Dirk Q. Feild

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.

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.

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.

Limitations on the Re-use of patient specific coefficients for 12-lead ECG reconstruction

Patient specific coefficients for reconstructing missing precordial leads (patient-specific single-use or PSS) show good performance but require a 12-lead ECG to start monitoring. A more convenient approach is either the use of population based coefficients (POP) or patient specific coefficients from an old 12-lead ECG (patient-specific multi-use or PSM). We used a data set of 1493 resting 12-lead ECGs from 224 patients. Waveform comparisons were made between recorded 12-lead and reconstructed cases using RMS difference. Three cases were compared, PSS, PSM and POP. Median RMS reconstruction error in the ST-T region was 16, 46 and 40 muV for lead configuration V1/V4 in the PSS, PSM and POP cases respectively. For the V2/V5 configuration, median ST-T RMS error was 8, 40 and 41 muV. The RMS error for the PSS case was lower and significantly better by paired T-test. The difference between the two more convenient use-models, PSM and POP, was not significant. Population based coefficients are preferred over patient-specific coefficients if the single-use use-model cannot be followed.

A novel heart rate variability index for evaluation of left ventricular function using five-minute electrocardiogram

In this paper, we introduce a new index based on the frequency-domain analysis of heart rate variability, or more precisely, the power spectrum of the instant heart rate signal. This index, called VHFI, is defined as the very high frequency component of the power spectrum normalized to represent its relative value in proportion to the total power minus the very low frequency component. We tested VHFI on patients with known reduced left ventricular function and found that this index has the potential to be a useful tool for quick evaluation of left ventricular function.

Comparison of two automated methods for QT interval measurement

In this paper we compared two methods of automated QT interval measurement on standard ECG databases: the Root-Mean-Square (RMS) lead combining method aimed at QT monitoring and the method of median of lead-by-lead QT interval measurements. We used the PhysioNet PTB (N=548) and CSE measurement (N=125) standard databases. Both have reference QT interval measurements from a group of annotators. The last 10 seconds of each PTB record was downsampled from 1000 sample per second (sps) and an amplitude resolution of 1 muV to 500 sps and 5 muV in order to match the CSE set. PTB records #205 and #557 were excluded due to ventricular paced rhythm and artifact, respectively. Twenty five cases were excluded from the CSE set to match the selection of cases for IEC algorithm testing (IEC 60601-2-51). We processed all records using the Philips resting 12- lead ECG algorithm to generate representative beats for QT interval measurement. The RMS method measures QRS onset and end of Ton an RMS waveform constructed from 9 leads I, II, III and V1-V6. The lead-by-lead method takes the median QT interval across leads. The automated QT intervals by the RMS and lead-by-lead methods were compared to the reference manual QT measurements. The mean difference between the lead-by-lead QT and the reference QT was 1.7plusmn9.7 ms and 12.4plusmn23.0 ms (mean plusmnstandard deviation (SD)) for the CSE and PTB sets respectively. For the RMS method, the mean difference was -2.8plusmn11.1 ms and 10.3plusmn20.9 ms. F-tests indicate that the standard deviation between methods is not significantly different for the CSE set (P=0.18) or the PTB set (P=0.77). The lead-by-lead and RMS methods perform similarly, leading to the conclusion that the choice between them should be based on considerations such as the number of leads available or computational efficiency.

Automatic detection of ECG cable interchange by analyzing both morphology and interlead relations.

BACKGROUND ECG cable interchange can generate erroneous diagnoses. For algorithms detecting ECG cable interchange, high specificity is required to maintain a low total false positive rate because the prevalence of interchange is low. In this study, we propose and evaluate an improved algorithm for automatic detection and classification of ECG cable interchange. METHOD The algorithm was developed by using both ECG morphology information and redundancy information. ECG morphology features included QRS-T and P-wave amplitude, frontal axis and clockwise vector loop rotation. The redundancy features were derived based on the EASI™ lead system transformation. The classification was implemented using linear support vector machine. The development database came from multiple sources including both normal subjects and cardiac patients. An independent database was used to test the algorithm performance. Common cable interchanges were simulated by swapping either limb cables or precordial cables. RESULTS For the whole validation database, the overall sensitivity and specificity for detecting precordial cable interchange were 56.5% and 99.9%, and the sensitivity and specificity for detecting limb cable interchange (excluding left arm-left leg interchange) were 93.8% and 99.9%. Defining precordial cable interchange or limb cable interchange as a single positive event, the total false positive rate was 0.7%. When the algorithm was designed for higher sensitivity, the sensitivity for detecting precordial cable interchange increased to 74.6% and the total false positive rate increased to 2.7%, while the sensitivity for detecting limb cable interchange was maintained at 93.8%. The low total false positive rate was maintained at 0.6% for the more abnormal subset of the validation database including only hypertrophy and infarction patients. CONCLUSION The proposed algorithm can detect and classify ECG cable interchanges with high specificity and low total false positive rate, at the cost of decreased sensitivity for certain precordial cable interchanges. The algorithm could also be configured for higher sensitivity for different applications where a lower specificity can be tolerated.

Sources of Variability In Qt Calculations

is time consuming and therefore has not been applied in clinical practice. Automation of this score could facilitate clinical application. Therefore, we aimed to develop and validate an automatic algorithm for the AW-score. Methods: The AW-score (obtained from presenting ECG), assesses changes in ST-T-segments, T-waves and Q-waves. Each lead is designated an acuteness phase (1A, 1B, 2A or 2B) and the overall score is calculated. AW-score ranges from 1 (late ischemia/least acute) to 4 (early ischemia/most acute) and is calculated from the formula: AW-score = [(4 × (#leads 1A) + 3 × (#leads 1B) + 2 × (#leads 2A) + 1 × (#leads 2B))/(∑#leads with 1A, 1B, 2A or 2B)]. We developed an algorithm to automatically determine AW-score. The algorithm was designed using 50 ECGs. Each ECG lead (except aVR) was manually scored according to AW-score by two independent experts (Exp1 and Exp2) and automatically by our designed algorithm (auto-score). An adjudicated manual score (Adj-score) was determined between Exp1 and Exp2. The inter-rater reliabilities (IRRs) between Exp1 vs Exp2, and Adj-score vs auto-score were assessed by interclass correlation coefficient (ICC). Results: The Adj-score and auto-score had median AW-score 2.7 (1.75–3.52) and 3.0 (2.32–3.80), respectively. The IRR forAW-score betweenAdj-score and auto-score was ICC = 0.64 (CI 0.36–0.80), p b 0.001). Substantial differences in AW-score between Adj-score and auto-score were due to difference in measures of Q-wave duration. The IRR for AW-score between Exp1 and Exp2 was ICC = 0.89 (confidence interval (CI) 0.79–0.93, p b 0.0001). Conclusion: We have developed an automatic algorithm for measurement of AW-score. The preliminary test result was near acceptable for the inter-rater reliability between manual Adj-score and auto-score. More adjustments are needed to improve the measure of agreements between manual score and automatic algorithm for AW-score.