Incremental value of high-frequency QRS analysis for diagnosis and prognosis in suspected exercise-induced myocardial ischaemia.

AIM: Exercise stress testing is used to detect myocardial ischaemia, but is limited by low sensitivity and specificity. The authors investigated the value of the analysis of high-frequency QRS components as a marker of abnormal depolarization in addition to standard ST-deviations as a marker of abnormal repolarization to improve the diagnostic accuracy. METHODS AND RESULTS: Consecutive patients undergoing bicycle exercise stress nuclear myocardial perfusion imaging were prospectively enrolled. Presence of myocardial ischaemia, the primary diagnostic endpoint, was adjudicated using MPI and coronary angiography. Automated high-frequency QRS analysis was performed in a blinded fashion. The prognostic endpoint was major adverse cardiac events (MACEs) during two years of follow-up. Exercise-induced ischaemia was detected in 147/662 patients (22%). The sensitivity of high-frequency QRS was similar to ST-deviations (46% vs. 43%, p=0.59), while the specificity was lower (75% vs. 87%, p<0.001). The combined use of high-frequency QRS and ST-deviations classified 59% of patients as 'rule-out' (both negative), 9% as 'rule-in' (both positive) and 32% in an intermediate zone (one test positive). The sensitivity for 'rule-out' and the specificity for 'rule-in' improved to 63% and 97% compared with ST-deviation analysis alone (both p<0.001). MACE-free survival was 90%, 80% and 42% in patients in the 'rule-out', intermediate and 'rule-in' groups (p<0.001). After adjustment for age, gender, ST-deviations and clinical post-test probability of ischaemia, high-frequency QRS remained an independent predictor for the occurrence of MACEs. CONCLUSION: The use of high-frequency QRS analysis in addition to ST-deviation analysis improves the diagnostic accuracy during exercise stress testing and adds independent prognostic information.

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 3.6milliseconds for the PR interval, 0.1 to 8.1milliseconds for QRS duration, and 0.1 to 9.3milliseconds 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 13.3milliseconds in the LQT1 subjects and from 0.2 to 11.0milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5milliseconds in the LQT1 subjects and from 0.9 to 12.8milliseconds 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.

Diagnostic value of ST-segment deviations during cardiac exercise stress testing: Systematic comparison of different ECG leads and time-points.

BACKGROUND: Exercise ECG stress testing is the most widely available method for evaluation of patients with suspected myocardial ischemia. Its major limitation is the relatively poor accuracy of ST-segment changes regarding ischemia detection. Little is known about the optimal method to assess ST-deviations. METHODS: A total of 1558 consecutive patients undergoing bicycle exercise stress myocardial perfusion imaging (MPI) were enrolled. Presence of inducible myocardial ischemia was adjudicated using MPI results. The diagnostic value of ST-deviations for detection of exercise-induced myocardial ischemia was systematically analyzed 1) for each individual lead, 2) at three different intervals after the J-point (J+40ms, J+60ms, J+80ms), and 3) at different time points during the test (baseline, maximal workload, 2min into recovery). RESULTS: Exercise-induced ischemia was detected in 481 (31%) patients. The diagnostic accuracy of ST-deviations was highest at +80ms after the J-point, and at 2min into recovery. At this point, ST-amplitude showed an AUC of 0.63 (95% CI 0.59-0.66) for the best-performing lead I. The combination of ST-amplitude and ST-slope in lead I did not increase the AUC. Lead I reached a sensitivity of 37% and a specificity of 83%, with similar sensitivity to manual ECG analysis (34%, p=0.31) but lower specificity (90%, p<0.001). CONCLUSION: When using ECG stress testing for evaluation of patients with suspected myocardial ischemia, the diagnostic accuracy of ST-deviations is highest when evaluated at +80ms after the J-point, and at 2min into recovery.

Diagnostic and Prognostic Value of Lead aVR During Exercise Testing in Patients Suspected of Having Myocardial Ischemia.

We aimed to assess the diagnostic and prognostic value of ST-segment deviation in aVR, a lead often ignored in clinical practice, during exercise testing and to compare it to the most widely used criterion of ST-segment depression in V5. We enrolled 1,596 patients with suspected myocardial ischemia referred for nuclear perfusion imaging undergoing bicycle stress testing. ST-segment amplitudes in leads aVR and V5 were automatically measured. The presence of inducible myocardial ischemia was the diagnostic end point and adjudicated based on nuclear perfusion imaging and coronary angiography. Major adverse cardiac events (MACE) during 2 years of follow-up including death, acute myocardial infarction, and coronary revascularization were the prognostic end point. Exercise-induced myocardial ischemia was detected in 470 patients (29%). Median ST amplitudes for leads aVR and V5 differed significantly among patients with and without ischemia (p <0.01). The diagnostic accuracy of ST changes for myocardial ischemia as quantified by the area under the receiver operating characteristic curve was highest 2 minutes into recovery and similar in aVR and V5 (0.62, 95% confidence interval CI 0.60 to 0.65 vs 0.60, 95% confidence interval 0.58 to 0.63, p = 0.08 for comparison). In multivariate analysis, ST changes in lead aVR, but not lead V5, contributed independent diagnostic information on top of clinical parameters and manual electrocardiographic interpretation. Within 2 years of follow-up, MACE occurred in 33% of patients with ST elevations in aVR and in 16% without (p <0.001). In conclusion, ST elevation in lead aVR during exercise testing indicates inducible myocardial ischemia independently of ST depressions in lead V5 and clinical factors and also predicts MACE during follow-up.

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.

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.

A Correction Formula for the ST-Segment Measurements of AC-Coupled Electrocardiograms.

GOAL: The ST segment of an electrocardiogram (ECG) is very important for the correct diagnosis of an acute myocardial infarction. Most clinical ECGs are recorded using an ACcoupled ECG amplifier. It is well known, that first-order high-pass filters used for the AC coupling can affect the ST segment of an ECG. This effect is stronger the higher the filter's cut-off frequency is and the larger the QRS integral is. We present a formula that estimates these changes in the ST segment and therefore allows for correcting ST measurements that are based on an ACcoupled ECG. METHODS: The presented correction formula can be applied when only four parameters are known: the possibly estimated QRS area A, the QRS duration W, the beat-to-beat interval TRR, and the filter time constant T, further, the time point Tj to correct-after the J point-must be specified. RESULTS: The formula is correct within 0.6% until 40% ms after the J point and within 6% until 80 ms after the J point. CONCLUSION AND SIGNIFICANCE: It is not necessary to have the raw data available and the formula therefore opens up the possibility of reevaluating studies that are based on ACcoupled ECGs and compare the results of such studies with studies that are based on newer, DC-coupled ECGs.

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).

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.

Real-time arrhythmia detection with supplementary ECG quality and pulse wave monitoring for the reduction of false alarms in ICUs.

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 rehospitalization rates. In the PhysioNet/CinC Challenge 2015 for reducing false arrhythmia alarms in ICU bedside monitor data, this paper validates the application of a real-time arrhythmia detection library (ADLib, Schiller AG) for the robust detection of five types of life-threatening arrhythmia alarms. The strength of the application is to give immediate feedback on the arrhythmia event within a scan interval of 3 s-7.5 s, and to increase the noise immunity of electrocardiogram (ECG) arrhythmia analysis by fusing its decision with supplementary ECG quality interpretation and real-time pulse wave monitoring (quality and hemodynamics) using arterial blood pressure or photoplethysmographic signals. We achieved the third-ranked real-time score (79.41) in the challenge (Event 1), however, the rank was not officially recognized due to the 'closed-source' entry. This study shows the optimization of the alarm decision module, using tunable parameters such as the scan interval, lead quality threshold, and pulse wave features, with a follow-up improvement of the real-time score (80.07). The performance (true positive rate, true negative rate) is reported in the blinded challenge test set for different arrhythmias: asystole (83%, 96%), extreme bradycardia (100%, 90%), extreme tachycardia (98%, 80%), ventricular tachycardia (84%, 82%), and ventricular fibrillation (78%, 84%). Another part of this study considers the validation of ADLib with four reference ECG databases (AHA, EDB, SVDB, MIT-BIH) according to the international recommendations for performance reports in ECG monitors (ANSI/AAMI EC57). The sensitivity (Se) and positive predictivity (+P) are: QRS detector QRS (Se, +P) > 99.7%, ventricular ectopic beat (VEB) classifier VEB (Se, +P) = 95%, and ventricular fibrillation detector VFIB (P + = 94.8%) > VFIB (Se = 86.4%), adjusted to the clinical setting requirements, giving preference to low false positive alarms.

Central role for GSK3ß in the pathogenesis of arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is characterized by redistribution of junctional proteins, arrhythmias, and progressive myocardial injury. We previously reported that SB216763 (SB2), annotated as a GSK3ß inhibitor, reverses disease phenotypes in a zebrafish model of ACM. Here, we show that SB2 prevents myocyte injury and cardiac dysfunction in vivo in two murine models of ACM at baseline and in response to exercise. SB2-treated mice with desmosome mutations showed improvements in ventricular ectopy and myocardial fibrosis/inflammation as compared with vehicle-treated (Veh-treated) mice. GSK3ß inhibition improved left ventricle function and survival in sedentary and exercised Dsg2mut/mut mice compared with Veh-treated Dsg2mut/mut mice and normalized intercalated disc (ID) protein distribution in both mutant mice. GSK3ß showed diffuse cytoplasmic localization in control myocytes but ID redistribution in ACM mice. Identical GSK3ß redistribution is present in ACM patient myocardium but not in normal hearts or other cardiomyopathies. SB2 reduced total GSK3ß protein levels but not phosphorylated Ser 9-GSK3ß in ACM mice. Constitutively active GSK3ß worsens ACM in mutant mice, while GSK3ß shRNA silencing in ACM cardiomyocytes prevents abnormal ID protein distribution. These results highlight a central role for GSKß in the complex phenotype of ACM and provide further evidence that pharmacologic GSKß inhibition improves cardiomyopathies due to desmosome mutations.

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.

Development of a toolbox for electrocardiogram-based interpretation of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) develops as a consequence of an underlying heart disease such as fibrosis, inflammation, hyperthyroidism, elevated intra-atrial pressures, and/or atrial dilatation. The arrhythmia is initiated by, or depends on, ectopic focal activity. Autonomic dysfunction may also play a role. However, in most patients, the actual cause of AF is difficult to establish, which hampers the selection of the optimal mode of treatment. This study aims to develop tools for assisting the physician's decision-making process. METHODS: Signal analytical methods have been developed for optimizing the assessment of the complexity of AF in all of the standard 12-lead signals. The development involved an evaluation of methods for reducing the signal components stemming from the electric activity of the ventricles (QRST suppression). The methods were tested on simulated recordings, on clinical recordings on patients in AF, and on patients exhibiting atrial flutter (AFL) and atrial tachycardia. The results have been published previously. Subsequently, the implementation of the algorithms in a commercially available electrocardiogram (ECG) recorder, an implementation referred to as its AF-Toolbox, has been carried out. The performance of this implementation was tested against those observed during the development stage. In addition, an improved visualization of the specific ECG components was implemented. This was enabled by providing a separate view on ventricular and atrial activity, which resulted from the steps implied in the QRST suppression. Furthermore, a search was initiated for identifying meaningful features in the cleaned up atrial signals. RESULTS: When testing the implementation of the previously developed methods in the Toolbox on simulated and clinical data, the suppression of ventricular activity in the ECG produced residuals down to the level of physiologic background noise, in agreement with those reported on previously. The QRST suppression resulted in a better visualization of the atrial signals in AF, atrial AFL, sinus rhythm in the presence of atrioventricular blocks, or ectopic beats. Classifiers for AF and AFL that have been defined so far include the distinct spectral components (multiple basic frequencies), exhibiting distinct dominance in specific leads. The annotations of ventricular and atrial activities, ventricular and atrial trigger, as well as ratio between atrial and ventricular rates were greatly facilitated. The time diagram of ventricular and atrial triggers provides an additional view on rhythm disturbances. CONCLUSIONS: The AF-Toolbox that is currently developed for clinical applications has the potential of reliably detecting and classifying AF, as well as to correctly describe atrioventricular conduction, propagation blocks and/or ectopic beats. Based on the results obtained, a first industrial prototype has been built, which will be used to assess its performance in a routine clinical environment. The availability of this tool will facilitate the search for meaningful signal features for identifying the source of AF in individual patients.

Improving automatic analysis of the electrocardiogram acquired during magnetic resonance imaging using magnetic field gradient artefact suppression.

The electrocardiogram (ECG) used for patient monitoring during magnetic resonance imaging (MRI) unfortunately suffers from severe artefacts. These artefacts are due to the special environment of the MRI. Modeling helped in finding solutions for the suppression of these artefacts superimposed on the ECG signal. After we validated the linear and time invariant model for the magnetic field gradient artefact generation, we applied offline and online filters for their suppression. Wiener filtering (offline) helped in generating reference annotations of the ECG beats. In online filtering, the least-mean-square filter suppressed the magnetic field gradient artefacts before the acquired ECG signal was input to the arrhythmia algorithm. Comparing the results of two runs (one run using online filtering and one run without) to our reference annotations, we found an eminent improvement in the arrhythmia module's performance, enabling reliable patient monitoring and MRI synchronization based on the ECG signal.