Automatic QT Interval Research Articles

Manual vs. automatic assessment of the QT-interval and corrected QT.

Sudden cardiac death (SCD) is challenging to predict. Electrocardiogram (ECG)-derived heart rate-corrected QT-interval (QTc) is used for SCD-risk assessment. QTc is preferably determined manually, but vendor-provided automatic results from ECG recorders are convenient. Agreement between manual and automatic assessments is unclear for populations with aberrant QTc. We aimed to systematically assess pairwise agreement of automatic and manual QT-intervals and QTc. A multi-centre cohort enriching aberrant QTc comprised ECGs of healthy controls and long-QT syndrome (LQTS) patients. Manual QT-intervals and QTc were determined by the tangent and threshold methods and compared to automatically generated, vendor-provided values. We assessed agreement globally by intra-class correlation coefficients and pairwise by Bland-Altman analyses and 95% limits of agreement (LoA). Further, manual results were compared to a novel automatic QT-interval algorithm. ECGs of 1263 participants (720 LQTS patients; 543 controls) were available [median age 34 (inter-quartile range 35) years, 55% women]. Comparing cohort means, automatic and manual QT-intervals and QTc were similar. However, pairwise Bland-Altman-based agreement was highly discrepant. For QT-interval, LoAs spanned 95 (tangent) and 92 ms (threshold), respectively. For QTc, the spread was 108 and 105 ms, respectively. LQTS patients exhibited more pronounced differences. For automatic QTc results from 440-540 ms (tangent) and 430-530 ms (threshold), misassessment risk was highest. Novel automatic QT-interval algorithms may narrow this range. Pairwise vendor-provided automatic and manual QT-interval and QTc results can be highly discrepant. Novel automatic algorithms may improve agreement. Within the above ranges, automatic QT-interval and QTc results require manual confirmation, particularly if T-wave morphology is challenging.

Diagnostic accuracy of the response to the brief tachycardia provoked by standing in children suspected for long QT syndrome.

BackgroundAdult long QT syndrome (LQTS) patients have inadequate corrected QT interval (QTc) shortening and an abnormal T-wave response to the sudden heart rate acceleration provoked by standing. In adults, this knowledge can be used to aid an LQTS diagnosis and, possibly, for risk stratification. However, data on the diagnostic value of the standing test in children are currently limited.ObjectiveTo determine the potential value of the standing test to aid LQTS diagnostics in children.MethodsIn a prospective cohort including children (≤18 years) who had a standing test, comprehensive analyses were performed including manual and automated QT interval assessments and determination of T-wave morphology changes.ResultsWe included 47 LQTS children and 86 control children. At baseline, the QTc that identified LQTS children with a 90% sensitivity was 435 ms, which yielded a 65% specificity. A QTc ≥ 490 ms after standing only slightly increased sensitivity (91%, 95% confidence interval [CI]: 80%–98%) and slightly decreased specificity (58%, 95% CI: 47%–70%). Sensitivity increased slightly more when T-wave abnormalities were present (94%, 95% CI: 82%–99%; specificity 53%, 95% CI: 42%–65%). When a baseline QTc ≥ 440 ms was accompanied by a QTc ≥ 490 ms and T-wave abnormalities after standing, sensitivity further increased (96%, 95% CI: 85%–99%) at the expense of a further specificity decrease (41%, 95% CI: 30%–52%). Beat-to-beat analysis showed that 30 seconds after standing, LQTS children had a greater increase in heart rate compared to controls, which was more evidently present in LQTS boys and LQTS type 1 children.ConclusionIn children, the standing test has limited additive diagnostic value for LQTS over a baseline electrocardiogram, while T-wave abnormalities after standing also have limited additional value. The standing test for LQTS should only be used with caution in children.

The Clinical Utility of Continuous QT Interval Monitoring in Patients Admitted With COVID-19 Compared With Standard of Care: A Prospective Cohort Study

BackgroundQT interval monitoring has gained much interest during the COVID-19 pandemic because of the use of QT-prolonging medications and the concern about viral transmission with serial electrocardiograms (ECGs). We hypothesized that continuous telemetry-based QT monitoring is associated with better detection of prolonged QT episodes. MethodsWe introduced continuous cardiac telemetry (CCT) with an algorithm for automated QT interval monitoring to our designated COVID-19 units. The daily maximum automated heart rate-corrected QT (Auto-QTc) measurements were recorded. We compared the proportion of marked QTc prolongation (Long-QTc) episodes, defined as QTc ≥ 500 ms, in patients with suspected or confirmed COVID-19 who were admitted before and after CCT was implemented (control group vs CCT group, respectively). Manual QTc measurement by electrophysiologists was used to verify Auto-QTc. Charts were reviewed to describe the clinical response to Long-QTc episodes. ResultsWe included 33 consecutive patients (total of 451 monitoring days). Long-QTc episodes were detected more frequently in the CCT group (69/206 [34%] vs 26/245 [11%]; P < 0.0001) and ECGs were performed less frequently (32/206 [16%] vs 78/245 [32%]; P < 0.0001). Auto-QTc correlated well with QTc measurement by electrophysiologists with an excellent agreement in detecting Long-QTc (κ = 0.8; P < 0.008). Only 28% of patients with Long-QTc episodes were treated with recommended therapies. There was 1 episode of torsade de pointes in the control group and none in the CCT group. ConclusionsContinuous QT interval monitoring is superior to standard of care in detecting episodes of Long-QTc with minimal need for ECGs. The clinical response to Long-QTc episodes is suboptimal.

Automated QT Interval Measurement Using Modified Pan-Tompkins Algorithm with Independent Isoelectric Line Approach

The QT interval on the electrocardiogram (ECG) signal is known to have an important role in monitoring heart’s electrical activity because the presence of QT interval prolongation can be associated with life-threatening cardiac events. This interval can be identified and measured using either manual or automated techniques. Currently, studies on automated QT interval measurement algorithms are becoming a growing field, as they can provide the best solution to overcome misdiagnosis and timely issues resulting from manual identification. However, the physiological variability of the QRS complex and the fluctuation of the isoelectric line are prevalent issues that need to be considered in the automatic method. In this report, an algorithm to identify the QRS onset and T-wave offset for measuring the corrected QT interval (QTc) is proposed. This method uses an improved Pan-Tompkins algorithm from the previous work with independent of the isoelectric line approach for detecting the QRS onset and the T offset. The algorithm was implemented in Matlab and applied to the 60 seconds duration of 27 records in the PPUKM database with a sampling frequency of 500 Hz. The performance of the algorithm achieved a sensitivity of 100% for QRS onset detection and 100% for T offset detection. As for the accuracy, the algorithm’s performance obtained 100% for QRS onset detection and 99.56% for T offset detection. The mean error results with respect to manual annotation were 37±18.5 ms for QRS onset detection and 32±22.3 ms for T offset detection which was within ANSI/AAMI-EC57:1998 standard tolerance. The proposed algorithm exhibits reliable automated QTc measurement. Besides insensitive to morphological variations of ECG waves, the computational method is simple and possibly implemented as the basis for future software development for portable device applications.

QT dynamics evaluated on fully automated QT measurement in children.

The association between QT interval and heart rate (HR), QT dynamics, has been reported to vary greatly between individuals. We investigated QT dynamics using fully automated QT interval measurement with a commercially available multifunctional electrocardiogram (ECG) recorder. The study group included 17 otherwise healthy subjects (mean age, 12.7 years; 11 girls, 6 boys), who were undergoing exercise test for arrhythmia evaluation. The subjects had single premature ventricular contraction. Mean corrected QT interval (Fridericia) at rest was 412 ± 19 ms (male, 408 ± 20 ms; female, 414 ± 19 ms), manually measured with a digitizer, using the tangent method. We assessed QT interval/HR slope during the whole exercise test, the exercise phase, and the recovery from exercise phase; the slopes were calculated offline after excluding wide QRS complex and were -1.15 ± 0.26 (r(2) = 0.65), -1.18 ± 0.30 (r(2) = 0.62), and -1.11 ± 0.25 (r(2) = 0.70), respectively. Estimated QT interval at HR 60 beats/min (QT60) was calculated using the regression line formula and was 383 ± 24 ms, 387 ± 28 ms, and 375 ± 21 ms, respectively. QT60 of the total exercise test correlated with the corrected QT interval at rest (P = 0.04, r(2) = 0.25). Assessment of QT dynamics is feasible on fully automated QT interval measurement with this ECG recorder. Further studies are required in larger populations to confirm the accuracy and precision of QT measurement and QT dynamics using this new multifunctional ECG.

Study on corrected QT interval before and after haemodialysis among patients on beta blocker therapy

Introduction : In recent days patients with end stage renal disease on maintenance hemodialysis is emerging.In the conventional ECG the prolonged QT interval has been associated with arrhythmogenesis in a number of cardiac disorders.It has been documented that dialysis patients exhibit sustained activation of the sympathetic nervous system and that the diseased kidney themselves are the trigger of this overactivity 106,107 .The sympathetic over activity is associated with mortality and worse cardiovascular outcomes. 19 So the study to assess the effect of beta blockers in chronic renal failure patients using QT interval is taken up. Materials & methods : A cross sectional hospital based study was done on fifty adult patients on hemodialysis for more than three months at associate hospitals of Kasturba Medical College, Mangalore.Informed consent was taken from them.Twelve-lead electrocardiographs were performed at 10mm/mv and 25mm/s using software based digital ECG recorder (Schiller Healthcare India Pvt Ltd.)10 minutes before and 10 min after a single hemodialysis session. The automatic QT interval was obtained. Statistical analysis was done using Students t-test and chi-square test. P value of ?0.05 was considered significant. Discussion : This study demonstrates that QT interval increases significantly after hemodialysis.There was no significant differences in QT dispersion in end stage renal disease patients on beta blockers and those patients not on beta blockers in our study.However, long term clinical trials are desperately needed to evaluate the safety and efficacy of beta blockers in chronic dialysis patients.

High dose droperidol and QT prolongation: analysis of continuous 12‐lead recordings

AimsTo investigate the QT interval after high dose droperidol using continuous 12-lead Holter recordings.MethodsThis was a prospective study of patients given droperidol with a continuous Holter recording. Patients were recruited from the DORM II study which included patients with aggression presenting to the emergency department. Patients initially received 10 mg droperidol as part of a standardized sedation protocol. An additional 10 mg dose was given after 15 min if required and further doses at the clinical toxicologist's discretion. Continuous 12-lead Holter recordings were obtained for 2–24 h utilizing high resolution digital recordings with automated QT interval measurement. Electrocardiograms were extracted hourly from Holter recordings. The QT interval was plotted against heart rate (HR) on the QT nomogram to determine if it was abnormal. QTcF (Fridericia's HR correction) was calculated and >500 ms was defined as abnormal.ResultsForty-six patients had Holter recordings after 10–40 mg droperidol and 316 QT–HR pairs were included. There were 32 abnormal QT measurements in four patients, three given 10 mg and one 20 mg. In three of the four patients QTcF >500 ms but only in one taking methadone was the timing of QTcF >500 ms consistent with droperidol dosing. Of the three other patients, one took amphetamines, one still had QT prolongation 24 h after droperidol and one took a lamotrigine overdose. No patient given >30 mg had a prolonged QT. There were no arrhythmias.ConclusionQT prolongation was observed with high dose droperidol. However, there was little evidence supporting droperidol being the cause and QT prolongation was more likely due to pre-existing conditions or other drugs.

Measurement and Rate Correction of the QT Interval

Measurement and Rate Correction of the QT Interval

Assessing QT Interval Prolongation and its Associated Risks with Antipsychotics

Several antipsychotics are associated with the ventricular tachycardia torsade de pointes (TdP), which may lead to sudden cardiac death (SCD), because of their inhibition of the cardiac delayed potassium rectifier channel. This inhibition extends the repolarization process of the ventricles of the heart, illustrated as a prolongation of the QT interval on a surface ECG. SCD in individuals receiving antipsychotics has an incidence of approximately 15 cases per 10,000 years of drug exposure but the exact association with TdP remains unknown because the diagnosis of TdP is uncertain. Most patients manifesting antipsychotic-associated TdP and subsequently SCD have well established risk factors for SCD, i.e. older age, female gender, hypokalaemia and cardiovascular disease. QT interval prolongation is the most widely used surrogate marker for assessing the risk of TdP but it is considered somewhat imprecise, partly because QT interval changes are subject to measurement error. In particular, drug-induced T-wave changes (e.g. flattening of the T-wave) may complicate the measurement of the QT interval. Furthermore, the QT interval depends on the heart rate and a corrected QT (QTc) interval is often used to compensate for this. Several correction formulas have been suggested, with Bazett's formula the most widely used. However, Bazett's formula overcorrects at a heart rate above 80 beats per minute and, therefore, Fridericia's formula is considered more appropriate to use in these cases. Several other surrogate markers for TdP have been developed but none of them is clinically implemented yet and QT interval prolongation is still considered the most valid surrogate marker. Although automated QT interval determination may offer some assistance, QT interval determination is best performed by a cardiologist skilled in its measurement. A QT interval >500 ms markedly increases the risk for TdP and SCD, and should lead to discontinuation of the offending drug and, if present, correction of underlying electrolyte disturbances, particularly serum potassium and magnesium derangements. Before prescribing antipsychotics that may increase the QTc interval, the clinician should ask about family and personal history of SCD, presyncope, syncope and cardiac arrhythmias, and recommend cardiology consultation if history is positive.

Difference in automated QT interval measurements in Holter electrocardiograms recorded at sampling rates of 180 and 1000 Hz: effects of up-/down-sampling of the digital electrocardiogram signal

Difference in automated QT interval measurements in Holter electrocardiograms recorded at sampling rates of 180 and 1000 Hz: effects of up-/down-sampling of the digital electrocardiogram signal

Improvement and limitation of the reliability of automated QT measurement by recent algorithms

Background Newer algorithms for automated QT interval measurements may be more reliable than previous algorithms. Objective This study compares Bazett-corrected QTc obtained by an older algorithm (Old12SL) and by 2 newer ones (New12SL and v3.19) to semiautomated measurement performed by experienced cardiologists. Methods A total of 6105 randomly selected electrocardiograms were classified by the cardiologists as normal (4227), borderline (1254), abnormal (575), or not analyzable (49). Errors of automated measurement were defined by more than 30 milliseconds of absolute difference between Bazett-corrected QTc obtained by automated algorithms and semiautomated measurement. Results The Old12SL had approximately twice as many errors (5.25%) as the New12SL (2.33%) and v3.19 (2.30%), P < .0001. Abnormal tracings resulted in more errors than did normal ones (Old12SL: 16.52% vs 3.45%, New12SL: 7.30% vs 1.51%, and v3.19: 10.61% vs 1.21%). Conclusion Newer automated algorithms for QT measurements are highly reliable in normal tracings. However, electrocardiogram abnormalities increase the risk of QT measurement errors.

Modeling the Effect of Sevoflurane on Corrected QT Prolongation

Sevoflurane may prolong the corrected QT (QTc) interval in healthy humans when administered for induction and maintenance of anesthesia. Little information is available about the dose-response relationship of sevoflurane on the QTc interval. We performed a pharmacodynamic analysis of the relationship between end-tidal sevoflurane concentration (CET) and the QTc. Twenty-one patients aged 20-50 yr were enrolled in this study. Sevoflurane concentrations were progressively increased and then decreased over 15 min at the start of anesthesia; CET and automated QT interval were recorded continuously. Pharmacodynamic analysis using a sigmoid Emax model was performed to assess the concentration-effect relationship. Maximal CET was 4.30 ± 0.33%. Measured baseline and maximally prolonged QTc interval values were 351.7 ± 15.4 ms and 397.8 ± 17.5 ms, respectively. During sevoflurane anesthesia, increased concentrations were correlated with prolonged QTc interval. Hysteresis between the CET and QTc interval were observed and accounted for in the model. Ce50 and ke0 were 2.5 ± 1.4 and 2.0 ± 1.0, respectively. The median prediction error, median absolute prediction error, and the coefficient of determination (R) were 0.02%, 0.75%, and 0.95, respectively. The effect-site concentration (Ce50) and QTc interval data fit to a sigmoid Emax model. Among patients receiving sevoflurane for anesthesia, QTc interval changes correlate to anesthetic level. The Ce50 for significant QTc change is at clinically relevant levels of sevoflurane anesthesia.

Clinical diagnosis of long QT syndrome: back to the caliper

In the last 10 years, the management of cardiac arrhythmias has evolved to an impressive pace, thanks to the development of highly sophisticated technologies for diagnosis and treatment. Mapping of the site of origin of arrhythmias, ablation, and implant of defibrillators have reduced the importance of the traditional approaches based on electrocardiographic reading and prescription of drugs. In this context, inherited arrhythmogenic diseases such as long QT syndrome (LQTS), catecholaminergic polymorphic ventricular tachycardia, short QT syndrome, and Brugada syndrome represent the exception in which the ECG has remained the pivotal diagnostic tool. When approaching the diagnosis of one of these diseases, the cardiologist has to leave fancy computer screens and go back to the ‘ruler and caliper’ to measure the duration of an ‘interval’ or the elevation of an electrocardiographic ‘segment’. This apparently simple diagnosis, however, is far from being easy and even ‘experts’ in the field may face substantial diagnostic dilemma. The diagnosis of the LQTS, for example, ‘simply’ requires the measurement of QT interval duration on a standard ECG recording and the correction of the duration for ‘heart rate’ using the Bazett's formula. In real-life, however, the diagnosis is far from being straightforward and it is often more elusive than anticipated for a variety of reasons that encompass the difficulty to define the ‘end’ of the T-wave, the lack of linear behaviour of the Bazett's formula at slower and faster heart rates, and the arbitrary definition of the gender-based diagnostic cut off values that define an abnormally prolonged QTc (QTc >440 for males and QTc >460 for females).1 A large body of literature has been dedicated to the discussion of limitations of manual and automatic QT interval measurement2,3 thus promoting the … *Corresponding author. fax: +39 0382 592059. E-mail address : spriori{at}fsm.it

Precision of QT Interval Measurement by Advanced Electrocardiographic Equipment

The costs of clinical investigations of drug-induced QT interval prolongation are mainly related to manual processing of electrocardiographic (ECG) recordings. Potentially, however, these costs can be decreased by automatic ECG measurement. To investigate the improvements in measurement accuracy of the modern ECG equipment, this study investigated QT interval measurement by the "old" and "new" versions of the 12SL ECG algorithm by GE Healthcare (Milwaukee, WI, USA) and compared the results to carefully validated and reconciled manual measurements. The investigation used two sets (A and B) of ECG recordings that originated from large clinical studies. Sets A and B consisted of 15,194, and 29,866 10-second ECG recordings, respectively. All the recordings were obtained with GE Healthcare recorders and were available in digital format compatible with ECG processing software by GE Healthcare. The two sets of recordings differed significantly in ECG quality with set B being substantially more noise polluted. Compared to careful manual QT interval readings in recording set A, the errors of the automatic QT interval measurement were (mean +/- SD) +3.95 +/- 5.50 ms, and +0.51 +/- 12.41 ms for the "new" and "old" 12SL algorithm, respectively. In recording set B, these numbers were +2.41 +/- 9.47 ms, and -0.17 +/- 14.89 ms, respectively (both differences were highly statistically significant, P < 0.000001). In recording set A, 95.9% and 76.6% of ECGs were measured automatically within 10 ms of the manual measurement by the "new" and "old" versions of the 12SL algorithm, In recording set B, these numbers were 83.9% and 59.5%. The errors made by the "new" and "old" version of 12SL algorithm were practically independent each of the other (correlation coefficients of 0.031 and 0.281 in recording sets A and B, respectively). The study shows that (a) compared to the "old" version of the 12SL algorithm, the QT interval measurement by the "new" version implemented in the most recent ECG equipment by GE Healthcare is significantly better, and (b) the precision of automatic measurement by the 12SL algorithm is substantially dependent on the quality of processed ECG recordings. The improved accuracy of the "new" 12SL algorithm makes it feasible to use modern ECG equipment without any manual intervention in selected parts of drug-development program.

QT Interval Measurement

Assessment of repolarization duration is often recommended to avoid administration of QT-prolonging drugs in patients with prolonged QTc interval, a frequent situation in the postoperative period. Bazett QT correction inappropriately increases QTc when heart rate is increased, and the use of the Fridericia formula may avoid a falsely prolonged QTc interval. The authors assessed automatic QT interval measurement to detect prolonged QTc interval (women >450 ms; men >440 ms) in the postoperative setting. Automatic and manual electrocardiograms were performed in 108 patients after anesthesia. Automatic electrocardiographic measurement used the Bazett formula. Manual measurements were made from each electrocardiogram and used as the reference. Agreement between the two methods was analyzed. Bazett and Fridericia QT corrections were compared in this population. Agreement between automatic and manual measurements was low. The Fridericia correction, but not the Bazett correction, was independent from heart rate and allowed adequate QT correction. Sensitivity of automatic measurements to detect prolonged QTc-Bazett interval was 54%. Automatic QTc-Bazett interval less than 430 ms ruled out a manual prolonged QTc interval. When automatic QTc-Bazett was greater than 430 ms, this value was converted according to Fridericia. Automatic QTc-Fridericia greater than 430 ms identified all patients with prolonged manual QTc with a negative predictive error of 0% (95% confidence interval, 0-7%). QTc-Fridericia can be approximated by respectively adding or subtracting 5% to the uncorrected QT for each increase or decrease by 10 beats/min in heart rate from 60 beats/min. Automatic QTc-Bazett measurement, if abnormal, associated with calculation of QTc-Fridericia reliably identifies patients in whom manual QTc measurement must be performed to confirm postoperative prolonged QTc interval.

Accuracy of popular automatic QT Interval algorithms assessed by a 'Gold Standard' and comparison with a Novel method: computer simulation study

BackgroundAccurate measurement of the QT interval is very important from a clinical and pharmaceutical drug safety screening perspective. Expert manual measurement is both imprecise and imperfectly reproducible, yet it is used as the reference standard to assess the accuracy of current automatic computer algorithms, which thus produce reproducible but incorrect measurements of the QT interval. There is a scientific imperative to evaluate the most commonly used algorithms with an accurate and objective 'gold standard' and investigate novel automatic algorithms if the commonly used algorithms are found to be deficient.MethodsThis study uses a validated computer simulation of 8 different noise contaminated ECG waveforms (with known QT intervals of 461 and 495 ms), generated from a cell array using Luo-Rudy membrane kinetics and the Crank-Nicholson method, as a reference standard to assess the accuracy of commonly used QT measurement algorithms. Each ECG contaminated with 39 mixtures of noise at 3 levels of intensity was first filtered then subjected to three threshold methods (T1, T2, T3), two T wave slope methods (S1, S2) and a Novel method. The reproducibility and accuracy of each algorithm was compared for each ECG.ResultsThe coefficient of variation for methods T1, T2, T3, S1, S2 and Novel were 0.36, 0.23, 1.9, 0.93, 0.92 and 0.62 respectively. For ECGs of real QT interval 461 ms the methods T1, T2, T3, S1, S2 and Novel calculated the mean QT intervals(standard deviations) to be 379.4(1.29), 368.5(0.8), 401.3(8.4), 358.9(4.8), 381.5(4.6) and 464(4.9) ms respectively. For ECGs of real QT interval 495 ms the methods T1, T2, T3, S1, S2 and Novel calculated the mean QT intervals(standard deviations) to be 396.9(1.7), 387.2(0.97), 424.9(8.7), 386.7(2.2), 396.8(2.8) and 493(0.97) ms respectively. These results showed significant differences between means at >95% confidence level. Shifting ECG baselines caused large errors of QT interval with T1 and T2 but no error with Novel.ConclusionThe algorithms T2, T1 and Novel gave low coefficients of variation for QT measurement. The Novel technique gave the most accurate measurement of QT interval, T3 (a differential threshold method) was the next most accurate by a large margin. The objective and accurate 'gold standard' presented in this paper may be useful to assess new QT measurement algorithms. The Novel algorithm may prove to be more accurate and reliable method to measure the QT interval.

Development of Automated 12-Lead QT Dispersion Algorithm for Sudden Cardiac Death

A single global QT interval measurement from the 12 lead ECG has been the standard measure, but recently there has been a great interest in the distribution of the QT intervals, at a given instant of time, across the 12 lead Electrocardiogram (ECG) leads. This parameter is called as QT dispersion (QTd). The QTd is emerging as an important new clinic tool as it has proposed as a marker of the risk of sudden cardiac death after Myocardium Infarction (MI). However, the measured QTd values vary among different studies around the world in term of the mean and the standard deviation. This research is aimed to develop an automatic algorithm to compute QTd for patients in Malaysia. DSP techniques were applied to 12 lead ECG signals to develop algorithm to facilitate offline automatic QT interval analysis. The results are statistically represented in terms of the mean and standard deviation, SD (mean ± SD). A threshold determined from Gaussian probability distribution function (pdf) is used to evaluate the significance difference of QTd between the non-MI patients and MI patients. The characteristic function is used to justify the discrimination of QTd between the non-MI and MI patients. A total number of 432 ECG recordings (36 patients X 12 leads per patient) were analyzed. It is found that the mean value of QTd for non-MI and MI group is 37.28 ± 11.13ms (p<0.05) and 66.17 ± 13.95ms (p<0.05) respectively. The QTd for non-MI and MI have the sensitivity of 88.89%. The threshold found is 50ms. Thus, QTd index is a clinically useful diagnostic adjunct in discriminating between non-MI patients and patients with MI. In conclusion, the QTd values are significantly higher in patients with MI compared to non-MI patients.

Development of a new QT algorithm with heterogenous ECG databases

An algorithm for automated QT interval assessments has been developed and evaluated using the PhysioNet QT database and the electrocardiogram multilead database (2 collections of electrocardiograms with different characteristics, eg, numbers of leads and expert annotations). QRS onset and coarse T offset detection was based on the definition of a short time window, within which the range of signal amplitudes was calculated and compared to given threshold values. The final position of T offset was based on a combination of 3 methods: decreasing thresholds, multiple tangents, and a model based approach. The evaluation was based on the comparison of a waveform marker as computed automatically, and those of the human experts. Mean and standard deviation of those differences compared well to other algorithms and to inter-expert variations. Waveform marker detection was successful in at least 98% of the annotated beats in both databases, thus, indicating the robustness of the proposed method.

Agreement and Reproducibility of Automatic Versus Manual Measurement of QT Interval and QT Dispersion

To determine whether the automatic measurement of the QT interval is consistent with the manual measurement, this study evaluated the reproducibility and agreement of both methods in 70 normal subjects and 54 patients with hypertrophic cardiomyopathy. The mean, minimum, and maximum QT interval and QT dispersion were computed in a set of 6 consecutive electrocardiograms (3 in the supine and 3 in the standing position) obtained from each subject. The automatic method determined the T-wave end as the intersect of the least-squares-fit line around the tangent to the T-wave downslope with the isoelectric baseline. Manual measurements were obtained using a high-resolution digitizing board. QT dispersion was defined as the difference between the maximum and minimum QT interval and as standard deviations of the QT interval duration in all and precordial leads. In patients with hypertrophic cardiomyopathy, the absolute values of the QT interval and QT dispersion were significantly higher than those in normal subjects (p <0.0001). In both groups, the intrasubject variability of the QT interval was significantly lower with automatic than with manual measurement (p <0.05). The agreement between automatic and manual QT interval measurements was surprisingly poor, but it was better in patients with hypertrophic cardiomyopathy (r 2 = 0.46 to 0.67) than in normal subjects (r 2 = 0.10 to 0.25). In both groups, the reproducibility and agreement of both methods for QT dispersion were significantly poorer than for QT interval. Hence, the automatic QT interval measurements are more stable and reproducible than manual measurement, but the lack of agreement between manual and automatic measurement suggests that clinical experience gained with manual assessment cannot be applied blindly to data obtained from the automatic systems.

Automated measurement of QT interval dispersion from hard-copy ECGs

Increased “dispersion” of the QT interval of the electrocardiogram has been proposed as a marker for increased risk of cardiac arrhythmias, but definitive identification of its independent predictive value requires accurate and reproducible measurement in large numbers of cases. A personal computer-based technique for (1) converting hard-copy electrocardiograms to digital records and (2) automatically measuring QT interval dispersion from the digitized records has been developed and validated. Hand measurements of the RR interval from the original tracing and cursor or automated measurements from digitized waveforms correlated to within 1%. QT intervals measured by cursor on digitized waveforms were a mean of 14 ms (95% confidence interval, 10–19 ms) longer than manual measurements on original tracings. Automatic QT interval measurements were a mean of 5 ms longer than cursor measurements (95% confidence interval, 3–7 ms). Automated measurements were observer independent and repeatable (coefficient of variation for repeat measurements, 0.137% RR and 0.370% QT). Estimates of QT dispersion (expressed as coefficient of interlead QT variation) were made for 14 patients with documented recurrent ventricular and 15 control subjects. The median coefficient of interlead QT variation was 8.8% (range, 4.4–12.4%) for arrhythmia patients and 3.6% (range, 2.7–6.3%) for the control group ( P < .001). The automatic measurements were more conservative and less likely to give spuriously large values for QT dispersion than manual measurements. Automated QT dispersion measurements should facilitate future studies on predicting the risk of ventricular arrhythmias.