Ramun Schmid

Intersubject variability and intrasubject reproducibility of 12-lead ECG metrics: Implications for human verification

BACKGROUND Electrocardiogram (ECG) biometrics is an advanced technology, not yet covered by guidelines on criteria, features and leads for maximal authentication accuracy. OBJECTIVE This study aims to define the minimal set of morphological metrics in 12-lead ECG by optimization towards high reliability and security, and validation in a person verification model across a large population. METHODS A standard 12-lead resting ECG database from 574 non-cardiac patients with two remote recordings (>1year apart) was used. A commercial ECG analysis module (Schiller AG) measured 202 morphological features, including lead-specific amplitudes, durations, ST-metrics, and axes. Coefficient of variation (CV, intersubject variability) and percent-mean-absolute-difference (PMAD, intrasubject reproducibility) defined the optimization (PMAD/CV→min) and restriction (CV<30%) criteria for selection of the most stable and distinctive features. Linear discriminant analysis (LDA) validated the non-redundant feature set for person verification. RESULTS AND CONCLUSIONS Maximal LDA verification sensitivity (85.3%) and specificity (86.4%) were validated for 11 optimal features: R-amplitude (I,II,V1,V2,V3,V5), S-amplitude (V1,V2), Tnegative-amplitude (aVR), and R-duration (aVF,V1).

Diagnostic and prognostic values of the V-index, a novel ECG marker quantifying spatial heterogeneity of ventricular repolarization, in patients with symptoms suggestive of non-ST-elevation myocardial infarction

BACKGROUND The V-index is an ECG marker quantifying spatial heterogeneity of ventricular repolarization. We prospectively assessed the diagnostic and prognostic values of the V-index in patients with suspected non-ST-elevation myocardial infarction (NSTEMI). METHODS We prospectively enrolled 497 patients presenting with suspected NSTEMI to the emergency department (ED). Digital 12-lead ECGs of five-minute duration were recorded at presentation. The V-index was automatically calculated in a blinded fashion. Patients with a QRS duration >120ms were ruled out from analysis. The final diagnosis was adjudicated by two independent cardiologists. The prognostic endpoint was all-cause mortality during 24months of follow-up. RESULTS NSTEMI was the final diagnosis in 14% of patients. V-index levels were higher in patients with AMI compared to other causes of chest pain (median 23ms vs. 18ms, p<0.001). The use of the V-index in addition to conventional ECG-criteria improved the diagnostic accuracy for the diagnosis of NSTEMI as quantified by area under the ROC curve from 0.66 to 0.73 (p=0.001) and the sensitivity of the ECG for AMI from 41% to 86% (p<0.001). Cumulative 24-month mortality rates were 99.4%, 98.4% and 88.3% according to tertiles of the V-index (p<0.001). After adjustment for age and important ECG and clinical parameters, the V-index remained an independent predictor of death. CONCLUSIONS The V-index, an ECG marker quantifying spatial heterogeneity of ventricular repolarization, significantly improves the accuracy and sensitivity of the ECG for the diagnosis of NSTEMI and independently predicts mortality during follow-up.

Digital DC-Reconstruction of AC-Coupled Electrophysiological Signals with a Single Inverting Filter

Since the introduction of digital electrocardiographs, high-pass filters have been necessary for successful analog-to-digital conversion with a reasonable amplitude resolution. On the other hand, such high-pass filters may distort the diagnostically significant ST-segment of the ECG, which can result in a misleading diagnosis. We present an inverting filter that successfully undoes the effects of a 0.05 Hz single pole high-pass filter. The inverting filter has been tested on more than 1600 clinical ECGs with one-minute durations and produces a negligible mean RMS-error of 3.1*10−8 LSB. Alternative, less strong inverting filters have also been tested, as have different applications of the filters with respect to rounding of the signals after filtering. A design scheme for the alternative inverting filters has been suggested, based on the maximum strength of the filter. With the use of the suggested filters, it is possible to recover the original DC-coupled ECGs from AC-coupled ECGs, at least when a 0.05 Hz first order digital single pole high-pass filter is used for the AC-coupling.

Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System.

This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules.

Validation of arrhythmia detection library on bedside monitor data for triggering alarms in intensive care

False Intensive Care Unit (ICU) alarms induce stress in both patients and clinical staff and decrease the quality of care, thus significantly increasing both the hospital recovery time and re-hospitalization rates. Therefore, PhysioNet/CinC Challenge 2015 encourages the development of algorithms for the analysis of bedside monitor data for robust detection of life-threatening arrhythmias. We participated in the Challenge with: (i) a closed source implementation of Arrhythmia Detection Library (ADLib, Schiller AG), including modules for lead quality monitoring, heartbeat detection, heartbeat classification and ventricular fibrillation detection; (ii) an open source Pulse Wave Analysis Module for verification of the hemodynamic status based on arterial blood pressure and photoplethysmogram signals; (iii) an open source Alarm Decision Module for final alarm rejection/validation. Our best scored entry in the real-time event is: score 79.41%, with 93%/83% true positive/negative rates. The average/max running time is 12.5/29.5% of quota.

Human Identification by Cross-Correlation and Pattern Matching of Personalized Heartbeat: Influence of ECG Leads and Reference Database Size

Human identification (ID) is a biometric task, comparing single input sample to many stored templates to identify an individual in a reference database. This paper aims to present the perspectives of personalized heartbeat pattern for reliable ECG-based identification. The investigations are using a database with 460 pairs of 12-lead resting electrocardiograms (ECG) with 10-s durations recorded at time-instants T1 and T2 > T1 + 1 year. Intra-subject long-term ECG stability and inter-subject variability of personalized PQRST (500 ms) and QRS (100 ms) patterns is quantified via cross-correlation, amplitude ratio and pattern matching between T1 and T2 using 7 features × 12-leads. Single and multi-lead ID models are trained on the first 230 ECG pairs. Their validation on 10, 20, ... 230 reference subjects (RS) from the remaining 230 ECG pairs shows: (i) two best single-lead ID models using lead II for a small population RS = (10–140) with identification accuracy AccID = (89.4–67.2)% and aVF for a large population RS = (140–230) with AccID = (67.2–63.9)%; (ii) better performance of the 6-lead limb vs. the 6-lead chest ID model—(91.4–76.1)% vs. (90.9–70)% for RS = (10–230); (iii) best performance of the 12-lead ID model—(98.4–87.4)% for RS = (10–230). The tolerable reference database size, keeping AccID > 80%, is RS = 30 in the single-lead ID scenario (II); RS = 50 (6 chest leads); RS = 100 (6 limb leads), RS > 230—maximal population in this study (12-lead ECG).

Quantification of the first-order high-pass filter's influence on the automatic measurements of the electrocardiogram

BACKGROUND AND OBJECTIVE The first-order high-pass filter (AC coupling) has previously been shown to affect the ECG for higher cut-off frequencies. We seek to find a systematic deviation in computer measurements of the electrocardiogram when the AC coupling with a 0.05 Hz first-order high-pass filter is used. METHODS The standard 12-lead electrocardiogram from 1248 patients and the automated measurements of their DC and AC coupled version were used. We expect a large unipolar QRS-complex to produce a deviation in the opposite direction in the ST-segment. RESULTS We found a strong correlation between the QRS integral and the offset throughout the ST-segment. The coefficient for J amplitude deviation was found to be -0.277 µV/(µV⋅s). CONCLUSIONS Potential dangerous alterations to the diagnostically important ST-segment were found. Medical professionals and software developers for electrocardiogram interpretation programs should be aware of such high-pass filter effects since they could be misinterpreted as pathophysiology or some pathophysiology could be masked by these effects.

The first-order high-pass filter influences the automatic measurements of the electrocardiogram

We have studied the effects of the 0.05 Hz first-order high-pass filter (AC coupling) on the automatic measurements of the electrocardiogram (ECG). The standard 12-lead ECG of 1248 patients and the automated measurements of the AC-and DC-coupled (unfiltered) versions were compared. We found a strong, linear correlation between the QRS integral and the difference in ST-segment amplitudes, suggesting that the AC coupling alters the ST segment. The effect on the remaining part of the ECG was minimal. Medical professionals and developers of software for ECG interpretation should be aware of such high-pass filter effects, since they could be misinterpreted as pathophysiology or some pathophysiology could be masked by these effects.

Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs

Background: Automated measurements of electrocardiographic (ECG) intervals by current‐generation digital electrocardiographs are critical to computer‐based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison. Methods: Seven widely used automated algorithms for computer‐based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2). Results: For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6 milliseconds for the PR interval, 0.1 to 8.1 milliseconds for QRS duration, and 0.1 to 9.3 milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3 milliseconds in the LQT1 subjects and from 0.2 to 11.0 milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5 milliseconds in the LQT1 subjects and from 0.9 to 12.8 milliseconds in the LQT2 subjects. Conclusions: Among current‐generation computer‐based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within‐individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted.

Inter-lead correlation analysis for automated detection of cable reversals in 12/16-lead ECG.

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.