internal medicine, medical student and the others. CONCLUSIONS: The PIS and EMI ECG patterns are reliably identified by experienced cardiologists and can be easily adopted by junior doctors.

Normal standards for computer-ECG programs for prognostically and diagnostically important ECG variables derived from a large ethnically diverse female cohort: the Women's Health Initiative (WHI).

BACKGROUND: Substantial new information has emerged recently about the prognostic value for a variety of new ECG variables. The objective of the present study was to establish reference standards for these novel risk predictors in a large, ethnically diverse cohort of healthy women from the Women's Health Initiative (WHI) study. METHODS AND RESULTS: The study population consisted of 36,299 healthy women. Racial differences in rate-adjusted QT end (QT(ea)) and QT peak (QT(pa)) intervals as linear functions of RR were small, leading to the conclusion that 450 and 390 ms are applicable as thresholds for prolonged and shortened QT(ea) and similarly, 365 and 295 ms for prolonged and shortened QT(pa), respectively. As a threshold for increased dispersion of global repolarization (T(peak)T(end) interval), 110 ms was established for white and Hispanic women and 120 ms for African-American and Asian women. ST elevation and depression values for the monitoring leads of each person with limb electrodes at Mason-Likar positions and chest leads at level of V1 and V2 were first computed from standard leads using lead transformation coefficients derived from 892 body surface maps, and subsequently normal standards were determined for the monitoring leads, including vessel-specific bipolar left anterior descending, left circumflex artery and right coronary artery leads. The results support the choice 150 µV as a tentative threshold for abnormal ST-onset elevation for all monitoring leads. Body mass index (BMI) had a profound effect on Cornell voltage and Sokolow-Lyon voltage in all racial groups and their utility for left ventricular hypertrophy classification remains open. CONCLUSIONS: Common thresholds for all racial groups are applicable for QT(ea), and QT(pa) intervals and ST elevation. Race-specific normal standards are required for many other ECG parameters.

Automated discrimination of proximal right coronary artery occlusion from middle-to-distal right coronary artery occlusion and left circumflex occlusion in ST-elevation myocardial infarction.

BACKGROUND: Classifying the location of an occlusion in the culprit artery during ST-elevation myocardial infarction (STEMI) is important for risk stratification to optimize treatment. We developed a new logistic regression (LR) algorithm for 3-group classification of occlusion location as proximal right coronary artery (RCA), middle-to-distal RCA or left circumflex (LCx) coronary artery with inferior myocardial infarction. We compared the performance of the new LR algorithm with the recently introduced decision tree classifier of Fiol et al (Ann Noninvasive Electrocardiol. 2004;4:383-388) in the classification of the same 3 categories. METHODS: The new algorithm was developed on a set of electrocardiograms from an emergency department setting (n = 64) and tested on a different set from a prehospital setting (n = 68). All patients met the current STEMI criteria with angiographic confirmation of culprit artery and occlusion location. Using LR, 4 ST-segment deviation features were chosen by forward stepwise selection. Final LR coefficients were obtained by averaging more than 200 bootstrap iterations on the training set. In addition, a separate 4-feature classifier was designed adding ST features of V4R and V8, only available in the training set. RESULTS: The LR algorithm classified proximal RCA occlusion vs combined LCx occlusion and middle-to-distal RCA occlusion, with a sensitivity of 76% and specificity of 81% as compared with 71% and 62% for the Fiol classifier. The difference in specificity was statistically significant. The LR classifier trained with additional ST features of V4R and V8, but still limited to 4, improved the overall agreement in the training set from 65% to 70%. CONCLUSION: Discrimination of proximal RCA lesion location from LCx or middle-to-distal RCA using the new LR classifier shows improvement over decision tree­type classification criteria. Automated identification of proximal RCA occlusion could speed up the risk stratification of patients with STEMI. The addition of leads V4R and V8 should further improve the automated classification of the occlusion site in RCA and LCx.

Automated discrimination of proximal right coronary artery occlusion from middle-to-distal right coronary artery occlusion and left circumflex occlusion in ST-elevation myocardial infarction.

BACKGROUND: Classifying the location of an occlusion in the culprit artery during ST-elevation myocardial infarction (STEMI) is important for risk stratification to optimize treatment. We developed a new logistic regression (LR) algorithm for 3-group classification of occlusion location as proximal right coronary artery (RCA), middle-to-distal RCA or left circumflex (LCx) coronary artery with inferior myocardial infarction. We compared the performance of the new LR algorithm with the recently introduced decision tree classifier of Fiol et al (Ann Noninvasive Electrocardiol. 2004;4:383-388) in the classification of the same 3 categories. METHODS: The new algorithm was developed on a set of electrocardiograms from an emergency department setting (n = 64) and tested on a different set from a prehospital setting (n = 68). All patients met the current STEMI criteria with angiographic confirmation of culprit artery and occlusion location. Using LR, 4 ST-segment deviation features were chosen by forward stepwise selection. Final LR coefficients were obtained by averaging more than 200 bootstrap iterations on the training set. In addition, a separate 4-feature classifier was designed adding ST features of V4R and V8, only available in the training set. RESULTS: The LR algorithm classified proximal RCA occlusion vs combined LCx occlusion and middle-to-distal RCA occlusion, with a sensitivity of 76% and specificity of 81% as compared with 71% and 62% for the Fiol classifier. The difference in specificity was statistically significant. The LR classifier trained with additional ST features of V4R and V8, but still limited to 4, improved the overall agreement in the training set from 65% to 70%. CONCLUSION: Discrimination of proximal RCA lesion location from LCx or middle-to-distal RCA using the new LR classifier shows improvement over decision tree-type classification criteria. Automated identification of proximal RCA occlusion could speed up the risk stratification of patients with STEMI. The addition of leads V4R and V8 should further improve the automated classification of the occlusion site in RCA and LCx.

Electrocardiographic estimates of regional action potential durations and repolarization time subintervals reveal ischemia-induced abnormalities in acute coronary syndrome not evident from global QT.

We evaluated electrocardiogram estimates of repolarization times (RTs) and action potential durations (APD) separately for initial and terminal repolarization periods in a reference group of 5376 healthy men and women and in 125 acute coronary syndrome patients with and 657 without diagnostic ST elevation (ST-elevation myocardial infarction [STEMI] and non-STEMI [NSTEMI], respectively). Two key covariates in the model are the rate-adjusted QT peak interval (QT(pa)), assigned to earliest epicardial RT (RT(epi)), and (T(p)-T(xd)), the rate-invariant interval from T(p) to the inflection point (T(xd)) at T wave downstroke. (T(p)-T(xd)) defines the crossmural RT gradient (XMRT(grad)). Transmural RT(grad) (TMRT(grad)) is obtained as CosΘ(R(max)|T(max))*XMRT(grad), where Θ is the spatial angle between the maximal QRS and T vectors. Derived endocardial variables are the XMRT(endo), equal to QT(pa) + XMRT(grad) and TMRT(endo), equal to QT(pa) + TMRT(grad). Noting that excitation time (ET) and RT define APD, APD(epi) = RT(epi) - QR(p) in V6 and TMAPD(endo) = TMRT(endo)--10 milliseconds. Compared to the reference group, the estimates for APD(epi) and TMAPD(endo) were shortened in STEMI by 20 and 31 milliseconds, respectively, (p < 0.001 for both) signifying transmural ischemia. In contrast, in NSTEMI, TMAPD(endo) was shortened by 28 milliseconds (P < 0.001) with a lesser, 5 millisecond shortening of APD(epi), signifying subendocardial ischemia. QT was prolonged by 6 milliseconds in STEMI (P < 0.05) and by 8 milliseconds in NSTEMI (P < 0.001). Prolonged QT with shortened APD(epi) suggests that prolonged repolarization in terminal possibly non-ischemic regions accounts for QT prolongation in both myocardial infarction groups. These substantial differences in ischemia-induced regional manifestations of repolarization abnormalities revealed by the repolarization model were not evident from evaluation of the global QT.

Electrocardiogram-derived respiration in screening of sleep-disordered breathing.

Methods for assessment of sleep-disordered breathing (SDB), including sleep apnea, range from a simple questionnaire to complex multichannel polysomnography. Inexpensive and efficient electrocardiogram (ECG)-based solutions could potentially fill the gap and provide a new SDB screening tool. In addition to the heart rate variability (HRV)-based SDB screening method that we reported a year ago, we have developed a novel method based on ECG-derived respiration (EDR). This method derives the respiratory waveform by (a) measuring peak-to-trough QRS amplitude in a single-channel ECG, (b) removing outlier introduced by noise and artifacts, (c) interpolating the derived values, and (d) filtering values within the respiration rates of 5 and 25 cycles per minute. Each 30 seconds of the respiratory waveform is then classified as normal, SDB, or indeterminate epoch. The previously reported HRV-based method, applied at the same time, is based on power spectrum of heart rate over a sliding 6-minute time window to classify the middle 30-second epoch. We then combined the EDR- and HRV-based techniques to optimize the classification of each epoch. The combined method further improved the accuracy of SDB screening in an independent test database with annotated SDB epochs. The development database was from PhysioNet (n = 25 polysomnograms). The test database was from Sleep Health Centers in Boston (n = 1907 polysomnogram) where the SDB epochs (n = 1,538,222 epochs) were scored using American Academy of Sleep Medicine criteria. The first test was to classify every epoch in the evaluation data set. The combined EDR and HRV method classified 78% of the epochs as either normal or SDB and 22% as indeterminate, with a total accuracy of 88% for scored epochs (not indeterminate). The second test was to evaluate the SDB status for each patient. The algorithm correctly classified 71% of patients with either moderate-to-severe SDB or mild-to-no SDB. We believe that the ECG-based methods provide an efficient and inexpensive tool for SDB screening in both home and hospital settings and make SDB screening feasible in large populations.

Electrocardiographic estimates of action potential durations and transmural repolarization time gradients in healthy subjects and in acute coronary syndrome patients--profound differences by sex and by presence vs absence of diagnostic ST elevation.

Action potential duration (APD) changes increasing repolarization time (RT) dispersion are potentially arrhythmogenic. A repolarization model developed from electrocardiographic data of 5376 healthy men and women was used to derive parameter estimates for APD and RT and their transmural gradients (RT(grad) and APD(grad), respectively) in myocardial infarction patients, 126 with and 658 without diagnostic ST elevation (STEMI and NSTEMI, respectively). The model uses, as covariates, rate-adjusted QT and QT peak intervals (QT(a) and QT(pa), respectively) and diagonal crossmural RT(grad) derived as T(p)-T(xd), the interval from T(p) to the inflection point at descending limb of global T wave. An additional parameter is Θ(T|T(ref)), the spatial angle between a subject's T vector and the average T vector of the normal reference group. If Θ(T|T(ref)) >0, QT(pa) is assigned to RT(epi) and QT(pa) + RT(grad) to RT(endo), with RT(epi) and RT(endo) assignments reversed if Θ(T|T(ref)) ≤0. Parameter estimates for APD(epi) and APD(endo) were shorter in men than in women (by 17 ms and 14 ms, respectively, P < .001 for both). Compared to the reference group, RT(epi) in the STEMI group was shortened by 14 ms in men and by 18 ms in women (P < .001 for both) with a lesser decrease in RT(endo) suggesting predominantly subepicardial ischemia. In NSTEMI only RT(endo) was shortened, by 6 ms in males (P < .01) and 10 ms in females (P < .001), suggesting subendocardial ischemia. RT(grad) signifying local crossmural RT dispersion was prolonged in STEMI by 8 ms in men and by 11 ms in men (P < .001 for both). RT(grad) was not changed significantly in NSTEMI. Rate-adjusted T(p)-T(e) interval signifying global RT dispersion was increased in both MI and in both sex groups (P <.001 for all). In conclusion, QT prolongation observed in NSTEMI without prolongation of RT(grad) and APD(epi) suggests a delay during terminal repolarization, and in contrast, in STEMI, QT is not changed significantly in spite of prolonged RT(grad) because of shortened APD(epi) and RT(epi). These repolarization abnormalities are not revealed by QT alone but readily by the repolarization model.

Heart rate, gender differences, and presence versus absence of diagnostic ST elevation as determinants of spatial QRS|T angle widening in acute coronary syndrome.

We explored the directions of the repolarization sequence (RS) and excitation sequence (ES) as determinants of QRS|T angle widening in 126 patients with acute coronary syndrome (ACS) with and 658 patients without diagnostic ST-segment elevation myocardial infarction and in a reference group of 5,376 normal subjects. The initial and terminal RS and ES directions were derived from the initial and terminal T and QRS vectors from quintiles 1 to 3 and 5 of time-normalized QRS and T. In addition to the QRS|T angle, we evaluated the RS and ES deviation angles from the normal RS and ES directions. The normal zones for the ES and RS directions in the reference group were used to classify the ES and RS in the group with and without ST-segment myocardial infarction as normal or deviant. The initial and terminal QRS|T angles were rate invariant in the normal group but correlated with the heart rate (r = 0.33) in the ACS group. Adjusted for a heart rate of 70 beats/min, the initial QRS|T angle increased progressively in the ACS group from 41° among those with normal RS and ES directions to 121° when both were deviant. This apparently ischemia-induced initial QRS|T angle increase was associated with an initial RS direction from the anterior-right to the posterior-left producing a progressively greater peak TV1 amplitude. The terminal ES orientation in the ACS group was toward the right ventricular outflow tract with a QRS|T angle of 124° in 48% and leftward and posterior, with the QRS|T angle decreasing to 59°, in the remaining 52%. In conclusion, the initial and terminal RS and ES orientations differ drastically and their deviant orientation accounts for the widened QRS|T angle in patients with ACS, differences not revealed by evaluation of the mean QRS|T angle.

Development of a serial comparison program for conduction defects, acute myocardial infarction, and the use of additional leads.

Serial comparison of electrocardiograms (ECGs) is a useful tool in clinical diagnostic ECG and an enhancement to computer ECG analysis. When an analysis algorithm is modified, the corresponding serial comparison program needs to be updated accordingly. The new Philips diagnostic algorithm increased the number of leads in the ECG from the traditional 12 leads to 16, making it possible to diagnose right ventricular infarct/injury based on right-sided lead V4R. To keep pace with the widespread reperfusion therapy for acute myocardial infarct, the serial comparison program was revised to recognize the rapid ECG changes in patients with ST-elevation myocardial infarct following successful reperfusion therapies. The serial comparison program was also enhanced to split "combined" statements in the category of ventricular conduction delay (includes incomplete ventricular conduction delay and bundle-branch blocks) and compare each of the statements separately.

Computerized classification of proximal occlusion in the left anterior descending coronary artery.

Proximal occlusion within the left anterior descending (LAD) coronary artery in patients with acute myocardial infarction leads to higher mortality than does nonproximal occlusion. We evaluated an automated program to detect proximal LAD occlusion. All patients with suspected acute coronary syndrome (n = 7,710) presenting consecutively to the emergency department of a local hospital with a coronary angiogram­confirmed flow-limiting lesion and notation of occlusion site were included in the study (n = 711). Electrocardiograms (ECGs) that met ST-segment elevation myocardial infarction (STEMI) criteria were included in the training set (n = 183). Paired angiographic location of proximal LAD and ECGs with ST elevation in the anterolateral region were used for the computer program development (n = 36). The test set was based on ECG criteria for anterolateral STEMI only without angiographic reports (n = 162). Tested against 2 expert cardiologists' agreed reading of proximal LAD occlusion, the algorithm has a sensitivity of 95% and a specificity of 82%. The algorithm is designed to have high sensitivity rather than high specificity for the purpose of not missing any proximal LAD in the STEMI population. Our preliminary evaluation suggests that the algorithm can detect proximal LAD occlusion as an additional interpretation to STEMI detection with similar accuracy as cardiologist readers.

Automatic detection and quantification of sleep apnea using heart rate variability.

Detection of sleep apnea using electrocardiographic (ECG) parameters is noninvasive and inexpensive. Our approach is based on the hypothesis that the patient's sleep-wake cycle during episodes of sleep apnea modulates heart rate (HR) oscillations. These HR oscillations appear as low-frequency fluctuations of instantaneous HR (IHR) and can be detected using HR variability analysis in the frequency domain. The purpose of this study was to evaluate the efficacy of our ECG-based algorithm for sleep apnea detection and quantification. The algorithm first detects normal QRS complexes and R-R intervals used to derive IHR and to estimate its spectral power in several frequency ranges. A quadratic classifier, trained on the learning set, uses 2 parameters to classify the 1-minute epoch in the middle of each 6-minute window as either apneic or normal. The windows are advanced by 1-minute steps, and the classification process is repeated. As a measure of quantification, the algorithm correctly classified 84.7% of all the 1-minute epochs in the evaluation database; and as a measure of the accuracy of apnea classification, the algorithm correctly classified all 30 test recordings in the evaluation database either as apneic or normal. Our sleep apnea detection algorithm based on analysis of a single-lead ECG provides accurate apnea detection and quantification. Because of its noninvasive and low-cost nature, this algorithm has the potential for numerous applications in sleep medicine.

Improvements in atrial fibrillation detection for real-time monitoring.

Electrocardiographic (ECG) monitoring plays an important role in the management of patients with atrial fibrillation (AF). Automated real-time AF detection algorithm is an integral part of ECG monitoring during AF therapy. Before and after antiarrhythmic drug therapy and surgical procedures require ECG monitoring to ensure the success of AF therapy. This article reports our experience in developing a real-time AF monitoring algorithm and techniques to eliminate false-positive AF alarms. We start by designing an algorithm based on R-R intervals. This algorithm uses a Markov modeling approach to calculate an R-R Markov score. This score reflects the relative likelihood of observing a sequence of R-R intervals in AF episodes versus making the same observation outside AF episodes. Enhancement of the AF algorithm is achieved by adding atrial activity analysis. P-R interval variability and a P wave morphology similarity measure are used in addition to R-R Markov score in classification. A hysteresis counter is applied to eliminate short AF segments to reduce false AF alarms for better suitability in a monitoring environment. A large ambulatory Holter database (n = 633) was used for algorithm development and the publicly available MIT-BIH AF database (n = 23) was used for algorithm validation. This validation database allowed us to compare our algorithm performance with previously published algorithms. Although R-R irregularity is the main characteristic and strongest discriminator of AF rhythm, by adding atrial activity analysis and techniques to eliminate very short AF episodes, we have achieved 92% sensitivity and 97% positive predictive value in detecting AF episodes, and 93% sensitivity and 98% positive predictive value in quantifying AF segment duration.

Philips QT interval measurement algorithms for diagnostic, ambulatory, and patient monitoring ECG applications.

BACKGROUND: Commonly used techniques for QT measurement that identify T wave end using amplitude thresholds or the tangent method are sensitive to baseline drift and to variations of terminal T wave shape. Such QT measurement techniques commonly underestimate or overestimate the "true" QT interval. METHODS: To find the end of the T wave, the new Philips QT interval measurement algorithms use the distance from an ancillary line drawn from the peak of the T wave to a point beyond the expected inflection point at the end of the T wave. We have adapted and optimized modifications of this basic approach for use in three different ECG application areas: resting diagnostic, ambulatory Holter, and in-hospital patient monitoring. The Philips DXL resting diagnostic algorithm uses an alpha-trimming technique and a measure of central tendency to determine the median QT value of eight most reliable leads. In ambulatory Holter ECG analysis, generally only two or three channels are available. QT is measured on a root-mean-square vector magnitude signal. Finally, QT measurement in the real time in-hospital application is among the most challenging areas of QT measurement. The Philips real time QT interval measurement algorithm employs features from both Philips DXL 12-lead and ambulatory Holter QT algorithms with further enhancements. RESULTS: The diagnostic 12-lead algorithm has been tested against the gold standard measurement database established by the CSE group with results surpassing the industrial ECG measurement accuracy standards. Holter and monitoring algorithm performance data on the PhysioNet QT database were shown to be similar to the manual measurements by two cardiologists. CONCLUSION: The three variations of the QT measurement algorithm we developed are suitable for diagnostic 12-lead, Holter, and patient monitoring applications.

Technical challenges and future directions in lead reconstruction for reduced-lead systems.

Reduced-lead electrocardiographic systems are currently a widely accepted medical technology used in a number of applications. They provide increased patient comfort and superior performance in arrhythmia and ST monitoring. These systems have unique and compelling advantages over the traditional multichannel monitoring lead systems. However, the design and development of reduced-lead systems create numerous technical challenges. This article summarizes the major technical challenges commonly encountered in lead reconstruction for reduced-lead systems. We discuss the effects of basis lead and target lead selections, the differences between interpolated vs extrapolated leads, the database dependency of the coefficients, and the approaches in quantitative performance evaluation, and provide a comparison of different lead systems. In conclusion, existing reduced-lead systems differ significantly in regard to trade-offs from the technical, practical, and clinical points of view. Understanding the technical limitations, the strengths, and the trade-offs of these reduced-lead systems will hopefully guide future research.

Where do derived precordial leads fail?

A 12-lead electrocardiogram (ECG) reconstructed from a reduced subset of leads is desired in continued arrhythmia and ST monitoring for less tangled wires and increased patient comfort. However, the impact of reconstructed 12-lead lead ECG on clinical ECG diagnosis has not been studied thoroughly. This study compares the differences between recorded and reconstructed 12-lead diagnostic ECG interpretation with 2 commonly used configurations: reconstruct precordial leads V(2), V(3), V(5), and V(6) from V(1),V(4), or reconstruct V(1), V(3), V(4), and V(6) from V(2),V(5). Limb leads are recorded in both configurations. A total of 1785 ECGs were randomly selected from a large database of 50,000 ECGs consecutively collected from 2 teaching hospitals. ECGs with extreme artifact and paced rhythm were excluded. Manual ECG annotations by 2 cardiologists were categorized and used in testing. The Philips resting 12-lead ECG algorithm was used to generate computer measurements and interpretations for comparison. Results were compared for both arrhythmia and morphology categories with high prevalence interpretations including atrial fibrillation, anterior myocardial infarct, right bundle-branch block, left bundle-branch block, left atrial enlargement, and left ventricular hypertrophy. Sensitivity and specificity were calculated for each reconstruction configuration in these arrhythmia and morphology categories. Compared to recorded 12-leads, the V(2),V(5) lead configuration shows weakness in interpretations where V(1) is important such as atrial arrhythmia, atrial enlargement, and bundle-branch blocks. The V(1),V(4) lead configuration shows a decreased sensitivity in detection of anterior myocardial infarct, left bundle-branch block (LBBB), and left ventricular hypertrophy (LVH). In conclusion, reconstructed precordial leads are not equivalent to recorded leads for clinical ECG diagnoses especially in ECGs presenting rhythm and morphology abnormalities. In addition, significant accuracy reduction in ECG interpretation is not strongly correlated with waveform differences between reconstructed and recorded 12-lead ECGs.

What is inside the electrocardiograph?

The details of digital recording and computer processing of a 12-lead electrocardiogram (ECG) remain a source of confusion for many health care professionals. A better understanding of the design and performance tradeoffs inherent in the electrocardiograph design might lead to better quality in ECG recording and better interpretation in ECG reading. This paper serves as a tutorial from an engineering point of view to those who are new to the field of ECG and to those clinicians who want to gain a better understanding of the engineering tradeoffs involved. The problem arises when the benefit of various electrocardiograph features is widely understood while the cost or the tradeoffs are not equally well understood. An electrocardiograph is divided into 2 main components, the patient module for ECG signal acquisition and the remainder for ECG processing which holds the main processor, fast printer, and display. The low-level ECG signal from the body is amplified and converted to a digital signal for further computer processing. The Electrocardiogram is processed for display by user selectable filters to reduce various artifacts. A high-pass filter is used to attenuate the very low frequency baseline sway or wander. A low-pass filter attenuates the high-frequency muscle artifact and a notch filter attenuates interference from alternating current power. Although the target artifact is reduced in each case, the ECG signal is also distorted slightly by the applied filter. The low-pass filter attenuates high-frequency components of the ECG such as sharp R waves and a high-pass filter can cause ST segment distortion for instance. Good skin preparation and electrode placement reduce artifacts to eliminate the need for common usage of these filters.

An algorithm for QT interval monitoring in neonatal intensive care units.

QT surveillance of neonatal patients, and especially premature infants, may be important because of the potential for concomitant exposure to QT-prolonging medications and because of the possibility that they may have hereditary QT prolongation (long-QT syndrome), which is implicated in the pathogenesis of approximately 10% of sudden infant death syndrome. In-hospital automated continuous QT interval monitoring for neonatal and pediatric patients may be beneficial but is difficult because of high heart rates; inverted, biphasic, or low-amplitude T waves; noisy signal; and a limited number of electrocardiogram (ECG) leads available. Based on our previous work on an automated adult QT interval monitoring algorithm, we further enhanced and expanded the algorithm for application in the neonatal and pediatric patient population. This article presents results from evaluation of the new algorithm in neonatal patients. Neonatal-monitoring ECGs (n = 66; admission age range, birth to 2 weeks) were collected from the neonatal intensive care unit in 2 major teaching hospitals in the United States. Each digital recording was at least 10 minutes in length with a sampling rate of 500 samples per second. Special handling of high heart rate was implemented, and threshold values were adjusted specifically for neonatal ECG. The ECGs studied were divided into a development/training ECG data set (TRN), with 24 recordings from hospital 1, and a testing data set (TST), with 42 recordings composed of cases from both hospital 1 (n = 16) and hospital 2 (n = 26). Each ECG recording was manually annotated for QT interval in a 15-second period by 2 cardiologists. Mean and standard deviation of the difference (algorithm minus cardiologist), regression slope, and correlation coefficient were used to describe algorithm accuracy. Considering the technical problems due to noisy recordings, a high fraction (approximately 80%) of the ECGs studied were measurable by the algorithm. Mean and standard deviation of the error were both low (TRN = -3 +/- 8 milliseconds; TST = 1 +/- 20 milliseconds); regression slope (TRN = 0.94; TST = 0.83) and correlation coefficients (TRN = 0.96; TST = 0.85) (P < .0001) were fairly high. Performance on the TST was similar to that on the TRN with the exception of 2 cases. These results confirm that automated continuous QT interval monitoring in the neonatal intensive care setting is feasible and accurate and may lead to earlier recognition of the "vulnerable" infant.

An algorithm for continuous real-time QT interval monitoring.

QT interval measurement in the patient monitoring environment is receiving much interest because of the potential for proarrhythmic effects from both cardiac and noncardiac drugs. The American Heart Association and American Association of Critical Care Nurses practice standards for ECG monitoring in hospital settings now recommend frequent monitoring of QT interval when patients are started on a potentially proarrhythmic drug. We developed an algorithm to continuously measure QT interval in real-time in the patient monitoring setting. This study reports our experience in developing and testing this automated QT algorithm. Compared with the environment of resting ECG analysis, real-time ECG monitoring has a number of challenges: significantly more amounts of muscle and motion artifact, increased baseline wander, a varied number and location of ECG leads, and the need for trending and for alarm generation when QT interval prolongation is detected. We have used several techniques to address these challenges. In contiguous 15-second time windows, we average the signal of tightly clustered normal beats detected by a real-time arrhythmia-monitoring algorithm to minimize the impact of artifact. Baseline wander is reduced by zero-phase high-pass filtering and subtraction of isoelectric points as determined by median signal values in a localized region. We compute a root-mean-squared ECG waveform from all available leads and use a novel technique to measure the QT interval. We have tested this algorithm against standard and proprietary ECG databases. Our real-time QT interval measurement algorithm proved to be stable, accurate, and able to track changing QT values.

Relation of QT interval measurements to evolving automated algorithms from different manufacturers of electrocardiographs.

QT-interval measurements have clinical importance for the electrocardiographic recognition of congenital and acquired heart disease and as markers of arrhythmogenic risk during drug therapy, but software algorithms for the automated measurement of electrocardiographic durations differ among manufacturers and evolve within manufacturers. To compare automated QT-interval measurements, simultaneous paired electrocardiograms were obtained in 218 subjects using digital recorders from the 2 major manufacturers of electrocardiographs used in the United States and analyzed by 2 currently used versions of each manufacturer's software. The 4 automated QT and QTc durations were examined by repeated-measures analysis of variance with post hoc testing. Significantly larger automated QT-interval measurements were found with the most recent software of each manufacturer (12- to 24-ms mean differences from earlier algorithms). Systematic differences in QT measurements between manufacturers were significant for the earlier algorithms (11-ms mean difference) but not for the most recent software (1.3-ms mean difference). Similar relations were found for the rate-corrected QTc, with large mean differences between earlier and later algorithms (15 to 26 ms). Although there was a <2-ms mean difference between the most recent automated QTc measurements of the 2 manufacturers, the SD of the difference was 12 ms. In conclusion, reference values for automated electrocardiographic intervals and serial QT measurements vary among electrocardiographs and analysis software. Technically based differences in automated QT and QTc measurements must be considered when these intervals are used as markers of heart disease, prognosis, or arrhythmogenic risk.