Simulating Arbitrary Electrode Reversals in Standard 12-lead ECG (original) (raw)
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Inter-lead correlation analysis for automated detection of cable reversals in 12/16-lead ECG
Computer Methods and Programs in Biomedicine, 2016
Background and objective: A crucial factor for proper electrocardiogram (ECG) interpretation is the correct electrode placement in standard 12-lead ECG and extended 16-lead ECG for accurate diagnosis of acute myocardial infarctions. In the context of optimal patient care, we present and evaluate a new method for automated detection of reversals in peripheral and precordial (standard, right and posterior) leads, based on simple rules with inter-lead correlation dependencies. Methods: The algorithm for analysis of cable reversals relies on scoring of inter-lead correlations estimated over 4s snapshots with time-coherent data from multiple ECG leads. Peripheral cable reversals are detected by assessment of nine correlation coefficients, comparing V6 to limb leads: (I, II, III,-I,-II,-III,-aVR,-aVL,-aVF). Precordial lead reversals are detected by analysis of the ECG pattern cross-correlation progression within lead sets (V1-V6), (V4R, V3R, V3, V4), and (V4, V5, V6, V8, V9). Disturbed progression identifies the swapped leads. Results: A test-set, including 2239 ECGs from three independent sources-public 12-lead (PTB, CSE) and proprietary 16-lead (Basel University Hospital) databases-is used for algorithm validation, reporting specificity (Sp) and sensitivity (Se) as true negative and true positive detection of simulated lead swaps. Reversals of limb leads are detected with Se = 95.5-96.9% and 100% when right leg is involved in the reversal. Among all 15 possible pairwise reversals in standard precordial leads, adjacent lead reversals are detected with Se = 93.8% (V5-V6), 95.6% (V2-V3), 95.9% (V3-V4), 97.1% (V1-V2), and 97.8% (V4-V5), increasing to 97.8-99.8% for reversals of anatomically more distant electrodes. The pairwise reversals in the four extra precordial leads are detected with Se = 74.7% (right-sided V4R-V3R), 91.4% (posterior V8-V9), 93.7% (V4R-V9), and 97.7% (V4R-V8, V3R-V9, V3R-V8). Higher true negative rate is achieved with Sp > 99% (standard 12-lead ECG), 81.9% (V4R-V3R), 91.4% (V8-V9), and 100% (V4R-V9, V4R-V8, V3R-V9, V3R-V8), which is reasonable considering the low prevalence of lead swaps in clinical environment. Conclusions: Inter-lead correlation analysis is able to provide robust detection of cable reversals in standard 12-lead ECG, effectively extended to 16-lead ECG applications that have not previously been addressed.
Detection of Unicolor ECG Electrode Reversals in Standard 12-Lead ECG
2018 Computing in Cardiology Conference (CinC), 2018
This paper presents the performance of a commercial lead quality monitoring library (LQMLib, Schiller AG) for detection of reversals between 12-lead ECG cables with matching colors and proposes methods for improvements, where necessary. The study is performed on a large 12-lead ECG database with 1331 chest pain patients (646 training, 685 test recordings) and ECGs from 29 volunteers with swapped red, yellow, green, black and all unicolor cables. Relying on assessment of inter-lead correlations over continuous 4s episodes, LQMLib without adjustments achieves Se>97% for all unicolor cable reversals, except RC1 (Se=29%) and NC5 (Se=88%). The proposed additional analyses improve RC1 and NC5 swap detection by 63% points and 7% points, respectively.
Use of the standard 12-lead ECG to simulate electrode displacements
Journal of Electrocardiology, 1996
Placement of the precordial electrodes for recording a 12-1ead electrocardiogram (ECG) is subject to variation. Previous research has shown that displacements, especially in the longitudinal direction, can lead to changes in diagnosis. In practice, both the displacement and the effects of displacement on an individual ECG are unknown. To assess this effect for a given ECG, the authors developed a method to simulate ECGs at different displacements using only the recorded ECG. The material consisted of 746 body surface potential maps (BSPMs) containing 232 cases without abnormalities, 277 with myocardial infarction (MI), and 237 with left ventricular hypertrophy. By interpolating BSPMs, ECGs from closely spaced electrode positions could be derived. Taking electrode positioning errors that may be encountered in practice, 40 ECGs at different electrode displacements (displaced ECGs) for each BSPM were derived. Using half of the BSPMs, for each displacement, a transformation matrix that transforms the ECG at the standard 12-lead electrode positions (standard ECG) to the displaced ECG was determined. Using the other half of the BSPMs, each displaced ECG was compared with the ECG yielded by the corresponding transformation matrix (transformed ECG). For each comparison, the differences were assessed between the two sets of ECG signals and between the diagnostic computer classifications of the two sets. Signal differences were expressed as mean absolute amplitude differences over the QRS. Computer interpretation of MI and left ventricular hypertrophy was graded in five levels of certainty (no, consider, possible, probable, definite). For instance, for the largest longitudinal displacement studied of about one intercostal space, the 96th percentile mean absolute amplitude difference over the test set was 204 gV. The percentage of cases showing a change in MI classification of more than two certainty levels was 2.7% for this displacement. When comparing the standard ECG with the displaced ECG, these figures were 434 gV and 8.3%, respectively. It is concluded that ECGs from displaced electrodes can be well simulated by transforming the standard ECG, both for the ECG signal and diagnostic classifications.
Detection of electrode interchange in right precordial and posterior ECG leads
2015 Computing in Cardiology Conference (CinC), 2015
This study presents a method for automated detection of misplaced supplementary precordial leads, including the right-sided V3R, V4R and the posterior V8, V9 leads. Considering their uncommon use in clinical routine, a lead reversal is quite probable and could result in erroneous diagnosis and treatment. The method allows real-time implementation by scoring inter-lead crosscorrelations over continuous 4s episodes, scanning the normal progression of PQRST waveforms within leads [V4R, V3R, V3, V4] and [V4, V5, V6, V8, V9]. A large 16-lead ECG database with 1333 chest pain patients is used to test the performance of the method for all possible 23 swaps between the supplementary leads V4R, V3R, V8, V9, assuming correct positions of the standard V1-V6. The sensitivity (Se) for lead reversals is Se=94.1±4.6%, ranged between 78.5% and 97.8%, with the most difficult detection of V3R/V4R swap (Se=78.5%), V4R/V9 swap (Se=83.7%), V8/V9 swap (Se=91.8%). The achieved specificity for the correct lead positions is Sp=83.4%.
Detection of electrode interchange in precordial and orthogonal ECG leads
This study presents methods for automated detection of interchanged precordial and orthogonal ECG leads that may prevent from incorrect diagnosis and treatment. For precordial leads V1-V6, correlation coefficients of QRS-T patterns and time-alignment of R and S-peaks are assessed. For orthogonal leads (X,Y,Z), analysis of QRS loops in the frontal plane, a set of correlation coefficients and a time-alignment of leads are implemented. The methods are elaborated using 15-lead ECG databases - 77 healthy control recordings from PTB database (training), and the total set of 1220 ECGs in CSE database with various arrhythmias (test). The specificity (Sp) for detection of the correct precordial leads configuration (V1 to V6) is 93.5% (training) and 91% (test) and the mean sensitivity (Se) for 23 simulated most common chest electrode swaps is 95.7% (training) and 95% (test). Sp for detection of the correct orthogonal leads X,Y,Z is 98.7% (training) and 93.3% (test), while mean Se for 47 rever...
Journal of Electrocardiology, 2008
The aim of this study was to develop and evaluate transformation coefficients for deriving the standard 12-lead electrocardiogram (ECG), 18-lead ECG (with additional leads V7, V8, V9, V3R, V4R, V5R), and Frank vectorcardiogram (VCG) from reduced lead sets using 3 "limb" electrodes at Mason-Likar torso sites combined with 2 chest electrodes at precordial sites V1 to V6; 15 such lead sets exist and each can be recorded with 6-wire cable. As a study population, we used Dalhousie Superset (n = 892) that includes healthy subjects, postinfarction patients, and patients with a history of ventricular tachycardia. For each subject, 120-lead ECG recordings of 15-second duration were averaged, and all samples of the QRST complex for leads of interest were extracted; these data were used to derive-by regression analysis-general and patient-specific coefficients for lead transformations. These coefficients were then used to predict 12-lead/18-lead ECG sets and 3-lead VCG from 15 reduced lead sets, and the success of these predictions was assessed by 3 goodness-offit measures applied to the entire QRST waveform and to the ST deviation at J point; these 3 measures were similarity coefficient (SC in percentage), relative error (in percentage), and RMS error (in microvolts). Our results show that the best pair for predicting the standard 12-lead ECG by either general coefficients (mean SC = 95.56) or patient-specific coefficients (mean SC = 99.11) is V2 and V4; the best pair for deriving the 18-lead set by general coefficients (mean SC = 93.74) or by patient-specific coefficients (mean SC = 98.71) is V1 and V4; the best pair for deriving the Frank X, Y, Z leads is V1 and V3 for general coefficients (mean SC = 95.76) and V3 and V6 for patientspecific coefficients (mean SC = 99.05). The differences in mean SC among the first 8 to 10 predictor sets in each ranking table are within 1% of the highest SC value. Thus, in conclusion, there are several near-equivalent choices of reduced lead set using 6-wire cable that offer a good prediction of 12-lead/18-lead ECG and VCG; a pair most appropriate for the clinical application can be selected.
Mathematical Modeling and Utility of the Derived 22-LEAD Electrocardiogram
Journal of the American College of Cardiology, 2011
Background: The cardiac electrical field is dipolar and is measured by the electrocardiogram (ECG) which may be described by a 3 lead-vector space. There are 22 leads used in clinical practice including the standard 12-lead ECG, right heart leads V3R-V6R, posterior leads V7-V9, and the vectorcardiographic (VCG) leads X, Y, Z. It would be advantageous to derive these 22 ECG leads from just 3 measured leads using a universal patient coefficient matrix (UPCM) that can be computed using simplex optimization (SOP). The objective is to derive the ECG (dECG) from 3 measured leads using a SOP-computed UPCM and calculate the quantitative and qualitative correlations with the measured ECG (mECG). Methods: A total of 371 mECGs of varying morphology for both men and women age 18 and older were acquired including 371 standard 12-lead ECGs, 353 VCGs, 75 right heart ECGs, and 34 posterior ECGs. The ECG morphologies included normals, acute MIs, LVH, bundle branch blocks, paced beats, PVCs, and non-specific STT types. Each ECG was interpreted by 2 physicians who were blinded reference standards. The SOP technique was used to derive a UPCM from an additional training set of 20 ECGs. Leads I, aVF, and V2 from the mECGs were chosen as the 3 lead-vector basis orthogonal lead set from which the dECGs were synthesized. The derived vs. measured test case ECGs were compared using Pearson and Kappa statistics. Results: The dECGs showed high correlation with mECGs overall by Pearson correlations (0.84-0.88). No clinically significant differences were noted in 98.1% of the dECGs. ECG rate, rhythm, segment, and axis interpretations showed 100% correlation. Acute MI differentiation showed 100% correlation. Kappa analysis of the mECG vs. dECG showed high overall correlations (0.73-1.00). Conclusions: The 22-lead ECG can be derived from just 3 measured leads using the SOP technique. The comparison of the mECGs and dECGs shows high quantitative and qualitative correlations. Using this technology a 22-lead derived ECG can be displayed instantaneously in real-time to enhance patient observation capabilities and will allow for a convenient and cost effective acquisition and analysis of the ECG in telemetry and critical care areas of health care.
Statistical and deterministic approaches to designing transformations of electrocardiographic leads
Journal of Electrocardiology, 2002
Two different approaches can be used to investigate the relationships among electrocardiographic leads: a statistical one, based on the analysis of recorded electrocardiograms (ECGs), and a deterministic one, based on physical principles that govern the current flow in irregularly shaped volume conductors such as the human body. The purpose of this study was to compare these two approaches. For the statistical investigation, the data set consisted of 120-lead ECGs recorded in a population including normal subjects (n ϭ 290), post-myocardial-infarction patients (n ϭ 497), patients with a history of ventricular tachycardia but no evidence of a previous myocardial infarction (n ϭ 105), and patients with a single-vessel coronary artery disease who underwent coronary angioplasty (n ϭ 91). Lead transformations of interest were obtained by fitting the multiple-regression model to this data set by the least-squares method. For the deterministic investigation, we used a boundary-element model of the human torso to simulate body-surface potentials in response to three orthogonal unit dipoles placed consecutively at 1,239 ventricular source locations, and the resulting body-surface potential distributions (instead of the recorded ECGs) were then fitted by the multipleregression model. The results suggest that the lead transformations should be preferably designed by statistical analysis of recorded ECGs. Regression models with a small number of predictors (eg, those based on three ECG leads) are the most reliable; those using more predictors are fraught with the danger of collinearity when predictors are highly correlated (as occurs in the standard 12-lead ECG). Model-derived deterministic transformations are compatible with statistically derived ones, provided that the distributed character of the cardiac sources is taken into account. We conclude that statistical associations among electrocardiographic leads can be reliably quantified in sufficiently large and diverse databases of recorded data; the causality of these associations can be supported by appropriate deterministic models based on the laws of physics.