Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome.

STUDY OBJECTIVE: Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis. METHODS: This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis. RESULTS: Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation. CONCLUSION: In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.

An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients.

BACKGROUND: False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies. PURPOSE: The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group. METHODS: The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO2 , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations. RESULTS: Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both. CONCLUSIONS: The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.

Ischemic Pre-Conditioning is Present in Patients with Non-ST Elevation Myocardial Infarction and ECG Derived Moderate Obstructive Sleep Apnea.

Background: Obstructive Sleep Apnea (OSA) is associated with an increased risk of cardiovascular events, including Acute Coronary Syndrome (ACS). There is conflicting evidence that suggests OSA has a cardioprotective effect (i.e., lower troponin), via ischemic pre-conditioning, in patients with ACS. Purpose: This study had two aims: (1) compare peak troponin between non-ST elevation (NSTE) ACS patients with and without moderate OSA identified using a Holter derived respiratory disturbance index (HDRDI); and (2) determine the frequency of transient myocardial ischemia (TMI) between NSTE-ACS patients with and without moderate HDRDI. Method: This was a secondary analysis. OSA events were identified from 12-lead ECG Holter recordings using QRSs, R-R intervals, and the myogram. Moderate OSA was defined as an HDRDI ≥15 events per/hour. TMI was defined as ≥1 millimeter of ST-segment ↑ or ↓, in ≥ 1 ECG lead, ≥ 1 minute. Results: In 110 NSTE-ACS patients, 39% (n=43) had moderate HDRDI. Peak troponin was higher in patients with moderate HDRDI (6.8 ng/ml yes vs. 10.2 ng/ml no; p=0.037). There was a trend for fewer TMI events, but there were no differences (16% yes vs. 30% no; p=0.081). Conclusions: NSTE-ACS patients with moderate HDRDI have less cardiac injury than those without moderate HDRDI measured using a novel ECG derived method. These findings corroborate prior studies suggesting a possible cardioprotective effect of OSA in ACS patients via ischemic pre-condition. There was a trend for fewer TMI events in moderate HDRDI patients, but there was no statistical difference. Future research should explore the underlying physiologic mechanisms of this finding.

Agreement of Computerized QT and QTc Interval Measurements Between Both Bedside and Expert Nurses Using Electronic Calipers.

BACKGROUND: In hospitalized patients, QT/QTc (heart rate corrected) prolongation on the electrocardiogram (ECG) increases the risk of torsade de pointes. Manual measurements are time-consuming and often inaccurate. Some bedside monitors automatically and continuously measure QT/QTc; however, the agreement between computerized versus nurse-measured values has not been evaluated. OBJECTIVE: The aim of this study was to examine the agreement between computerized QT/QTc and bedside and expert nurses who used electronic calipers. METHODS: This was a prospective observational study in 3 intensive care units. Up to 2 QT/QTc measurements (milliseconds) per patient were collected. Bland-Altman test was used to analyze measurement agreement. RESULTS: A total of 54 QT/QTc measurements from 34 patients admitted to the ICU were included. The mean difference (bias) for QT comparisons was as follows: computerized versus expert nurses, -11.04 ± 4.45 milliseconds (95% confidence interval [CI], -2.3 to -19.8; P = .016), and computerized versus bedside nurses, -13.72 ± 6.70 (95% CI, -0.70 to -26.8; P = .044). The mean bias for QTc comparisons was as follows: computerized versus expert nurses, -12.46 ± 5.80 (95% CI, -1.1 to -23.8; P = .035), and computerized versus bedside nurses, -18.49 ± 7.90 (95% CI, -3.0 to -33.9; P = .022). CONCLUSION: Computerized QT/QTc measurements calculated by bedside monitor software and measurements performed by nurses were in close agreement; statistically significant differences were found, but differences were less than 20 milliseconds (on-half of a small box), indicating no clinical significance. Computerized measurements may be a suitable alternative to nurse-measured QT/QTc. This could reduce inaccuracies and nurse burden while increasing adherence to practice recommendations. Further research comparing computerized QT/QTc from bedside monitoring to standard 12-lead electrocardiogram in a larger sample, including non-ICU patients, is needed.

Evaluation of premature ventricular complexes during in-hospital ECG monitoring as a predictor of ventricular tachycardia in an intensive care unit cohort.

In-hospital electrocardiographic (ECG) monitors are typically configured to alarm for premature ventricular complexes (PVCs) due to the potential association of PVCs with ventricular tachycardia (VT). However, no contemporary hospital-based studies have examined the association of PVCs with VT. Hence, the benefit of PVC monitoring in hospitalized patients is largely unknown. This secondary analysis used a large PVC alarm data set to determine whether PVCs identified during continuous ECG monitoring were associated with VT, in-hospital cardiac arrest (IHCA), and/or death in a cohort of adult intensive care unit patients. Six PVC types were examined (i.e., isolated, bigeminy, trigeminy, couplets, R-on-T, and run PVCs) and were compared between patients with and without VT, IHCA, and/or death. Of 445 patients, 48 (10.8%) had VT; 11 (2.5%) had IHCA; and 49 (11%) died. Isolated and run PVC counts were higher in the VT group (p = 0.03 both), but group differences were not seen for the other four PVC types. The regression models showed no significant associations between any of the six PVC types and VT or death, although confidence intervals were wide. Due to the small number of cases, we were unable to test for associations between PVCs and IHCA. Our findings suggest that we should question the clinical relevance of activating PVC alarms as a forewarning of VT, and more work should be done with larger sample sizes. A more precise characterization of clinically relevant PVCs that might be associated with VT is warranted.

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications.

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.

Premature ventricular complexes during continuous electrocardiographic monitoring in the intensive care unit: Occurrence rates and associated patient characteristics.

AIMS AND OBJECTIVES: This study examined the occurrence rate of specific types of premature ventricular complex (PVC) alarms and whether patient demographic and/or clinical characteristics were associated with PVC occurrences. BACKGROUND: Because PVCs can signal myocardial irritability, in-hospital electrocardiographic (ECG) monitors are typically configured to alert nurses when they occur. However, PVC alarms are common and can contribute to alarm fatigue. A better understanding of occurrences of PVCs could help guide alarm management strategies. DESIGN: A secondary quantitative analysis from an alarm study. METHODS: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was followed. Seven PVC alarm types (vendor-specific) were described, and included isolated, couplet, bigeminy, trigeminy, run PVC (i.e. VT >2), R-on-T and PVCs/min. Negative binomial and hurdle regression analyses were computed to examine the association of patient demographic and clinical characteristics with each PVC type. RESULTS: A total of 797,072 PVC alarms (45,271 monitoring hours) occurred in 446 patients, including six who had disproportionately high PVC alarm counts (40% of the total alarms). Isolated PVCs were the most frequent type (81.13%) while R-on-T were the least common (0.29%). Significant predictors associated with higher alarms rates: older age (isolated PVCs, bigeminy and couplets); male sex and presence of PVCs on the 12-lead ECG (isolated PVCs). Hyperkalaemia at ICU admission was associated with a lower R-on-T type PVCs. CONCLUSIONS: Only a few distinct demographic and clinical characteristics were associated with the occurrence rate of PVC alarms. Further research is warranted to examine whether PVCs were associated with adverse outcomes, which could guide alarm management strategies to reduce unnecessary PVC alarms. RELEVANCE TO CLINICAL PRACTICE: Targeted alarm strategies, such as turning off certain PVC-type alarms and evaluating alarm trends in the first 24 h of admission in select patients, might add to the current practice of alarm management.

Agreement between respiratory rate measurement using a combined electrocardiographic derived method versus impedance from pneumography.

BACKGROUND: Impedance pneumography (IP) is the current device-driven method used to measure respiratory rate (RR) in hospitalized patients. However, RR alarms are common and contribute to alarm fatigue. While RR derived from electrocardiographic (ECG) waveforms hold promise, they have not been compared to the IP method. PURPOSE: Study examined the agreement between the IP and combined-ECG derived (EDR) for normal RR (≥12 or ≤20 breaths/minute [bpm]); low RR (≤5 bpm); and high RR (≥30 bpm). METHODOLOGY: One-hundred intensive care unit patients were included by RR group: (1) normal RR (n = 50; 25 low RR and 25 high RR); (2) low RR (n = 50); and (3) high RR (n = 50). Bland-Altman analysis was used to evaluate agreement. RESULTS: For normal RR, a significant bias difference of -1.00 + 2.11 (95% CI -1.60 to -0.40) and 95% limit of agreement (LOA) of -5.13 to 3.13 was found. For low RR, a significant bias difference of -16.54 + 6.02 (95% CI: -18.25 to -14.83) and a 95% LOA of -28.33 to - 4.75 was found. For high RR, a significant bias difference of 17.94 + 12.01 (95% CI: 14.53 to 21.35) and 95% LOA of -5.60 to 41.48 was found. CONCLUSION: Combined-EDR method had good agreement with the IP method for normal RR. However, for the low RR, combined-EDR was consistently higher than the IP method and almost always lower for the high RR, which could reduce the number of RR alarms. However, replication in a larger sample including confirmation with visual assessment is warranted.

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

AIMS: Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for the identification of drugs at the high risk of TdP relies on measuring the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG). However, QTc has a low positive predictive value. METHODS AND RESULTS: We used convolutional neural network (CNN) models to quantify ECG alterations induced by sotalol, an IKr blocker associated with TdP, aiming to provide new tools (CNN models) to enhance the prediction of drug-induced TdP (diTdP) and diagnosis of cLQTS. Tested CNN models used single or multiple 10-s recordings/patient using 8 leads or single leads in various cohorts: 1029 healthy subjects before and after sotalol intake (n = 14 135 ECGs); 487 cLQTS patients (n = 1083 ECGs: 560 type 1, 456 type 2, 67 type 3); and 48 patients with diTdP (n = 1105 ECGs, with 147 obtained within 48 h of a diTdP episode). CNN models outperformed models using QTc to identify exposure to sotalol [area under the receiver operating characteristic curve (ROC-AUC) = 0.98 vs. 0.72, P ≤ 0.001]. CNN models had higher ROC-AUC using multiple vs. single 10-s ECG (P ≤ 0.001). Performances were comparable for 8-lead vs. single-lead models. CNN models predicting sotalol exposure also accurately detected the presence and type of cLQTS vs. healthy controls, particularly for cLQT2 (AUC-ROC = 0.9) and were greatest shortly after a diTdP event and declining over time (P ≤ 0.001), after controlling for QTc and intake of culprit drugs. ECG segment analysis identified the J-Tpeak interval as the best discriminator of sotalol intake. CONCLUSION: CNN models applied to ECGs outperform QTc measurements to identify exposure to drugs altering the QT interval, congenital LQTS, and are greatest shortly after a diTdP episode.

Computerized automated algorithm-based analyses of digitized paper ECGs in Brugada syndrome.

BACKGROUND: Brugada syndrome is a rare inherited arrhythmic syndrome with a coved type 1 ST-segment elevation on ECG and an increased risk of sudden death. Many studies have evaluated risk stratification performance based on ECG-derived parameters. However, since historical Brugada patient cohorts included mostly paper ECGs, most studies have been based on manual ECG parameter measurements. We hypothesized that it would be possible to run automated algorithm-based analysis of paper ECGs. We aimed: 1) to validate the digitization process for paper ECGs in Brugada patients; and 2) to quantify the acute class I antiarrhythmic drug effect on relevant ECG parameters in Brugada syndrome. METHODS: A total of 176 patients (30% female, 43 ± 13 years old) with induced type 1 Brugada syndrome ECG were included in the study. All of the patients had paper ECGs before and during class I antiarrhythmic drug challenge. Twenty patients also had a digital ECG, in whom printouts were used to validate the digitization process. Paper ECGs were scanned and then digitized using ECGScan software, version 3.4.0 (AMPS, LLC, New York, NY, USA) to obtain FDA HL7 XML format ECGs. Measurements were automatically performed using the Bravo (AMPS, LLC, New York, NY, USA) and Glasgow algorithms. RESULTS: ECG parameters obtained from digital and digitized ECGs were closely correlated (r = 0.96 ± 0.07, R2 = 0.93 ± 0.12). Class I antiarrhythmic drugs significantly increased the global QRS duration (from 113 ± 20 to 138 ± 23, p < 0.0001). On lead V2, class I antiarrhythmic drugs increased ST-segment elevation (from 110 ± 84 to 338 ± 227 µV, p < 0.0001), decreased the ST slope (from 14.9 ± 23.3 to -27.4 ± 28.5, p < 0.0001) and increased the TpTe interval (from 88 ± 18 to 104 ± 33, p < 0.0001). CONCLUSIONS: Automated algorithm-based measurements of depolarization and repolarization parameters from digitized paper ECGs are reliable and could quantify the acute effects of class 1 antiarrhythmic drug challenge in Brugada patients. Our results support using computerized automated algorithm-based analyses from digitized paper ECGs to establish risk stratification decision trees in Brugada syndrome.

Correction of QRS voltage for body mass index does not improve the prediction of fatal and nonfatal cardiovascular events. The Moli-sani study.

BACKGROUND AND AIMS: The diagnosis of LVH by ECG may particularly difficult in obese individuals. The aim of this study was to prospectively investigate whether the correction for body mass index (BMI) might improve the prognostic significance for cerebro and cardiovascular events of two electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) in a large cohort of Italian adults. METHODS AND RESULTS: In 18,330 adults (54 ± 11 years, 55% women) from the Moli-sani cohort, obesity was defined using the ATPIII criteria. The Sokolow-Lyon (SL) and Cornell Voltage (CV) criteria were used for ECG-LVH. In overweight and obese subjects, as compared with normal weight, the prevalence of ECG-LVH by the SL index was lower. During follow-up (median 4.3 yrs), 503 cerebro and cardiovascular events occurred. One standard deviation (1-SD) increment in uncorrected and in BMI-corrected SL index and CV was associated with an increased risk of events (HR 1.12, 95% CI 1.02-1.22 and HR 1.16, 95% CI 1.06-1.26 and HR 1.12, 95% CI 1.03-1.23 and HR 1.17, 95% CI 1.07-1.27, respectively for SL and CV). In obese subjects, 1-SD increment in uncorrected CV and in BMI-corrected CV was not associated to a significant risk of events (HR 1.05, 95% CI 0.910-1.22 and HR 1.08, 95% CI 0.95-1.23 respectively). Uncorrected SL index showed a significant association with events, which was marginally stronger with BMI-corrected SL voltage (HR 1.18, 95% CI 1.02-1.37 and HR 1.17, 95% CI 1.04-1.33 respectively, Akaike information criterion change from 3220 to 3218). CONCLUSIONS: BMI correction of ECG LVH voltage criteria does not significantly improve the prediction of cerebro and cardiovascular events in obese patients in a large cohort at low cardiovascular risk.

Sex influences on ventricular repolarization duration in normal subjects and in type 1, 2 and 3 long QT syndrome patients: Different effect in acquired and congenital type 2 LQTS.

AIM: To evaluate the interaction between sex and rate corrected QT interval (QTc) duration in normal subjects after drug-induced QT prolongation and in LQTS patients. METHODS: Semi-automated measurements were performed on 875 digital ECGs (200 normal subjects off drugs (100 females), 200 normal subjects on Moxifloxacin (100 females), 259 LQT1 patients (161 females), 183 LQT2 patients (100 females) and 33 LQT3 patients (15 females)). A sex specific coefficient was calculated in each group and was used to calculate group specific corrected QT intervals (QTci). RESULTS: The mean sex difference (female minus male) in QTci interval duration was 17 ms 95%CI(12.7; 21.3) in normal subjects, 19 ms (14.5; 23.5) on Moxifloxacin, and 13 ms (4.8; 21.2) in LQT1 patients. The mean difference was 2 ms (-7.9; 11.9) in LQT2 and - 5 ms (-32.2; 22.2) in LQT3 patients (p = 0.0067 for the group and sex interaction). In the subgroup of patients above 15 years and without beta blocker treatment, the sex effect (female minus male) on QTci interval duration was 17 ms (4.1; 29.9) in LQT1 patients. QTc duration was not different between sex in LQT2 and in LQT3 patients (mean difference - 3 ms (-21.6; 15.6) and 12 ms (-28.4; 52.4), respectively) (p = 0.0191 for group and sex interaction). CONCLUSIONS: The interaction between sex and QTc interval is preserved in type 1 LQTS and drug-induced QTc prolongation but blurred in type 2 LQTS. Further experimental studies are warranted to better understand the interaction of sexual hormones with malfunctioning KCNH2 encoded repolarizing potassium channel.

Correction of the QRS duration for heart rate.

OBJECTIVE: To determine the clinical value of correcting the QRS duration for heart rate. BACKGROUND: We recently observed [1] that the QRS duration shortens during spontaneous increases in heart rate. In the current study, we analyzed ECG and pharmacokinetic data of 21 subjects who received quinidine in a recent study [2]. They experienced the expected post-quinidine increase in heart rate, allowing us to determine if quinidine's well-known QRS prolongation might be attenuated due to the concomitant rate increase. METHODS: In a crossover-designed study, after baseline ECG recording, the subjects received quinidine 400 mg orally or placebo, and ECGs and quinidine plasma concentrations were then obtained at 15 prespecified timepoints over 24 h. The previously determined QRS-RR regression slope (0.0125) [1] was used to rate-correct QRS. Change in QRS from baseline (dQRS) and rate-corrected change in QRS from baseline (dQRSc) over time were plotted with the mean quinidine concentration and the correlation of plasma concentration with dQRS and dQRSc was assessed by pairwise correlation and linear regression. RESULTS: There was a statistically significantly greater increase in heart rate at all timepoints combined in the quinidine arm compared with the placebo arm (9.9 ±â€¯6.80 vs. 5.2 ±â€¯7.42, respectively, p < 0.0001). dQRSc was significantly greater at all timepoints combined compared with dQRS (1.99 ±â€¯4.824 vs -0.68 ±â€¯4.640 msec, respectively, p < 0.0001). dQRS correlated poorly with quinidine plasma concentration (p = 0.127), with no clear change in QRS observed. On the other hand, dQRSc correlated well with quinidine concentration (p = 0.010), with a clear rise and fall in dQRSc that mirrored the rise and fall of quinidine concentration. CONCLUSION: Rate correction of the QRS duration improves detection of QRS prolongation in the presence of heart rate change. CONDENSED ABSTRACT: Using the mean QRS - RR slope determined previously in normal volunteers [1], we corrected the QRS duration for its known dependency on heart rate in 21 subjects who received quinidine and experienced the expected post-quinidine increase in heart rate in a recent clinical trial [2]. We found that uncorrected QRS did not correlate with quinidine concentration (p = 0.127), while rate-corrected QRS correlated well (p = 0.010) and mirrored the rise and fall of quinidine concentration. Rate correction of the QRS duration improves detection of QRS prolongation in the presence of heart rate change.

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.

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation.

BACKGROUND: Accurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes. METHODS: Inter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes. RESULTS: In 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features. CONCLUSIONS: An increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy.

Archiving and exchange of digital ECGs: A review of existing data formats.

Digital ECG is today a common practice but a universal format for its storage and exchange has never been widely implemented. The reason is linked on one side to the need of the manufacturing industry to (rightly) protect intellectual propriety and technology, but on the other to an inadequate effort of the research community to sufficiently enforce the use of digital ECG data. To some degree, and at least from a practical point of view, the problem is also linked to other factors, such as the need in some instances to protect patient-sensitive information, and whether digital exchanged data should also include annotations and measurements from an algorithm or by human intervention. As a result, after more than 30 years it is still common that the full ECG acquired information is not preserved, but only partially stored or saved as a PDF report. Paradoxically, the modern era of hospital information technology and the advent and large diffusion of electronic health record systems did not bring expected improvements: the process of digital ECG retrieval and management remains extremely complicated and cumbersome. The ultimate risk is that the ECG may end up being considered "just" an image rather than a voltage-versus-time signal as it has always been. A critical review of the most commonly used formats for digital ECG will be given, focusing in particular to those linked with DICOM, HL7 and SCP-ECG standards, and highlighting the respective advantages and limitations with special emphasis to the needs typically encountered by the research community. The goal is to provide a snapshot of the present, and to discuss mid- and long-term potential directions and changes, emphasizing what digital ECG organizations could do to "save" ECG information and facilitate its widespread exchange.

Automated JTpeak analysis by BRAVO.

Using BRAVO algorithm (AMPS-LLC, NY, v4.4.0), 5223 ECGs from a publicly available annotated dataset from a randomized clinical trial on four different compounds and placebo were analyzed. ECGs were automatically processed and JTp interval was computed on: 12 standard ECG leads, Vector Magnitude (VM), and root mean square (RMS) leads. On VM and RMS, JTp intervals were nearly identical (228 ± 29 vs. 227 ± 30 ms respectively, with correlation of 0.99, p < 0.0001). On lead II, JTp interval was about 10 ms longer, but highly correlated with that measured on VM (0.94, p < 0.0001). Similarly, on lead V5, JTp was about 8 ms longer than on VM, with a correlation of 0.95, p < 0.0001. When compared to the public available annotations, JTp by BRAVO generated longer (about 8 ms) measurement and evidenced outliers conducible to both the T-wave peak (in few ECGs presenting notched shapes) and, to a lesser degree, to the J point, due to variability of the two algorithms. Differences on the drug-induced effect from the four compounds were negligible.

A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram.

BACKGROUND: The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named 'Interactive Progressive based Interpretation' (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. OBJECTIVES: To improve interpretation accuracy and reduce missed co-abnormalities. METHODS: The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach. RESULTS: A total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. CONCLUSION: Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size.

PDF-ECG in clinical practice: A model for long-term preservation of digital 12-lead ECG data.

BACKGROUND: In clinical practice, data archiving of resting 12-lead electrocardiograms (ECGs) is mainly achieved by storing a PDF report in the hospital electronic health record (EHR). When available, digital ECG source data (raw samples) are only retained within the ECG management system. OBJECTIVE: The widespread availability of the ECG source data would undoubtedly permit successive analysis and facilitate longitudinal studies, with both scientific and diagnostic benefits. METHODS & RESULTS: PDF-ECG is a hybrid archival format which allows to store in the same file both the standard graphical report of an ECG together with its source ECG data (waveforms). Using PDF-ECG as a model to address the challenge of ECG data portability, long-term archiving and documentation, a real-world proof-of-concept test was conducted in a northern Italy hospital. A set of volunteers undertook a basic ECG using routine hospital equipment and the source data captured. Using dedicated web services, PDF-ECG documents were then generated and seamlessly uploaded in the hospital EHR, replacing the standard PDF reports automatically generated at the time of acquisition. Finally, the PDF-ECG files could be successfully retrieved and re-analyzed. CONCLUSION: Adding PDF-ECG to an existing EHR had a minimal impact on the hospital's workflow, while preserving the ECG digital data.

An evaluation of multiple algorithms for the measurement of the heart rate corrected JTpeak interval.

Interest in the effects of drugs on the heart rate-corrected JTpeak (JTpc) interval from the body-surface ECG has spawned an increasing number of scientific investigations in the field of regulatory sciences, and more specifically in the context of the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. We conducted a novel initiative to evaluate the role of automatic JTpc measurement technologies by comparing their ability to distinguish multi- from single-channel blocking drugs. A set of 5232 ECGs was shared by the FDA (through the Telemetric and Holter ECG Warehouse) with 3 ECG device companies (AMPS, Mortara, and Philips). We evaluated the differences in drug-concentration effects on these measurements between the commercial and the FDA technologies. We provide a description of the drug-induced placebo-corrected changes from baseline for dofetilide, quinidine, ranolazine, and verapamil, and discuss the various differences across all technologies. The results revealed only small differences between measurement technologies evaluated in this study. It also confirms that, in this dataset, the JTpc interval distinguishes between multi- and single-channel (hERG) blocking drugs when evaluating the effects of dofetilide, quinidine, ranolazine, and verapamil. However, in the case of quinidine and dofetilide, we noticed a poor consistency across technologies because of the lack of standard definitions for the location of the peak of the T-wave (T-apex) when the T-wave morphology is abnormal.